How Blockchain Could Help End Poverty in All Its Forms

How Blockchain Could Help End Poverty in All Its Forms

Technological advancements have reduced global poverty significantly in the past 100 years. Although many people have been able to leave poverty due to this, however, still more than 1.3 billion people live in extreme poverty. Extreme poverty is defined as having less than $1.25 to spend every day. There are a wide variety of causes for poverty, which differ per country, but in general causes for poverty include: lack of education, environmental problems, lack of access to banking facilities, lack of legal ownership of property, lack of rule of law, overpopulation, epidemic diseases or changing trends in a country’s economy.

Overcoming poverty is vital if we want to create a world that is peaceful and fair for everyone. Besides, in 2015 the United Nations adopted the Sustainable Development Goals, which includes challenging global leaders to help end poverty in all its forms, everywhere, by 2030. In this article, I will argue why and how Blockchain could help achieving this goal. Breaking the cycle of poverty begins with investing in children and providing them with quality education, knowledge and skills to enable them to realise their full potential. Next to education comes access to affordable proper health care, access to clean water ...


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Intelligent Automation for DevOps: An Interview with Rocana’s CTO & Co-Founder Eric Sammer

Intelligent Automation for DevOps: An Interview with Rocana’s CTO & Co-Founder Eric Sammer


Recently big data and analytics company Rocana, a provider specialized in the provision visibility for IT & DevOps announced the release of its data platform Rocana Ops.

Is in this context that we had the chance to have an excellent interview with Eric Sammer, CTO and Co-Founder of Rocana who kindly agreed to provide us with excellent insights in regards to the company, its software offering as well details from the new version.

Eric has served as a Senior Engineer and Architect at several large scale data-driven organizations, including Experian and Conductor. Most recently served as an Engineering Manager at Cloudera where he was responsible for working with hundreds of partners to develop robust solutions and integrate them tightly with Cloudera's Enterprise Data Hub.

He is deeply entrenched in the open source community and has an ambition for solving difficult scaling and processing problems. Passionate about challenging assumptions and showing large, complex enterprises new ways to solve large, complex IT infrastructure challenges Eric now lead Rocana’s product development and company direction as CTO.

Eric is also the author of Hadoop Operations published by O'Reilly Media and is also a frequent speaker on technology and techniques for large scale data processing, integration, and system management.

Hi Eric, so, what was the motivation behind founding Rocana, the company, and developing Rocana Ops the product?

Rocana was founded directly in response to the growing sophistication of the infrastructure and technology that runs the modern business, and the challenges companies have in understanding those systems. Whether its visibility into health and performance, investigating specific issues, or holistically understanding the impact infrastructure health and well-being have on the business, many businesses are struggling with the complexity of their environments.

These issues have been exacerbated by trends in cloud computing, hybrid environments, microservices, and data-driven products and features such as product recommendations, real time inventory visibility, and customer account self-management that rely on data from, and about, the infrastructure and the business. There are a greater number of more varied data sources, producing finer-grained, data faster than ever before.

Meanwhile, the existing solutions to understand and manage these environments are not keeping pace. All of them focus on interesting, but limited, slices of the problem - just log search, just dashboards of metrics, just the last 30 minutes of network flow data, only security events - making it almost impossible to understand what’s happening. These tools tend to think of each piece of infrastructure as a special case rather than the data warehousing and advanced analytics problem it is.

Outside of core IT, it’s natural to source feeds of data from many different places, cleanse and normalize that data, and bring it into a central governed repository where it can be analyzed, visualized, or used to augment other applications.

We want to extend that thinking into infrastructure, network, cloud, database, platform, and application management to better run the business, while at the same time, opening up new opportunities to bring operational and business data together. That means all of data, from every data source, in real time, with full retention, on an open platform, with advanced analytics to make sense of that data.

How would you describe what Rocana Ops is?

Rocana Ops is a data warehouse for event-oriented data. That includes log events, infrastructure and application metrics, business transactions, IoT events, security events, or anything else with a time stamp. It includes the collection, transformation and normalization, storage, query, analytics, visualization, and management of all event-oriented data in a single open system that scales horizontally on cost-effective hardware or cloud platforms.

A normal deployment of Rocana Ops for our customers will take in anywhere from 10 to 100TB of new data every day, retaining it for years. Each event captured by the system is typically available for query in less than one second, and always online and “query-able thanks to a fully parallelized storage and query platform.

Rocana is placed in a very interesting segment of the IT industry. What are in your view, the differences between the common business analytics user and the IT user regarding the use of a data management and analytics solution? Different needs? Different mindsets? Goals?

I think the first thing to consider when talking about business analytics - meaning both custom-built and off-the-shelf BI suites - and IT focused solutions is that there has historically been very little cross-pollination of ideas between them. Business users tend to think about customized views on top of shared repositories, and building data pipelines to feed those repositories.

There tends to be a focus on reusing data assets and pipelines, lineage concerns, governance, and lifecycle management. IT users on the other hand, think about collection through analytics for each data source as a silo: network performance, application logs, host and process-level performance, and so on each have dedicated collection, storage, and analytics glued together in a tightly coupled package.

Unlike their business counterparts, IT users have very well known data sources and formats (relatively speaking) and analytics they want to perform. So in some ways, IT analytics have a more constrained problem space, but less integration. This is Conway’s Law in serious effect: the notion that software tends to mimic the organizational structures in which it’s developed or designed. These silos lead to target fixation.

IT users can wind up focusing on making sure the operating system is healthy, for example, while the business service it supports is unhealthy. Many tools tend to reinforce that kind of thinking. That extends to diagnostics and troubleshooting which is even worse. Again, we’re talking in generic terms here, but the business users tend to have a holistic focus on an issue relevant to the business rather than limited slices.

We want to open that visibility to the IT side of the house, and hopefully even bring those worlds together.

What are the major pains of IT Ops and how Rocana helps them to solve this pains?

Ops is really a combination of both horizontal and vertically focused groups. Some teams are tasked with building and/or running a complete vertical service like an airline check-in and boarding pass management system. Other teams are focused on providing horizontal services such as data center infrastructure with limited knowledge or visibility into what those tens of thousands of boxes do.

Let’s say customers can’t check-in and get their boarding passes on their mobile devices. The application ops team finds that a subset of application servers keep losing connections to database servers holding reservations, but there’s no reason why, and nothing has changed. Meanwhile, the networking team may be swapping out some bad optics in a switch that has been flaky thinking that traffic is being properly routed over another link. Connecting these two dots within a large organization can be maddeningly time consuming - if it even happens at all - leading to some of the high profile outages we see in the news.

Our focus is really on providing a shared view over all systems under management. Each team still has their focused view on their part of the infrastructure in Rocana Ops, but in this example, the application ops team could also trace the failing connections through to link state changes on switches and correlate that with traffic changes in network flow patterns.

Could you describe Rocana’s main architecture?

Following the data flow through Rocana Ops, data is first collected by one of the included data collection methods. These include native syslog, file and directory tailing, netflow and IPFIX, Windows event log, application and host metrics collection, and native APIs for popular programming languages, as well as REST.

As data is collected, basic parsing is performed turning all data into semi-structured events that can be easily correlated regardless of their source. These events flow into an event data bus forming a real-time stream of the cleansed, normalized events. All of the customer-configurable and extensible transformation, model building and application (for features like anomaly detection), complex event processing, triggering, alerting, and other data services are real time stream-oriented services.

Rocana's General Architecture (Courtesy of Rocana)

A number of representations of the data are stored in highly optimized data systems for natural language search, query, analysis, and visualization in the Rocana Ops application. Under the hood, Rocana Ops is built on top of a number of popular open source systems, in open formats, that may be used for other applications and systems making lock-in a non-issue for customers.

Every part of Rocana’s architecture - but notably the collection, processing, storage, and query systems - is a parallelized, scale-out system, with no single point of failure.

What are the basic or general requirements needed for a typical Rocana deployment?

Rocana Ops is really designed for large deployments as mentioned earlier - 10s to 100s of terabytes per day.

Typically customers start with a half-rack (10) of 2 x 8+ core CPUs, 12 x 4 or 8TB SATA II drives, 128 to 256GB RAM, and a 10Gb network (typical models are the HP DL380 G9 or Dell R730xd) or the cloud-equivalent (Amazon d2.4xl or 8xl) for the data warehouse nodes.

A deployment this size easily handles in excess of a few terabytes per day of data coming into the system from tens to hundreds of thousands of sources.

As customers onboard more data sources or want to retain more data, they begin adding nodes to the system. We have a stellar customer success team that helps customers plan, deploy, and service Rocana Ops, so customers don’t need to worry about finding “unicorn” staff.

What are then, the key functional differentiators of Rocana?

Customers pick Rocana for a few reasons: scale, openness, advanced data management features, and cost. We’ve talked a lot about scale already, but openness is equally critical.

Enterprises, frankly, are done with being locked into proprietary formats and vendors holding their data hostage. Once you’re collecting all of this data in one place, customers often want to use Rocana Ops to provide real time streams to other systems without going through expensive translations or extractions.

 Another major draw is the absence of advanced data management features in other systems such as record-level role-based access control, data lifecycle management, encryption, and auditing facilities. When your log events potentially contain personally identifiable information (PII) or other sensitive data, this is critical.

Finally, operating at scale is both a technology and economic issue. Rocana Ops’ licensing model is based on users rather than nodes or data captured by the system freeing customers to think about how best to solve problems rather than perform license math.

Recently, you've released Rocana Ops 2.0, could you talk about these release’s new capabilities?

Rocana Ops 2.0 is really exciting for us.

We’ve added Rocana Reflex, which incorporates complex event processing and orchestration features allowing customers to perform actions in response to patterns in the data. Actions can be almost anything you can think of including REST API calls to services and sending alerts.

Reflex is paired with a first responder experience designed to help ops teams to quickly triage alerts and anomalies, understand potential causes, collaborate with one another, and spot patterns in the data.

One of the major challenges customers face in deploying dynamic next-generation platforms is operational support, so 2.0 includes first-class support for Pivotal CloudFoundry instrumentation and visibility. Those are just a small sample of what we’ve done. It’s really a huge release!

How does Rocana interact with the open source community, especially the Apache Hadoop project?

Open source is core to what we do at Rocana, and it’s one of the reasons we’re able to do a lot of what we do in Rocana Ops.

We’re committed to collaborating with the community whenever possible. We’ve open sourced parts of Rocana Ops where we believe there’s a benefit to the community (like Osso - A modern standard for event-oriented data). As we build with projects like Apache Hadoop, Kafka, Spark, Impala, and Lucene, we look closely at places where we can contribute features, insight, feedback, testing, and (most often) fixes.

The vast majority of our engineers, customer success, and sales engineers come from an open source background, so we know how to wear multiple hats.

Foremost is always our customers’ success, but it’s absolutely critical to help advance the community along where we are uniquely positioned to help. This is an exciting space for us, and I think you’ll see us doing some interesting work with the community in the future.

Finally, what is in your opinion the best and geekiest song ever?

Now you’re speaking my language; I studied music theory.
Lateralus by Tool for the way it plays with the fibonacci sequence and other math without it being gimmicky or unnatural.
A close second goes to Aphex Twin’s Equation, but I won’t ruin that for you.



6 Tech Gadgets That Wouldn’t Have Been Predicted to Be Around a Decade Ago

6 Tech Gadgets That Wouldn’t Have Been Predicted to Be Around a Decade Ago

Tech has come a long way over the years. The world ten years ago, was radically different from the world of today. Despite the best efforts of tech experts, they have failed to predict many of the innovations we take for granted today. Here are the biggest tech gadgets nobody predicted a decade ago.

The Smartphone Revolution

Tech experts knew that smartphones were going to be big. But what they never would have expected is for smartphones to take over conventional desktop computers and laptops. Smartphones were expected to be the natural evolution of the mobile phone, but nobody expected them to take over like they did.

These days a smartphone is so much more than a device for SMS messaging and making calls. It’s used for practically everything. Most people couldn’t live without a smartphone.

Virtual Reality

It’s true that the idea of virtual reality has existed for decades, but a decade ago nobody expected it to become a reality. Today virtual reality is at the forefront of gaming, retail, and a variety of other industries. It’s yet to hit the mainstream, but it’s advancing at an incredible rate. Virtual reality is set to disrupt multiple industries and nobody predicted it would have arrived so soon.

The 3D ...


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Big Data And Its Impact On the E-learning Industry

Big Data And Its Impact On the E-learning Industry

E-learning has been evolving over the years with the advancement in technology and the changing requirements of the users. The processes and techniques associated with the dissemination of knowledge or skills for an effective learning experience have also become sophisticated. We have all heard how Big Data is the future and its impact on various industries. This article will try to throw some light on how Big Data is going to reshape the e-learning industry.

What is Big Data?

We have already used the term ‘Big Data’ more than once in the first paragraph. If you are one of those who is unfamiliar with the term, let us have a brief look at what Big Data is.

Nowadays in the age of information and internet, people and businesses are creating a lot of data (or information) daily. This set of data (or information) is so huge, it is literally known as Big Data. With the help of technology, it is possible nowadays to analyse this data to acquire meaningful patterns and behaviours. This has led to the new area of analytics – Big Data Analytics.

Big Data from an e-learning context

The interactions of users in e-learning, which is a totally digital environment, generate a ...


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Why There Are Opportunities Abound for Green Data Centers

Why There Are Opportunities Abound for Green Data Centers

Traditional data centers are resource hogs. They require a lot of power to operate, not just for the computer equipment, but also for the climate control systems. That’s not the only resource they require, either.

According to the United States Data Center Energy Usage Report, national data centers consumed 626 billion liters of water throughout 2014. That number is expected to reach 660 billion liters by 2020.

As a data center manager, you know water is just being used to cool the facility, so it’s primarily going to waste. That is an incredible amount of excess, and it also translates to a lot of wasted revenue.

However, there are ways to make data centers more efficient, so they are better for the environment and are also financially friendly. Going green can also improve a data center’s performance and uptime, ultimately boosting a company’s resiliency and sustainability. That explains why a lot of data center managers are looking into green technology.

In fact, green data centers are on the rise, so much that the market is expected to be worth $75.89 billion by the end of 2019.

Green Data Center Opportunities

Running a data center can be costly, especially for those unwilling to change. In a way, ...


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Career Path: How Big Data Insights Can Steer Decisions

Career Path: How Big Data Insights Can Steer Decisions

Not long ago, before the time of the internet, Big Data, and apps for everything, it was more common for parents to pass down their careers to their children. Now, autonomy, passion and drive are the keywords for choosing a career. It’s not that family businesses don’t exist. It’s that the new normal is for parents to (hopefully) empower their kids to make a choice based on what they want to do. If this coincides with the family line of work, so much the better.

This new level of individualism opens up a mind-boggling number of choices. It can be overwhelming. But what if you were to use the power of big data to help inform your career path? All of a sudden the guesswork of which way to go is gone. What’s in front of you is a clear picture. With this picture in hand, you’re empowered to make a smart decision.

Where data can take you 

Earnest is a startup dedicated to combining tech and data to help people with their career paths. The data section of Earnest’s blog illuminates some fascinating findings:

Title can determine pay

People with the term “lead” in their title earn an average of $23,000 more than people ...


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7 Surges Found in E-commerce after Implementing Data Management System

7 Surges Found in E-commerce after Implementing Data Management System

Frigid weather can stop you from visiting physical stores, but it cannot prevent you from shopping on your smartphone or laptop. The convenience that comes with E-commerce has shifted the attention of most businesses and customers to the online platforms. However, you should expect some surges as you develop an online store.

An E-commerce surge is a temporary slowdown in the levels of service delivery due to high traffic than the service provider can handle.  Most E-commerce surges result from the presence of fantastic offers at E- commerce stores or during holidays. They compromise the quality of service delivery at E-commerce stores to a great extent.  

However, implementation of a data management system can also lead to surges in online retailers. Therefore, you need to adequately prepare for surges as you implement an E-commerce data management system. E-commerce is the best option especially when you cannot get out of your house to make a purchase. Here are some of the seven surges that you can find after the successful implementation of a Data management system for your online store.

Traffic Surge

Each store has a maximum number of clients that it can handle within a particular period. As you develop an online store, ...


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What is a Data Lake and What Are the Benefits?

What is a Data Lake and What Are the Benefits?

A data lake is a central location in which to store all your data, regardless of its source or format. It is typically, although not always, built using Hadoop. The data can be structured or unstructured. You can then use a variety of storage and processing tools—typically tools in the extended Hadoop ecosystem—to extract value quickly and inform key organizational decisions.

Because of the growing variety and volume of data, data lakes are an emerging and powerful architectural approach, especially as enterprises turn to mobile, cloud-based applications, and the Internet of Things (IoT) as right-time delivery mediums for big data.

Data Lake versus EDW

The differences between enterprise data warehouses (EDW) and data lakes are significant. An EDW is fed data from a broad variety of enterprise applications. Naturally, each application’s data has its own schema, requiring the data to be transformed to conform to the EDW’s own predefined schema. Designed to collect only data that is controlled for quality and conforming to an enterprise data model, the EDW is capable of answering only a limited number of questions. 

Data lakes, on the other hand, are fed information in its native form. Little or no processing is performed for adapting the structure to an enterprise schema. The biggest ...


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Roadmap to CSPO Certification | Simplilearn

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Roadmap to CSPO Certification | Simplilearn Certified Scrum Product Owner (CSPO) is a certification initiated by the Scrum Alliance for professionals who have the ability to serve as excellent Product Owners for Scrum teams. The certification is achieved through passing a CSPO exam. An individual who holds this kind of certification is charged with the following roles; •  &nbs...Read More.
What is CCNA? | Simplilearn

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What is CCNA? | Simplilearn The CCNA certification has been devised by CISCO and stands for Cisco Certified Network Associate. The certificate validates a professional’s ability to understand, configure, operate, configure and troubleshoot medium-level switched and routed networks and also includes the verification and implementation of connections via remote sites usin...Read More.
From Developer to AWS Cloud Specialist – The AWS Certification Learning Paths Explained | Simplilearn

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From Developer to AWS Cloud Specialist - The AWS Certification Learning Paths Explained | Simplilearn The Value of an AWS Certification It is globally known that to earn an AWS certification implies the higher level of qualification of experience in an employer’s and peer’s eyes and increase an organization’s proficiency with applications that are AWS-based. However, there is another benefit that has remained in the dark up until ...Read More.
The Missing Link between Big Data Intelligence and Decision-Making

The Missing Link between Big Data Intelligence and Decision-Making

Big data intelligence is considered the Holy Grail of decision-making in business today. As far back as 2013, some 78% of US business leaders surveyed agreed with the statement that, “If we could harness all of our data, we'd be a much stronger business.”

Indeed, it takes a lot more than having a large amount of readily available – even real-time – information to unlock better, faster decision-making. Experts shout themselves hoarse advocating democratization and smart use of data, as well as fast track analysis, for everything from business intelligence to IoT:



Image source

However, companies are not yet well-equipped to deal with issues emerging from the big data explosion. Issues like “TMI” (too much information) and “analysis paralysis” hurt more than help in decision-making. The answer to this conundrum lies in reducing the latency between data processing and decision-making.

Let’s delve into some ideas and best practices that might help you reduce latency between data collection, mining and decision-making, so you can reap full benefits of your big data tools.

Limit the Scope of Data

The first step towards minimizing analysis paralysis is to define appropriate processes, limits and expectations. Analysis is not a deliverable unto itself. Rather, it is a step in the project ...


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CISA Certification – Modules, Eligibility Criteria and Pluses | Simplilearn

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CISA Certification – Modules, Eligibility Criteria and Pluses | Simplilearn The CISA (Certified Information Systems Auditor) certificate is renowned all across the globe as a standard for Business Systems and Information technology professionals to be able to audit, monitor, access and control data. Being certified identifies candidates for their professional experience, knowledge and skills and further their expertise in ...Read More.
How to become a Certified Salesforce Admin? | Simplilearn

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How to become a Certified Salesforce Admin? | Simplilearn Introduction – What Is The Certification About? The Salesforce ADM 201, or the Salesforce Administrator Certification, is the basic level of qualification for professionals who are involved in and have experience in the application of Salesforce in their organization. Salesforce systems help to manage CRM (Customer Relationship Management) t...Read More.
Roadmap to CEH Certification Infographics | Simplilearn

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CBAP Certification: An Overview | Simplilearn

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11 Reasons to get an ITIL Certification | Simplilearn

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11 Reasons to get an ITIL Certification | Simplilearn The tech industry is constantly changing, making it difficult to stay current with the latest trends. It can also be difficult to know: when something is just a fad versus something that will be around for a while.  The Information Technology Infrastructure Library, or ITIL, is one of the few things that is an established entity within the IT...Read More.
Quick Tips for Clearing CCBA Certification Exam | Simplilearn

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Why Cloud Computing Makes DevOps Inevitable | Simplilearn webinar starts 31-01-2017 10:30

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With cloud computing having solved the infrastructure availability problem, IT organizations confront the next issue: speed of application development.  This webinar will discuss why cloud computing makes DevOps inevitable, and how IT organizations should pursue their DevOps journey.  Specific topics covered include:  Why DevOps i...Read More.
Statistics Denial Myths #13-14, Minimizing The Profession

Statistics Denial Myths #13-14, Minimizing The Profession

"Leo Breiman was still a statistician, and while he critiques parts of his profession he's not dismissing the importance of statistics.  The same can be said for David Donoho's excellent recent article, "50 Years of Data Science."  The fact that some statisticians have too narrow a scope in no way diminishes the validity and importance of statistics to good decisions and valid science.  Sure, it is incumbent upon statisticians to prove their worth to the larger, emerging discussions around data science, but I remain concerned about "data scientists" who may be able to wrangle data well and write code but don't understand the fundamentals of analysis." — Polly Mitchell-Guthrie

Myth #13: Statisticians are homogeneous

Myth #14: Academic statisticians are typical of and can speak for the whole profession

First, statisticians are not homogeneous. 

Instead, there is a rich diversity of applied statisticians/quants and they work on every conceivable data analysis problem and in every technically advancing field.  A statistics degree is not required; knowledge of applied statistics and the domain are essential.

Sometimes, we perceive groups that are unfamiliar to us as homogeneous. Table 1 explores pockets of statisticians in no particular order.  The descriptions were not robustly collected; there are no comprehensive information sources, no ...


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How Blockchain Can Offer a Secure Model for the Internet of Things

How Blockchain Can Offer a Secure Model for the Internet of Things

The Internet of Things (IoT) is an ecosystem of ever-increasing complexity; it’s the next wave of innovation that will humanize every object in our life, and it is the next level of automation for every object we use. IoT is bringing more and more things into the digital fold every day, which will likely make IoT a multi-trillion dollar industry in the near future.

To understand the scale of interest in the internet of things (IoT) just check how many conferences, articles, and studies have been conducted about IoT recently. This interest has hit fever pitch point in 2016 as many companies see big opportunities and believe that IoT holds the promise to expand and improve businesses processes and accelerate growth. However, the rapid evolution of the IoT market has caused an explosion in the number and variety of IoT solutions, which created real challenges as the industry evolves, mainly, the urgent need for a secure IoT model to perform common tasks such as sensing, processing, storage, and communicating. Developing that model will never be an easy task by any stretch of the imagination, there are many hurdles and challenges facing a real secure IoT model.

There are many views of IoT, ...


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How Big Data Takes on Big (Boob) Problems

How Big Data Takes on Big (Boob) Problems

Big Data application comes in many shapes and sizes and the Big Data Bra is Big Data wrapped in the silk and latex of lingerie.

It starts with a simple problem

We have come a long way, from corsets and elastic superstructures. The width and breadth of materials and configurations are overwhelming today and surpassed only by the running shoe industry in the utilitarian fashion spectrum.

Yet bras still don’t fit well. For those who “need” to wear them, they pinch and sag and ride up your back. You push and pull to pack ‘em in and in return they pooch and groan to comply. The straps spend a lifetime slowly paving a permanent groove over the shoulder. At the end of the day, within the privacy of the house, you pop the straining band – freedom!

Open the bra drawer on the dresser and the volume and variety (2 Vs) are apparent. There’s a bra for every occasion: bras for sweaters or for t-shirts or racer back or the all-day-at-work ones. The padded, the push-ups and the breast minimizers. Going to the gym or hitting the trail is a whole different world altogether, with sports bras equipped in extraordinary configurations to look good ...


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5 Things You Need to Know about Big Data for 2017

5 Things You Need to Know about Big Data for 2017

Big Data made a Big Difference in the biggest story of the year – the US Presidential election. Although President-Elect Trump had pooh-poohed the impact of Big Data, he rallied a last minute expert team in the summer prior that just may have made the difference.

Using digital targeting, the strategy collected information from online and offline sources to find potential voters. With over 4,000 finely tuned messages, a specific one was placed after assessing the potential voter’s Facebook, Pandora and Snapchat activity. Virtual grassroots at its finest.

Not being a billionaire or President . . .

What do you need to know about Big Data in 2017?

Bringing Big Data to the people. Whether you are an experienced data scientist or an aspiring one, whether you are in big business or a one-man shop, whether you are worried about your weight or what your government is doing – Big Data is a part of everyone’s future. 

#1 Not a Fad

The 3 Vs (Volume, Velocity and Variety) of Big Data were coined by META Group (now Gartner) analyst Doug Laney in 2001. In the ensuing 15 years, it has gotten a lot of attention from techies, industry and the public. I think it’s up to six ...


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Big data nyugati és keleti szemléletben

Big data nyugati és keleti szemléletben

A Műegyetem büféjében egy közgazdász kolléganőnktől hallottam egy érdekes gondolatot (mémet), melyen az elmúlt hónapban sokat morfondíroztam. Zsuzsa meglátása szerint az európai kultúrában a gazdaságban megjelenő adatokhoz, az azokban megjelenő összefüggésekhez, a ma big data néven futó jelenséghez külön nyugati és keleti (bizánci) megközelítés tartozik. A nyugati gondolkodásmódban az adatokra támaszkodva növelhetjük a hatékonyságunkat, alkalmazkodhatunk a változó környezethez, versenyelőnyhöz juthatunk - így az adatelemzés célja tipikusan a profitnövelés irányába mutat. A keleti (bizánci) szemléletben az adatok gyűjtésének célja az ellenőrzés, az állami vagy egyéb hatalom felügyeleti tevékenységéhez tartozik - célunk az ellenőrzés, a kitűzött célok végrehajtásának ellenőrzése. 

east-vs-west.jpgValóban kézzelfoghatóan különválik ez a két szemlélet, még akkor is, ha egy-egy jól működő szervezeten belül mindkettőnek meg kell jelennie. Ha technológiai szemmel nézzük az gépi tanulási feladatok előrejelzéshez köthető részei inkább a profitmaximalizáló, hatékonyságnövelő szemlélethez köthető, míg az én látásmódomban kicsit a klaszterezés és nagy mértékben az anomália detekció a felügyelő / ellenőrző látásmódhoz köthető. Egy BI vagy riporting rendszer magában nem köthető egy-egy területhez, de a használatuk mögötti motiváció gyakran az egyik szemlélethez húz. 

Az elmúlt héten áttekintettem a fenti szempontból a 2016-os projektjeinket (jó lehetőséget adott erre, mikor az Éviránytű évértékelő munkafüzetét töltöttem ki), és egyértelműen szátváltak erre a két csoportra, még akkor is, ha egyes partnereknél végül mindkét irány megjelent. Az egyik legtipikusabb példa, mikor kamionsofőrök fogyasztási szokásainak elemzését végeztük: elsőként úgy volt megfogalmazva az üzleti kérdés, hogy találjuk meg, mi a különbség a jól fogyasztási adatokkal futó sofőrök és a több üzemanyagot használó kollégáik között. A projekt végkicsengésénél viszont megjelent az az igény, hogy mennyivel jobb lenne a sofőr szokásai, az útviszonyok, az időjárás és a rakomány figyelembevételével olyan útvonaltervezést megvalósítani, ahol a becsült üzemanyagköltséggel és a különböző útdíjakkal egyszerre tudnánk számolni.

Elgondolkodtam azon is, vajon milyen szemlélet jellemző hazai cégekre? Ez cégkultúrától függ, ami alakítható - így talán nem is ez a jó kérdés. Ha az egyének szintjén vizsgáljuk a kérdést, azt mondhatjuk, hogy a magyarok alap beállítottsága inkább a keleti szemlélethez húz. Jó példázza ezt nekem, hogy mikor egy társaságban elmesélem, hogy egy jó és egy rossz kamionsofőr között 3-4 liter fogyasztás-különbség is lehet 100 km-en, a legtöbben azt a zsigeri választ adják, hogy biztos lopják az üzemanyagot. Az adatokból látszik, hogy rengeteg oka lehet a különbségnek (például mennyit használja a tempomatot az vezető), de az alap asszociációnk oda mutat, az adatok valami kis stiklit, csalást, trükközést fognak felfedni.

Fontos kiemelni, hogy ez a kettősség nem a személyes adatokról vagy privacy védelméről szól - de mégis van ide vágó aspektusa. Képzeljük el, hogy a munkahelyünkön minden eddiginél pontosabb és jobb adatgyűjtést vezet be a főnökség, például pontosabb képet fognak kapni az egyes kollégák teljesítményéről. A változást lehet pozitívan látni ("végre látni fogják, milyen sokat tettem a cégért"), vagy negatív módon viszonyulni hozzá ("ki fogják szúrni, hogy pénteken hamarabb szoktam lelépni"), és utána ennek megfelelően lehet támogatni vagy szabotálni a bevezetést. Mindenkire rábízom, hogy mit tenne ő egy ilyen szituációban. 

Bármilyen is az alap beállítottságunk, erre rálátva tudatosan tudjuk integrálni a kétfajta szemlélet előnyeit. Izgalmasabb kérdés számomra, hogy mennyire más módon kell a különböző szemléletű cégeknél egy-egy megoldást bevezetni, mennyire más motivációk és félelmek uralják a gondolkodást a két esetben.

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How lastminute.com uses machine learning to improve travel bookings user experience

How lastminute.com uses machine learning to improve travel bookings user experience

The next BriefingsDirect Voice of the Customer digital transformation case study highlights how online travel and events pioneer lastminute.com leverages big-data analytics with speed at scale to provide business advantages to online travel services.

We'll explore how lastminute.com manages massive volumes of data to support cutting-edge machine-learning algorithms to allow for speed and automation in the rapidly evolving global online travel research and bookings business.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.

To learn how a culture of IT innovation helps make highly dynamic customer interactions for online travel a major differentiator, we're joined by Filippo Onorato, Chief Information Officer at lastminute.com group in Chiasso, Switzerland. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Most people these days are trying to do more things more quickly amid higher complexity. What is it that you're trying to accomplish in terms of moving beyond disruption and being competitive in a highly complex area?
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Onorato: The travel market -- and in particular the online travel market -- is a very fast-moving market, and the habits and behaviors of the customers are changing so rapidly that we have to move fast.

Disruption is coming every day from different actors ... [requiring] a different way of constructing the customer experience. In order to do that, you have to rely on very big amounts of data -- just to style the evolution of the customer and their behaviors.

Gardner: And customers are more savvy; they really know how to use data and look for deals. They're expecting real-time advantages. How is the sophistication of the end user impacting how you work at the core, in your data center, and in your data analysis, to improve your competitive position?

Onorato
Onorato: Once again, customers are normally looking for information, and providing the right information at the right time is a key of our success. The brand we came from was called Bravofly and Volagratis in Italy; that means "free flight." The competitive advantage we have is to provide a comparison among all the different airline tickets, where the market is changing rapidly from the standard airline behavior to the low-cost ones. Customers are eager to find the best deal, the best price for their travel requirements.

So, the ability to construct their customer experience in order to find the right information at the right time, comparing hundreds of different airlines, was the competitive advantage we made our fortune on.

Gardner: Let’s edify our listeners and reader a bit about lastminute.com. You're global. Tell us about the company and perhaps your size, employees, and the number of customers you deal with each day.

Most famous brand

Onorato: We are 1,200 employees worldwide. Lastminute.com, the most famous brand worldwide, was acquired by the Bravofly Rumbo Group two years ago from Sabre. We own Bravofly; that was the original brand. We own Rumbo; that is very popular in Spanish-speaking markets. We own Volagratis in Italy; that was the original brand. And we own Jetcost; that is very popular in France. That is actually a metasearch, a combination of search and competitive comparison between all the online travel agencies (OTAs) in the market.

We span across 40 countries, we support 17 languages, and we help almost 10 million people fly every year.

Gardner: Let’s dig into the data issues here, because this is a really compelling use-case. There's so much data changing so quickly, and sifting through it is an immense task, but you want to bring the best information to the right end user at the right time. Tell us a little about your big-data architecture, and then we'll talk a little bit about bots, algorithms, and artificial intelligence.

Onorato: The architecture of our system is pretty complex. On one side, we have to react almost instantly to the search that the customers are doing. We have a real-time platform that's grabbing information from all the providers, airlines, other OTAs, hotel provider, bed banks, or whatever.

We concentrate all this information in a huge real-time database, using a lot of caching mechanisms, because the speed of the search, the speed of giving result to the customer is a competitive advantage. That's the real-time part of our development that constitutes the core business of our industry.

Gardner: And this core of yours, these are your own data centers? How have you constructed them and how do you manage them in terms of on-premises, cloud, or hybrid?

Onorato: It's all on-premises, and this is our core infrastructure. On the other hand, all that data that is gathered from the interaction with the customer is partially captured. This is the big challenge for the future -- having all that data stored in a data warehouse. That data is captured in order to build our internal knowledge. That would be the sales funnel.
Right now, we're storing a short history of that data, but the goal is to have two years worth of session data.

So, the behavior of the customer, the percentage of conversion in each and every step that the customer does, from the search to the actual booking. That data is gathered together in a data warehouse that is based on HPE Vertica, and then, analyzed in order to find the best place, in order to optimize the conversion. That’s the main usage of the date warehouse.

On the other hand, what we're implementing on top of all this enormous amount of data is session-related data. You can imagine how much a data single interaction of a customer can generate. Right now, we're storing a short history of that data, but the goal is to have two years' worth of session data. That would be an enormous amount of data.

Gardner: And when we talk about data, often we're concerned about velocity and volume. You've just addressed volume, but velocity must be a real issue, because any change in a weather issue in Europe, for example, or a glitch in a computer system at one airline in North America changes all of these travel data points instantly.

Unpredictable events

Onorato: That’s also pretty typical in the tourism industry. It's a very delicate business, because we have to react to unpredictable events that are happening all over the world. In order to do a better optimization of margin, of search results, etc, we're also applying some machine-learning algorithm, because a human can't react so fast to the ever-changing market or situation.

In those cases, we use optimization algorithms in order to fine tune our search results, in order to better deal with a customer request, and to propose the better deal at the right time. In very simple terms, that's our core business right now.

Gardner: And Filippo, only your organization can do this, because the people with the data on the back side can’t apply the algorithm; they have only their own data. It’s not something the end user can do on the edge, because they need to receive the results of the analysis and the machine learning. So you're in a unique, important position. You're the only one who can really apply the intelligence, the AI, and the bots to make this happen. Tell us a little bit about how you approached that problem and solved it.
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Onorato: I perfectly agree. We are the collector of an enormous amount of product-related information on one side. On the other side, what we're collecting are the customer behaviors. Matching the two is unique for our industry. It's definitely a competitive advantage to have that data.

Then, what you do with all those data is something that is pushing us to do continuous innovation and continuous analysis. By the way, I don't think something can be implemented without a lot of training and a lot of understanding of the data.

Just to give you an example, what we're implementing, the machine learning algorithm that is called multi-armed bandit, is kind of parallel testing of different configurations of parameters that are presented to the final user. This algorithm is reacting to a specific set of conditions and proposing the best combination of order, visibility, pricing, and whatever to the customer in order to satisfy their research.

What we really do in that case is to grab information, build our experience into the algorithm, and then optimize this algorithm every day, by changing parameters, by also changing the type of data that we're inputting into the algorithm itself.
It's endless, because the market conditions are changing and the actors in the market are changing as well.

So, it’s an ongoing experience; it’s an ongoing study. It's endless, because the market conditions are changing and the actors in the market are changing as well, coming from the two operators in the past, the airline and now the OTA. We're also a metasearch, aggregating products from different OTAs. So, there are new players coming in and they're always coming closer and closer to the customer in order to grab information on customer behavior.

Gardner: It sounds like you have a really intense culture of innovation, and that's super important these days, of course. As we were hearing at the HPE Big Data Conference 2016, the feedback loop element of big data is now really taking precedence. We have the ability to manage the data, to find the data, to put the data in a useful form, but we're finding new ways. It seems to me that the more people use our websites, the better that algorithm gets, the better the insight to the end user, therefore the better the result and user experience. And it never ends; it always improves.

How does this extend? Do you take it to now beyond hotels, to events or transportation? It seems to me that this would be highly extensible and the data and insights would be very valuable.

Core business

Onorato: Correct. The core business was initially the flight business. We were born by selling flight tickets. Hotels and pre-packaged holidays was the second step. Then, we provided information about lifestyle. For example, in London we have an extensive offer of theater, events, shows, whatever, that are aggregated.

Also, we have a smaller brand regarding restaurants. We're offering car rental. We're giving also value-added services to the customer, because the journey of the customer doesn't end with the booking. It continues throughout the trip, and we're providing information regarding the check-in; web check-in is a service that we provide. There are a lot of ancillary businesses that are making the overall travel experience better, and that’s the goal for the future.

Gardner: I can even envision where you play a real-time concierge, where you're able to follow the person through the trip and be available to them as a bot or a chat. This edge-to-core capability is so important, and that big data feedback, analysis, and algorithms are all coming together very powerfully.

Tell us a bit about metrics of success. How can you measure this? Obviously a lot of it is going to be qualitative. If I'm a traveler and I get what I want, when I want it, at the right price, that's a success story, but you're also filling every seat on the aircraft or you're filling more rooms in the hotels. How do we measure the success of this across your ecosystem?
We can jump from one location to another very easily, and that's one of the competitive advantages of being an OTA.

Onorato: In that sense, we're probably a little bit farther away from the real product, because we're an aggregator. We don’t have the risk of running a physical hotel, and that's where we're actually very flexible. We can jump from one location to another very easily, and that's one of the competitive advantages of being an OTA.

But the success overall right now is giving the best information at the right time to the final customer. What we're measuring right now is definitely the voice of the customer, the voice of the final customer, who is asking for more and more information, more and more flexibility, and the ability to live an experience in the best way possible.
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So, we're also providing a brand that is associated with wonderful holidays, having fun, etc.

Gardner: The last question, for those who are still working on building out their big data infrastructure, trying to attain this cutting-edge capability and start to take advantage of machine learning, artificial intelligence, and so forth, if you could do it all over again, what would you tell them, what would be your advice to somebody who is merely more in the early stages of their big data journey?

Onorato: It is definitely based on two factors -- having the best technology and not always trying to build your own technology, because there are a lot of products in the market that can speed up your development.

And also, it's having the best people. The best people is one of the competitive advantages of any company that is running this kind of business. You have to rely on fast learners, because market condition are changing, technology is changing, and the people needs to train themselves very fast. So, you have to invest in people and invest in the best technology available.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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Is Your Data Integrity Causing You Fear, Uncertainty and Doubt?

You know those sales calls that you get from software companies, where the eager sales rep asks you what keeps you up at night? Claiming that their “solution” will integrate your existing systems, reduce your technology costs, generate powerful reports, and improve employee productivity? Seems like an information executive’s Utopian ideal, doesn’t it?

Whether that sales rep’s company can deliver on all of these promises isn’t as material to this article as whether your company’s data can be trusted to make important decisions on. The individual data records themselves are innocent enough, they likely have been generated by your customers, employees or suppliers with the best of intentions. It’s when they “socialize” through integration with data from other systems, get mixed up with duplicate records, or are manipulated by employees in various departments of your company that “Good” data can go “Bad”.

Struggles with bad data are common across many industries and companies

Running multiple departmental reporting products against applications like CRM, ERP or POS systems have been the go-to strategy for most companies. Departmental managers generally trust their own local data, but prefer to put a wall around it from the enterprise. A recent Experian study, and infographic published in InsideBigData.com showed:


66% of ...


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4 Industries Leading the Way in IoT Integration

4 Industries Leading the Way in IoT Integration

The Internet of Things (IoT) is coming, and almost no one is disputing that reality. Someday in the near future, most devices will be connected and networked to make our lives easier. The only question now is…when? Some industries have been quicker than others to hop on board and build the technology and integrate IoT devices into their everyday operations. Here are 4 that are leading the way in IoT integration—and reaping the benefits. 

Utilities

There’s a lot of waste in public utilities, and many companies are using IoT sensors and other devices to cut down on waste and promote efficiency. Smart meters and smart grids allow utility companies to better analyze demand and cut down on inefficiencies by distributing utilities according to where they’re needed at any given time. As utility systems are replaced, they’re being upgraded to connected, digitized systems, which offer more opportunities for cities to become greener, smarter, and less wasteful. 

Transportation

Self-driving cars are the next big step in transportation, and these systems rely heavily on IoT sensors to pilot the car and prevent collisions. According to Intel, a self-driving car must process about 1GB of data per second, using advanced computing systems and analyzing potential threats in real time. ...


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How Big Data Can be Used Responsibly in Higher Education

How Big Data Can be Used Responsibly in Higher Education

Big data has been used to improve almost every aspect of our lives, and higher education is no exception. Over the past few years, universities have been working hard to figure out how to leverage the massive amount of data they’re collecting to make their schools perform better and assist students in completion. Predictive analysis of data sets can be used to improve efficiency and tailor the college experience according to students’ needs. However, some insights gained can be used for irresponsible purposes, and higher education organizations need to be aware of these pitfalls and avoid them. Here are some of the ways big data can be used responsibly in higher education.

1. Improving Graduation Rates

Universities have been challenged to maintain high retention rates for years, and many state schools receive funding based on several performance benchmarks that include retention, on-time completion, and transfer rates. Georgia State University achieved great success in using data analysis to help improve graduation rates for low income and minority students. Using the data they collected from students, Georgia State amped up advising efforts and was able to close the gap between these low income/minority students and the rest of the student body over a ten ...


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Want to Learn Something New in 2017?

Want to Learn Something New in 2017?

Happy New Year everyone! This is the time of year when we all make plans and set goals for the new year, right? So what are you going to do different this year? How about grow your career by learning something new? How? Read a book on an area of tech you are not so familiar […]
How to Overcome Challenges With Dynamic Data

How to Overcome Challenges With Dynamic Data

We have come a long way in terms of the technology and infrastructure required to handle big data processes. It is not uncommon for retailers to run big data tests on their customer database to identify patterns and trends in buying behavior. Similarly, businesses routinely run big data processes on their logistics and operations to optimize routes and improve their margins. Many of these everyday big data problems have static large databases to deal with and consequently, the challenges here are mostly infrastructure-related and may be fixed by increasing capacity and investments.

This is not always sufficient though. High Frequency Trading, for instance, relies on interpreting millions of data points each second to make buy/sell calls on the fly. Instead of betting on major news stories like earnings report and product releases, HFT uses algorithms to identify and execute buy/sell calls often in fractions of seconds. The database here is extremely dynamic and this adds another layer of complexity to big data processing.

There are several other use-cases for dynamic big data processing. PR agencies monitoring brand reputation online often have to track down and reach out to negative reviews as and when they happen in order to minimize brand impact and ...


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Common Data Security Screw-ups On The Cloud

Common Data Security Screw-ups On The Cloud

Data security on the cloud is an issue which usually gets lost in the discussion. Understanding the many critical avenues of securing data on the cloud is an absolute must for proper cloud data security. Businesses implementing cloud hosted services believe that all data security procedures are covered by their service providers. But this not completely true! Digital data is generally very volatile and requires safe practices from the client's end as well. 

With that being said, here are a few mistakes that are common among businesses, that may turn ugly and result in data security screw-ups on the cloud. 

Failing to understand the data you need to protect 

The data security measures put in place should always harmonize with the type of data meant to be secured on the cloud. Different service providers take different approaches to their data security strategy, which greatly depends on the type of data to be stored and secured on the cloud. Businesses which blindly implement a single type of data security protocol for all the different types of their data are at risk of spending more than they need to on data security, under-protect certain data types, or even creating legal or compliance related issues. 

Failing to consider data in flight 

Most service providers and businesses alike only consider data encryption when it is stored somewhere and is at rest. They fail ...


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Happy New Year!

Happy New Year!

Filed under: Uncategorized Tagged: #HappyNewYear, Happy New Year
Is Your Company Guilty Of These 4 Cyber Security Mistakes?

Is Your Company Guilty Of These 4 Cyber Security Mistakes?

Security concerns are all around us, particularly with the proliferation of the internet. Data breaches should be a top concern for all major companies, but they’re surprisingly not very high on the list for many. According to research from the Cisco 2016 Annual Security Report, only 29 percent of small to mid-size businesses used standard tools to prevent security breaches in 2015. What’s more surprising is that this percentage is 10 percent less than those who used it in 2014.

With 80 percent of companies expected to experience a cyberattack in the course of a year and the average cost of stolen records being $154, this is a cost that businesses should be better equipped to handle.

1. You don’t use tools properly

When considering the ways that businesses could better handle their IT concerns, it’s useful to compare the security of a business to that of a parent with a child. Because of social media, parents must be more careful in their dealings with child security. For example, it’s a common practice to put creative labels on kids’ belongings to keep track of their items, but allowing a stranger to see your child’s name can also put them in danger. Parents must ...


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Blending Agile with Waterfall | Simplilearn webinar starts 12-01-2017 10:30

Blending Agile with Waterfall | Simplilearn webinar starts 12-01-2017 10:30

One size does not fit all. In the Software Development world, not everything can be a perfect fit for Agile OR Waterfall method. Some projects can immensely benefit from a Hybrid approach that has best practices from both schools of thought.  The webinar will cover the following  Introduction to Waterfall, Agile and Hybrid Methods for...Read More.
What Big Data Could Have in Store for the Car Industry by the Year 2030

What Big Data Could Have in Store for the Car Industry by the Year 2030

For the past ten years, the car industry has been one of the biggest industries that has had the most beneficial technological advancements. The use of data in and outside the cars has been the a very significant technological improvement. With this technology, cars are safer, more user friendly and more efficient. Car manufacturers are always looking to improve their models and with big data, they are able to spring forward in their industry.

With the advancements of bluetooth, sensors, seats, trunks, screens and apps, big data has improved these new features even more. What customers are looking for is the bigger, better model and each car company is competing to come out with the features everyone wants. Whether it’s the Toyota Rav4 Hybrid or the 2017 Honda Civic, customers want fancier, flashier cars. It’s the science of the car industry.

The question on both the manufacturer's and the customer’s minds is, what is next? Customers, in any industry ask this question that puts pressure on the company but it also pushes the company to become bigger and better. Customer’s interest and loyalty is the reason car companies come out with new models every year. So, with the addition of big data ...


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Top 5 Analytics trends in Fashion Retail

Top 5 Analytics trends in Fashion Retail

Technology has enriched the overall customer experience. As a result, today’s leading fashion houses are looking at several ways to utilize emerging analytical technologies in fashion retail today. 

Let’s look at some of the ways this is happening today.

Digital Marketing and Social Media Analytics

Digital marketing analytics expenses increased by 60% in 2015 as branding and advertising businesses boomed. Social media and online advertising on mobile will continue to grow as integration of offline and online customer experience is on the rise. This in turn, has increased consumer brands’ ability to digitally influence customers and digitally empowered customers’ ability to influence brand image and value.

Cross-selling and upselling through Personalization

Owing to advancements in technology combined with the avalanche of data available today, enterprises across industries are leveraging inexpensive technologies such as Hadoop to analyze huge amounts of customer data, understand patterns and subsequently personalize their offers to their customers. This in turn helps them out-think and out-do the competition. “More data storytelling equals more engagement”.

A leading Indian retailer boosted category growth by 50% with tailored campaigns based on affinity analysis, cross promotion between categories like kids, baby world and toys.

Strategic customer segmentation enabled the business to drive a consistent marketing strategy across all concepts. Customer ...


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Top 10 Insights with Datafloq Articles in 2016

Top 10 Insights with Datafloq Articles in 2016

2016 has come to an end, so it is time to look back at all the great articles that were published on Datafloq. From artificial intelligence to blockchain and the Internet of Things, our most popular articles covered leading and emerging technology trends. Take a minute and read-up on the articles help you better understand these trends.

For 2017, we will continue to publish great articles to help you understand and get started with these technologies. At Datafloq we are looking to grow our network of expert contributors. If you have expertise in Big Data, AI, Robotics, IoT, VR, Cloud, or Technology in general and are interested in contributing please click here.

For now, enjoy reading these articles and we wish you a fantastic 2017 with new emerging technology and trends!

12 Algorithms Every Data Scientist Should Know

Algorithms have become part of our daily lives and can be found in any aspect of business. What are the 12 algorithms every data scientist should know?

The Top 7 Big Data Trends for 2017

Which Big Data trends will create the Year of Intelligence? Here are the top 7 Big Data trends for 2017 that will affect organisations and governments.

13 Predictions on Artificial Intelligence

Artificial Intelligence already had a ...


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What Is Fog Computing? And Why It Matters In Our IoT And Big Data World

What Is Fog Computing? And Why It Matters In Our IoT And Big Data World

Fog computing is a disruptive technology that adds another level of complexity to Cloud computing, which is the centralization of information into the hands of developers like Amazon (S3) who offer both massive storage and data analytics and processing for the data on their servers. Fog computing is a term that was laid out by a Cisco Systems paper, and is also known by IBM as “Edge Computing”. Fog computing is positioned to revolutionize production systems, transportation, and even the networks on which we execute business daily.

Contributions to Cloud Computing

Fog computing contributes to the Cloud computing platform by allowing for devices to transmit and process sensory data, system updates, and even application processing amongst themselves. The need for the Fog contribution is shown in a study by Forbes, which points out that the U.S. is ranked 35th for bandwidth per user in the world. This limitation will likely have a lessened impact on business as the Internet of Things and Big Data adapt to Fog Computing and transcend their current bandwidth limits.

Relation to Internet of Things

The discrepancy drives a need for innovation, and the Internet of Things (IoT) allows for devices to share processing power, as well as to push ...


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How Cloud Computing Affects Individuals and Organizations

How Cloud Computing Affects Individuals and Organizations

Cloud computing is a computation technique that depends on computing resources which are shared over the internet. In cloud computing resources are hosted remotely and are accessible to as many persons as the cloud computing servers restrict. Each individual or organization is allocated a “workspace” or is allowed to access applications and software pertinent to their functioning.

How cloud computing applies in individuals

Cloud computing is evident in our interactions with computers and internet and we may consciously or naively use it. An elaborate example of cloud computing services is using Google mail services, alternatively named Gmail. In this cloud computing formulation Google hosts the mail services for you. Instead of burdening your laptop or personal computer with software to organize your mails, Google enables you to do it online by just having internet connection and an internet enabled device. It is important to note that cloud computing also offers storage facilities to a company as stated by PCMag.com.

Another example of such cloud computing service that many people use is the programmer’s platform GitHub. GitHub enables a computer or mobile programmer to save his codes online. This can be done when a complete program is written or gradual saving as an application ...


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Merry Christmas!

Merry Christmas!

Filed under: Uncategorized Tagged: #MerryChristmas, Merry Christmas
The Impact of Predictive Analytics on Horse Racing

The Impact of Predictive Analytics on Horse Racing

Predictive analytics and big data have been used in sports for well over a decade. Using advanced statistical analysis allows professional sports franchises to better identify talent and predict future player performance.

In 2002, the trend entered the mainstream when Billy Beane, the Oakland A's general manager, used it to create a cost-effective baseball roster. He used the data to predict that a player's on-base percentage was more important for team success than traditional stats such as their batting averages or home runs.

Beane’s strategy was successful and led to sustained success by the A’s, a small-market team with a payroll exponentially smaller than many of their competitors such as the New York Yankees. Since Beane’s “Moneyball” era, predictive analytics and big data have taken over the sports world.

Beyond baseball, another sport where such tools are especially prominent is horse racing. Data mining, statistics, modeling, machine learning, and a variety of other tools are used by owners, trainers, race course management, bookies, and bettors alike.

Equibase and the Origins of Big Data in Horse Racing

In 1990, the Jockey Club and the Thoroughbred racing associations collaborated to form Equibase as means to collect and share thoroughbred data. In the two and a half decades ...


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A túlterhelt csomagküldők esete – adatelemző szemmel

A túlterhelt csomagküldők esete – adatelemző szemmel

Idén az adventi időszak kötelező feladatai közé bekerült a Posta illetve más csomagküldő szolgálatok szidása, miszerint miért nem készültek fel időben a karácsonyi dömpingre. Örök téma ez, bárkivel el lehet róla beszélgetni, mindenkit érint és mindenki ért hozzá - olyan mint az időjárás, a politika vagy a foci. A szállingózó történetek először idén is a Postáról szóltak, de az elmúlt hetekre végülis minden csomagküldőre kiterjedtek, végül nem egyetlen cég bénázásáról, hanem valami általánosabb jelenségről van szó.

Tudvalevő, hogy a karácsonyi webshop őrületre a csomagküldő szolgáltatók is készülnek. Ez a szektor az elmúlt évtized EU-s szabályozási változtatásai kapcsán egy fejlődő, sokszereplős és versenyezni képes iparággá nőtte ki magát, ahol a hétköznapi értelemben van innováció, a legtöbb cég vezetőit kőkemény üzleti racionalitás hatja át: a karácsony a legerősebb időszakuk, biztosan kalkuláltak a megnövekedett feladatokkal. Ahogy a hírekben hallható mentegetőzésből kijön, azt is tudták, hogy nemcsak több, hanem a tavalyinál jóval több csomagot kell elvinni majd. Több helyen is olvashatjuk, hogy a karácsonyi időszak csomagmennyisége évi 20%-kal növekedett az elmúlt években, így a legtöbb helyen erre a mennyiségre lőtték be a kapacitásaikat.

screen_shot_2016-12-22_at_23_44_33.pngÉs itt jön a fordulat - idén a 40%-kal több csomagot adtunk fel. Persze lehetne ezt a hazai webes áruküldés csodájaként is megélni, ünnepelhetnénk a hazai digitális fordulatot (mint ahogy sok más országban egy-egy black friday után a webes cégek azzal dicsekednek mennyivel dőltek meg az eddigi rekordok), ehelyett inkább azt hallhatjuk, hogy ez az óriási különbség mennyire váratlanul érte az előrejelzés szerint 20%-os emelkedésre számító szereplőket.

ÁBRA: Az eNet pont egy évvel ezelőtti infografikájából kivágott rész a hazai webes kereskedelem forgalmáról - Eredeti cikket is ajánlom figyelmetekbe: eNet: E-kereskedelmi körkép 2015

Adatelemzőként persze csak csóválom a fejem. Ha valami három éve 20%-ot nő, akkor a következő évre 20% növekedést prognosztizálni elsőre nem tűnik butaságnak. Csak akkor, ha valamit épp pont a web hajt. Hol hallottunk olyat egy webes trend kapcsán, hogy úgy örökké egyenletesen emelkedett? A weben a dolgok berobbanása vagy elhalása a gyakoribb. Mindemelett 40% növekedés nem berobbanás. És itt kezdek zavarba jönni adatok híján: vajon megvizsgálták a cégek, hogy a sok rendelés közül mely webáruházakra, mely termékcsoportokra volt jellemző a felfutás? Nagy összegekben mernék fogadni, hogy a többlet nem egyenletesen oszlik el. Vagy nem egyenletes vásárlói csoporton. A web világa csak eleinte "lassú víz partot mos" jellegű - később vagy nem lesz semmi, vagy jön a földcsuszamlás. Ez az óriási melléfogás bizony az előrejelzést végzők hibája - a csomagküldés piacát legjobban a webes kiskereskedelem hajtja, ennek elemzése nélkül nem lehet csak a görbére ránézni, és csak úgy továbbhúzni a vonalat.

Hallottam persze furfangos magyarázatokat is - szigorúan a felelősöket minél távolabb keresve. A kedvenc két témám a vasárnapi boltbezárás és a későn érkező hideggel magyarázza a dolgokat. Az első úgy jön a képbe, hogy a vasárnapi boltbezárás időszaka alatt a webáruházak jóval elfogadottabbak lettek az elmúlt években, ez a hatás "gyűrűzött" be most karácsonykor ennyire erősen. Egy másik elmélet azzal magyarázza a dolgokat, hogy későn jött be az igazán hideg hazákba, ami hatására mindig jobban felpörögnek az otthon melegéből rendelhető termékek forgalma. Egyik hatásra vonatkozó elemzéseket se ismerek, de mindkettő inkább műértő okoskodásnak tűnik elsőre.

Mit tegyenek a csomagküldők jövőre? Ezer ötletem van az adatelemzés területén kívülről is, kezdve a novemberben előre megvásárolható futárkapacitástól, a rendelés pillanatában előre kalkulált valós érkezési időpontig. Az adatok jobb kihasználása nem annyit tesz, hogy jövőre komolyabb aparátussal ki tudnak hozni 20+x százaléknyi várható forgalomnövekedést, és ezt higgye el mindenki. Itt folyamatokhoz kellene hozzányúlni, miközben extra nagy források a feladat megoldására nem állhat rendelkezésre.

Leginkább azokat az ajándékozni vágyókat sajnálom, akik félve figyelik a kaput, megjön-e még a csomagjuk karácsony este előtt. Drukkolunk nekik és bízunk hogy nem kuponok gyártásával telik majd a szombat délutánjuk.

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6 Steps You Should Be Taking to Secure Your Corporate Data

6 Steps You Should Be Taking to Secure Your Corporate Data

In today’s data-driven world, information is as valuable as currency. All the data your company collects is a commodity valuable to somebody—it might take the form of customer information, private financial data, or even proprietary secrets. Losing any of this data, or having it compromised could be a major threat to your business; not only would you lose some of your most valuable information, you could put it in irresponsible hands and lose your reputation as a trustworthy business.

How Data Is Vulnerable

So what exactly could happen to your corporate data?


Theft. Hackers could infiltrate your servers to steal customers’ valuable personal information; for example, they could steal credit card numbers to commit identity thefts at a later date.
Exploitation. Cybercriminals could also take your data or access your servers and hold them hostage, demanding payment with consequences of non-payment being restricted access or leaks to your competitors. Ransomware is a popular example of this.
Destruction. Don’t forget you could also lose your data to more tangible, destructive means. For example, if a fire breaks out in your office, would your physically stored data be destroyed along with everything else?


Preventative Measures to Take

How can you prevent these catastrophes? These six steps are a good ...


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Early Christmas: The New #SQLDev Data Modeler is Here!

Early Christmas: The New #SQLDev Data Modeler is Here!

The newest version of Oracle (#SQLDev) SQL Developer Data Modeler (SDDM) is ready for download!
6 Incredible Ways Small Businesses Can Use Big Data

6 Incredible Ways Small Businesses Can Use Big Data

Can big data provide similar opportunities for small businesses and retail traders the way it does for large corporations? This remains one of the most commonly asked questions amongst many venturing entrepreneurs. For decades, big companies like Google and Facebook have benefited from big data; and they still are! The fact that the giant companies have more self-generated data doesn’t mean big data harvesting is out of small businesses’ reach. Interestingly, big data is even more suited to smaller businesses because it is easier for them to act on data driven insights. So how best can small enterprises make use of big data and analytics for their own benefit?

1. Identifying Essential Trends

Being able to monitor particular behaviours and patterns makes it possible to predict future outcomes. What’s the likely change in demand for your services of products in the next few days, weeks, or even years? What factors are likely to influence or trigger such changes? With analysis of big data, getting answers to such questions become easy. Previously, trend analysis and predictions were out of instincts but not anymore. Using trending topics from various sources like Facebook and Twitter, service providers can easily have an idea of what ...


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Reflections on Data Natives conference, October 2016

Reflections on Data Natives conference, October 2016

A conference for the data-driven generation!

It’s late October 2016, an incredible crowd of young data-driven peeps are on their way to Berlin, looking forward to meet many other peeps with the same attitude at the Data Natives conference: Doing business with data or seeing a huge value in using data for the future. Besides the crowd I was not only impressed by the location but also by the amount of startups at the conference.

The schedule for two days was full packed with talks and it wasn’t easy to choose between all these interesting topics. So I decided not to give myself too much pressure. Instead I cruised  through the program, and stumbled on some highlights.

Why Your Business Should Be Using Qualitative Research

Why Your Business Should Be Using Qualitative Research

A person’s digital behavior can now be tracked better than ever before. From the websites and social media platforms they visit to the mediums they use to access that information, cutting-edge software has been developed to provide companies with virtually endless data about the consumers of today.

But while this data may seem like a gold mine, numbers are just one piece of the puzzle when it comes to getting a holistic view of who your consumers are. Truly understanding the customer involves going beyond the data to determine the “why” behind their actions. As you’ll discover here, there are huge business benefits for companies that can accurately craft a more well-rounded picture of their consumers.

Why Qualitative Research?

Research typically consists of two methods of data collection: quantitative and qualitative. As the name infers, quantitative is all about numbers — examining consumer behavior and distilling that information into a figure that can be cited with great precision; “90% of customers think this” or “one-quarter of consumers use that.” This information is excellent for the collection of broad census data but is less effective at providing a picture of that consumer’s habits outside of whatever metric you happen to be measuring.

That’s where qualitative ...


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DrivenBI Helps Companies Drive Analytics to the Next Level

DrivenBI Helps Companies Drive Analytics to the Next Level



Privately held company DrivenBI was formed in 2006 by a group of seasoned experts and investors in the business intelligence (BI) market in Taiwan and the United States. Currently based in Pasadena, California, the company has been steadily growing in the ten years since, gaining more than 400 customers in both the English and Chinese markets.

Led by founder and CEO Ben Tai (previously VP of global services with the former BusinessObjects, now part of SAP), DrivenBI would be considered part of what I call a new generation of BI and analytics solutions that is changing the analytics market panorama, especially in the realm of cloud computing.

A couple of weeks ago, I had the opportunity to speak with DrivenBI’s team and to have a briefing and demonstration, most of it in regards to their current analytics offerings and the company’s business strategy and industry perspective, all of which I will share with you here.

How DrivenBI Drives BI
DrivenBI’s portfolio is anchored by SRK, DrivenBI’s native cloud self-service BI platform and collaboration hub.

SRK provides a foundation for sourcing and collecting data in real time within a collaborative environment. Being a cloud platform, SRK can combine the benefits of a reduced IT footprint with a wide range of capabilities for efficient data management.

The SRK native cloud-centralized self-service BI solution offers many features, including:
  • the ability to blend and work with structured and unstructured data using industry-standard data formats and protocols;
  • a centralized control architecture providing security and data consistency across the platform;
  • a set of collaboration features to encourage team communication and speed decision making; and agile reporting and a well-established data processing logic.
SRK’s collaborative environment featuring data and information sharing between users within a centralized setting allows users to maintain control over every aspect and step of the BI and analytics process (figure 1).

Figure 1. DrivenBI’s SRK self-driven and collaborative platform (courtesy of DrivenBI)
DrivenBI: Driving Value throughout Industries, Lines of Business, and Business Roles

One important aspect of the philosophy embraced by DrivenBI has to do with its design approach, providing, within the same platform, valuable services across the multiple functional areas of an organization, including lines of business such as finance and marketing, inventory control, and resource management, as well as across industries such as fashion, gaming, e-commerce, and insurance.

Another element that makes DrivenBI an appealing offering is its strategic partnerships, specifically with Microsoft Azure and Salesforce.com. DrivenBI has the ability to integrate with both powerhouse cloud offerings.

I had the opportunity to play around a bit with DrivenBI’s platform, and I was impressed with the ease of use and intuitive experience in all stages of the data analytics process, especially for dynamic reporting and dashboard creation (figure 2).

Figure 2. DrivenBI’s SRK dashboard (courtesy of DrivenBI)
Other relevant benefits of the DrivenBI platform that I observed include:
  • elimination/automation of some heavy manual processes;
  • analysis and collaboration capabilities, particularly relevant for companies with organizational and geographically distributed operations, such as widespread locations, plants, and global customers;
  • support for multiple system data sources, including structured operational data, unstructured social media sources, and others.
As showcased in its business-centered approach and design, DrivenBI is one of a new generation of BI and analytics offerings that enable a reduced need for IT intervention in comparison to peer solutions like Domo, Tableau, and GoodData. These new-generation solutions are offered through cloud delivery, a method that seems to suit analytics and BI offerings and their holistic take on data collection well. By replacing expensive IT-centric BI tools, the DrivenBI cloud platform is useful for replacing or minimizing the use of complex spreadsheets and difficult analytics processes.

DrivenBI’s Agile Analytics
My experience with DrivenBI was far more than “interesting.” DrivenBI is a BI software solution that is well designed and built, intuitive, and offers a fast learning curve. Its well-made architecture makes the solution easy to use and versatile. Its approach—no spreadsheets, no programming, no data warehouse—is well-suited to those organizations that truly need agile analytics solutions. Still, I wonder how this approach fits with large BI deployments that require robust data services, especially in the realms of merging traditional analytics with big data and Internet of Things (IoT) strategies.

To sample what DrivenBI has to offer, I recommend checking out its SRK demo:



(Originally published on TEC's Blog)

How AI is Revolutionizing Business Models

How AI is Revolutionizing Business Models

AI is introducing radical innovation even in the way we think about business, and the aim of this section is indeed to categorize different AI companies and business models.

It is possible to look at the AI sector as really similar in terms of business models to the biopharma industry: expensive and long R&D; long investment cycle; low-probability enormous returns; concentration of funding toward specific phases of development. There are anyway two differences between those two fields: the experimentation phase, that is much faster and painless for AI, and the (absent) patenting period, which forces AI to continuously evolve and to use alternative revenue models (e.g., freemium model).

I. The DeepMind Strategy and the Open Source Model

If we look from the incumbents’ side, we might notice two different nuances in their business models evolution. First, the growth model is changing. Instead of competing with emerging startups, the biggest incumbents are pursuing an aggressive acquisition strategy.

I named this new expansion strategy the “DeepMind strategy” because it has become extremely common after the acquisition of DeepMind operated by Google.

The companies are purchased when they are still early stage, in their first 1–3 years of life, where the focus is more on people and pure technological advancements rather than revenues (AI is the ...


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Free eBook: 2015’s Top 8 IT Certifications | Simplilearn

Free eBook: 2015’s Top 8 IT Certifications | Simplilearn

An IT certification can add immense value to your resume: 86% of hiring managers believe that it is a high priority while evaluating a candidate! But which ones to choose? To make life easier, we’ve prepared a list of the highest-paying IT certifications across the world –get your copy today! In this e-Book, you will find – 1. W...Read More.
Smart Homes: Are the Security Risks Worth It?

Smart Homes: Are the Security Risks Worth It?

How cool would it be to click off your lights at home—during the day, while you’re at work? Or set your temperature controls to kick on the heat an hour or two before you head home? For smart home owners, these kinds of conveniences are an everyday reality that has lots of perks. However, these early adopters are still working with technology that’s in its infancy, which means that manufacturers are still working out a lot of the early kinks that come with the territory. Unfortunately, some of the biggest problems with smart homes so far have been related to security. Potential smart home owners have some legitimate concerns over the cyber vulnerabilities involved with getting in on the smart home trend. Before smart homes can go mainstream, many consumers are going to ask the question: are these homes worth the security risks? 

Ever-Evolving Features 

One one hand, smart home technology is very appealing—and will only become more appealing with time as the technology develops. Current smart home technology mostly supports fairly simple tasks, like controlling the lights or the television, but as technology integrates new artificial intelligence features, smart homes will be able to make our lives easier in myriad ways. ...


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Veikkaus digitally transforms as it emerges as new combined Finnish national gaming company

Veikkaus digitally transforms as it emerges as new combined Finnish national gaming company

The next BriefingsDirect Voice of the Customer digital transformation case study highlights how newly combined Finnish national gaming company, Veikkaus, is managing a complex merger process while also bringing more of a digital advantage to both its operations and business model. We'll now explore how Veikkaus uses a power big-data analytics platform to respond rapidly to the challenges of digitization.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
 
To learn how a culture of IT innovation is helping to establish a single wholly nationally-owned company to operate gaming and gambling in Finland, we're joined by Harri Räsänen, Information and Communications Technology Architect at Veikkaus in Helsinki. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Why has Veikkaus reinvented its data infrastructure technology?

Räsänen: Our data warehouse solution was a traditional data warehouse, and had been around for 10 years. Different things had gone wrong. One of the key issues we faced was that our data wasn’t real-time. It was far from real time -- it was data that was one or two days old.

We decided that we need to get data quicker and in more detail because we now had aggregate data.

Gardner: What were some of your top requirements technically in order to accomplish that?

Real-time data

Räsänen: As I said, we had quite a old-fashioned data warehouse. Initially, we needed our game-service provider to feed us data more in real-time. They needed to build up a mechanism to complete data, and we needed to build out capabilities to gather it. We needed to rethink the information structure -- totally from scratch.

Räsänen
Gardner: When we think about gambling, gaming, or lotteries, in many cases, this is an awful lot of data, a very big undertaking. Give us a sense of the size of the data and the disparity of the three organizations that came together including the Finnish national football gaming reorganization.

Räsänen: I'll talk about our current-situation records, for the new combined company we are starting up in 2017.

We have a big company from a customer point of view. We have 1.8 million consumers. Finland has a population of 5.5 million. So, we have a quite a lot of Finnish consumers. When it comes to transactions, we get one to three million transactions per day. So it’s quite large, if you think about the transactional data.

In addition to that, we gather different kinds of information about our web store; it’s one of the biggest retail web stores in Finland.

Gardner: It’s one thing to put in a new platform, but it’s another to then change the culture and the organization -- and transform into a digital business. How is the implementation of your new data environment aiding in your cultural shift?

Räsänen: Luckily, Veikkaus has a background of doing things quite analytically. If you think about a web store, there is a culture that we need to be able to monitor what we're doing if we're running some changes in our web store -- whether it works or not. That’s a good thing.
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But, we are redoing our whole data technology. We added the Apache Kafka integration point and then, Cloudera, the Hadoop system. Then, we added a new ETL tool for us, Pentaho, and last but not least, HPE Vertica. It's been really challenging for us, with lots of different things to consider and learn.

Luckily, we've been able to use good external consultants to help us out, but as you said, we can always make the technology work better. In transforming the culture of doing things, we're still definitely in the middle of our journey.

Gardner: I imagine you'll want to better analyze what takes place within your organization so it’s not just serving the data and managing the transactions. There's an opportunity to have a secondary benefit, which is more control of your data. The more insight you have allows you to adapt and improve your customer experience and customer service. Have you been able to start down that path of that secondary analysis of what goes on internally?

New level of data

Räsänen: Some of our key data was even out of our hands in our service-provider environments. We wanted to get all the relevant data with us, and now we've been working on that new level of data access. We have analysts working on that, both IT and business people, browsing the data. They already have some findings on things that previously they could have asked or even thought about. So, we have been getting our information up-to-date.

Gardner: Can you give us more specific examples of how you've been able to benefit from this new digital environment?

Räsänen: Yeah, consumer communication on CRM is one of the key successes, things we needed to have in place. We've been able to constantly improve on that. Before, we had data that was too old, but now, we have near real-time data. We get one-minute-old data, so we can communicate with the consumers better. We know whether they've been playing their lotteries or betting on football matches.

We can say, "It’s time for football today, and you haven’t yet placed a bet." We can communicate, and on the other hand, we can avoid disturbing customers by sending out e-mails or SMS messages about things they've already done.
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Gardner: Yes, less spam, but more help. It’s important, of course, with any organization like this in a government environment, for trust and safety to be involved. I should think that there's some analysis to help keep people from overdoing it and managing the gaming for a positive outcome.

Räsänen: Definitely. That’s one of the key metrics we're measuring with our consumer so that gaming is responsible. We need to see that all things they do can be thought of as good, because as you said, we're a national company, it’s a very regulated market, and that kind of thing.

Gardner: But a great deal of good comes from this. I understand that more than 1 billion euros a year go to the common good of people living in Finland. So, there are a lot of benefits when this is done properly.

Now that you've gone quite a ways into this, and you're going to need to be going to the new form and new organization the first of 2017, what advice would you be able to give to someone who is beginning a big data consolidation and modernization journey? What lessons have you learned that you might share?

Out of the box

Räsänen: If you're experimenting, you need to start to think a little bit out of the box. Integration is one of crucial part, and avoid all direct integration as much as possible.

We're utilizing Apache Kafka as an integration point, and that’s one of the crucial things, because then you can "appify" everything. You're going to provide an application interface for integrating systems and that will help those of us in gaming companies.

Gardner: A lot a services-orientation?

Räsänen: That’s one of the components of our data architecture. We have been using our Cloudera Hadoop system for archiving and we are building our capabilities on top of that. In addition, of course, we have HPE Vertica. It’s one of our most crucial things in our data ecosystem because it’s a traditional enterprise data warehousing in that sense it is a SQL database. Users can take a benefit out of that, and it’s lightning-fast. You need to design all the components and make those work on that role that they are based at.
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Gardner: And of course SQL is very commonly understood as the query language. There's no great change there, but it's really putting it into the hands of more people.

Räsänen: I've been writing or talking in SQL since the beginning of the ’90s, and it’s actually a pretty easy language to communicate, even between business and IT, because at least, at some level, it’s self-explanatory. That’s where the communication matters.

Gardner: Just a much better engine under the hood, right?

Räsänen: Yeah, exactly.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or  
download a copy. Sponsor: Hewlett Packard Enterprise.

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Good Bye 2016

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I did not see that Millennials accelerated the Internet of Things as IDC predicted.
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The next BriefingsDirect Voice of the Customer digital transformation case study highlights how World Wide Technology, known as WWT, in St. Louis, found itself with a very serious yet somehow very common problem -- users simply couldn’t find relevant company content.

We'll explore how WWT reached deep into its applications, data, and content to rapidly and efficiently create a powerful Google-like, pan-enterprise search capability. Not only does it search better and empower users, the powerful internal index sets the stage for expanded capabilities using advanced analytics to engender a more productive and proactive digital business culture.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.

Here to describe how WWT took an enterprise Tower of Babel and delivered cross-applications intelligent search are James Nippert, Enterprise Search Project Manager, and Susan Crincoli, Manager of Enterprise Content, both at World Wide Technology in St. Louis. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: It seems pretty evident that the better search you have in an organization, the better people are going to find what they need as they need it. What holds companies back from delivering results like people are used to getting on the web?

Nippert
Nippert:  It’s the way things have always been. You just had to drill down from the top level. You go to your Exchange, your email, and start there. Did you save a file here? "No, I think I saved it on my SharePoint site," and so you try to find it there, or maybe it was in a file directory.

Those are the steps that people have been used to because it’s how they've been doing it their entire lives, and it's the nature of beast as we bring more and more enterprise applications into the fold. You have enterprises with 100 or 200 applications, and each of those has its own unique data silos. So, users have to try to juggle all of these different content sources where stuff could be saved. They're just used to having to dig through each one of those to try to find whatever they’re looking for.

Gardner: And we’ve all become accustomed to instant gratification. If we want something, we want it right away. So, if you have to tag something, or you have to jump through some hoops, it doesn’t seem to be part of what people want. Susan, are there any other behavioral parts of this?

Find the world

Crincoli: We, as consumers, are getting used to the Google-like searching. We want to go to one place and find the world. In the information age, we want to go to one place and be able to find whatever it is we’re looking for. That easily transfers into business problems. As we store data in myriad different places, the business user also wants the same kind of an interface.

Crincoli
Gardner: Certain tools that can only look at a certain format or can only deal with certain tags or taxonomy are strong, but we want to be comprehensive. We don’t want to leave any potentially powerful crumbs out there not brought to bear on a problem. What’s been the challenge when it comes to getting at all the data, structured, unstructured, in various formats?

Nippert: Traditional search tools are built off of document metadata. It’s those tags that go along with records, whether it’s the user who uploaded it, the title, or the date it was uploaded. Companies have tried for a long time to get users to tag with additional metadata that will make documents easier to search for. Maybe it’s by department, so you can look for everything in the HR Department.

At the same time, users don’t want to spend half an hour tagging a document; they just want to load it and move on with their day. Take pictures, for example. Most enterprises have hundreds of thousands of pictures that are stored, but they’re all named whatever number the camera gave, and they will name it DC0001. If you have 1,000 pictures named that you can't have a successful search, because no search engine will be able to tell just by that title -- and nothing else -- what they want to find.

Gardner: So, we have a situation where the need is large and the paybacks could be large, but the task and the challenge are daunting. Tell us about your journey. What did you do in order to find a solution?

Nippert: We originally recognized a problem with our on-premises Microsoft SharePoint environment. We were using an older version of SharePoint that was running mostly on metadata, and our users weren’t uploading any metadata along with their internet content.
Your average employee can spend over an entire work week per year searching for information or documentation that they need to get their job done.

We originally set out to solve that issue, but then, as we began interviewing business users, we understood very quickly that this is an enterprise-scale problem. Scaling out even further, we found out it’s been reported that as much as 10 percent of staffing costs can be lost directly to employees not being able to find what they're looking for. Your average employee can spend over an entire work week per year searching for information or documentation that they need to get their job done.

So it’s a very real problem. WWT noticed it over the last couple of years, but as there is the velocity in volume of data increase, it’s only going to become more apparent. With that in mind, we set out to start an RFI process for all the enterprise search leaders. We used the Gartner Magic Quadrants and started talks with all of the Magic Quadrant leaders. Then, through a down-selection process, we eventually landed on HPE.

We have a wonderful strategic partnership with them. It wound up being that we went with the HPE IDOL tool, which has been one of the leaders in enterprise search, as well as big data analytics, for well over a decade now, because it has very extensible platform, something that you can really scale out and customize and build on top of. It doesn’t just do one thing.
Humanizes Machine Learning
For Big Data Success
Gardner: And it’s one solution to let people find what they're looking for, but when you're comprehensive and you can get all kinds of data in all sorts of apps, silos and nooks and crannies, you can deliver results that the searching party didn’t even know was there. The results can be perhaps more powerful than they were originally expecting.

Susan, any thoughts about a culture, a digital transformation benefit, when you can provide that democratization of search capability, but maybe extended into almost analytics or some larger big-data type of benefit?

Multiple departments

Crincoli: We're working across multiple departments and we have a lot of different internal customers that we need to serve. We have a sales team, business development practices, and professional services. We have all these different departments that are searching for different things to help them satisfy our customers’ needs.

With HPE being a partner, where their customers are our customers, we have this great relationship with them. It helps us to see the value across all the different things that we can bring to bear to get all this data, and then, as we move forward, what we help people build more relevant results.

If something is searched for one time, versus 100 times, then that’s going to bubble up to the top. That means that we're getting the best information to the right people in the right amount of time. I'm looking forward to extending this platform and to looking at analytics and into other platforms.
That means that we're getting the best information to the right people in the right amount of time.

Gardner: That’s why they call it "intelligent search." It learns as you go.

Nippert: The concept behind intelligent search is really two-fold. It first focuses on business empowerment, which is letting your users find whatever it is specifically that they're looking for, but then, when you talk about business enablement, it’s also giving users the intelligent conceptual search experience to find information that they didn’t even know they should be looking for.

If I'm a sales representative and I'm searching for company "X," I need to find any of the Salesforce data on that, but maybe I also need to find the account manager, maybe I need to find professional services’ engineers who have worked on that, or maybe I'm looking for documentation on a past project. As Susan said, that Google-like experience is bringing that all under one roof for someone, so they don’t have to go around to all these different places; it's presented right to them.

Gardner: Tell us about World Wide Technology, so we understand why having this capability is going to be beneficial to your large, complex organization?
Humanizes Machine Learning
For Big Data Success
Crincoli: We're a $7-billion organization and we have strategic partnerships with Cisco, HPE, EMC, and NetApp, etc. We have a lot of solutions that we bring to market. We're a solution integrator and we're also a reseller. So, when you're an account manager and you're looking across all of the various solutions that we can provide to solve the customer’s problems, you need to be able to find all of the relevant information.

You probably need to find people as well. Not only do I need to find how we can solve this customer’s problem, but also who has helped us to solve this customer’s problem before. So, let me find the right person, the right pre-sales engineer or the right post-sales engineer. Or maybe there's somebody in professional services. Maybe I want the person who implemented it the last time. All these different people, as well as solutions that we can bring in help give that sales team the information they need right at their fingertips.

It’s very powerful for us to think about the struggles that a sales manager might have, because we have so many different ways that we can help our customer solve those problems. We're giving them that data at their fingertips, whether that’s from Salesforce, all the way through to SharePoint or something in an email that they can’t find from last year. They know they have talked to somebody about this before, or they want to know who helped me. Pulling all of that information together is so powerful.

We don’t want them to waste their time when they're sitting in front of a customer trying to remember what it was that they wanted to talk about.

Gardner: It really amounts to customer service benefits in a big way, but I'm also thinking this is a great example of how, when you architect and deploy and integrate properly on the core, on the back end, that you can get great benefits delivered to the edge. What is the interface that people tend to use? Is there anything we can discuss about ease of use in terms of that front-end query?

Simple and intelligent

Nippert: As far as ease of use goes, it’s simplicity. If you're a sales rep or an engineer in the field, you need to be able to pull something up quickly. You don’t want to have to go through layers and layers of filtering and drilling down to find what you're looking for. It needs to be intelligent enough that, even if you can’t remember the name of a document or the title of a document, you ought to be able to search for a string of text inside the document and it still comes back to the top. That’s part of the intelligent search; that’s one of the features of HPE IDOL.

Whenever you're talking about front-end, it should be something light and something fast. Again, it’s synonymous with what users are used to on the consumer edge, which is Google. There are very few search platforms out there that can do it better. Look at the  Google home page. It’s a search bar and two buttons; that’s all it is. When users are used to that at home and they come to work, they don’t want a cluttered, clumsy, heavy interface. They just need to be able to find what they're looking for as quickly and simply as possible. 

Gardner: Do you have any examples where you can qualify or quantify the benefit of this technology and this approach that will illustrate why it’s important?
It’s gotten better at finding everything from documents to records to web pages across the board; it’s improving on all of those.

Nippert: We actually did a couple surveys, pre- and post-implementation. As I had mentioned earlier, it was very well known that our search demands weren't being met. The feedback that we heard over and over again was "search sucks." People would say that all the time. So, we tried to get a little more quantification around that with some surveys before and after the implementation of IDOL search for the enterprise. We got a couple of really great numbers out of it. We saw that people’s satisfaction with search went up by about 30 percent with overall satisfaction. Before, it was right in the middle, half of them were happy, half of them weren’t.

Now, we're well over 80 percent that have overall satisfaction with search. It’s gotten better at finding everything from documents to records to web pages across the board; it’s improving on all of those. As far as the specifics go, the thing we really cared about going into this was, "Can I find it on the first page?" How often do you ever go to the second page of search results.

With our pre-surveys, we found that under five percent of people were finding it on the first page. They had to go to second or third page or four through 10. Most of the users just gave up if it wasn’t on the first page. Now, over 50 percent of users are able to find what they're looking for on the very first page, and if not, then definitely the second or third page.

We've gone from a completely unsuccessful search experience to a valid successful search experience that we can continue to enhance on.

Crincoli: I agree with James. When I came to the company, I felt that way, too -- search sucks. I couldn’t find what I was looking for. What’s really cool with what we've been able to do is also review what people are searching for. Then, as we go back and look at those analytics, we can make those the best bets.

If we see hundreds of people are searching for the same thing or through different contexts, then we can make those the best bets. They're at the top and you can separate those things out. These are things like the handbook or PTO request forms that people are always searching for.

Gardner: I'm going to just imagine that if I were in the healthcare, pharma, or financial sectors, I'd want to give my employees this capability, but I'd also be concerned about proprietary information and protection of data assets. Maybe you're not doing this, but wonder what you know about allowing for the best of search, but also with protection, warnings, and some sort of governance and oversight. 

Governance suite

Nippert: There is a full governance suite built in and it comes through a couple of different features. One of the main ones is induction, where as IDOL scans through every single line of a document or a PowerPoint slide of a spreadsheet whatever it is, it can recognize credit card numbers, Social Security numbers anything that’s personally identifiable information (PII) and either pull that out, delete it, send alerts, whatever.

You have that full governance suite built in to anything that you've indexed. It also has a mapped security engine built in called Omni Group, so it can map the security of any content source. For example, in SharePoint, if you have access to a file and I don’t and if we each ran a search, you would see a comeback in the results and I wouldn’t. So, it can honor any content’s security.  

Gardner: Your policies and your rules are what’s implemented, and that’s how it goes?

Nippert: Exactly. It is up to as the search team or working with your compliance or governance team to make sure that that does happen.

Gardner: As we think about the future and the availability for other datasets to be perhaps brought in, that search is a great tool for access to more than just corporate data, enterprise data and content, but maybe also the front-end for some advanced querying analytics, business intelligence (BI), has there been any talk about how to take what you are doing in enterprise search and munge that, for lack of a better word, with analytics BI and some of the other big data capabilities.
It is going to be something that we can continue to build on top of, as well and come up with our own unique analytic solutions.

Nippert: Absolutely. So HPE has just recently released BI for Human Intelligence (BIFHI), which is their new front end for IDOL and that has a ton of analytics capabilities built into it that really excited to start looking at a lot of rich text, rich media analytics that can pull the words right off the transcript of an MP4 raw video and transcribe it at the same time. But more than that, it is going to be something that we can continue to build on top of, as well and come up with our own unique analytic solutions.

Gardner: So talk about empowering your employees. Everybody can become a data scientist eventually, right, Susan?

Crincoli: That’s right. If you think about all of the various contexts, we started out with just a few sources, but we also have some excitement because we built custom applications, both for our customers and for our internal work. We're taking that to the next level with building an API and pulling that data into the enterprise search that just makes it even more extensible to our enterprise.

Gardner: I suppose the next step might be the natural language audio request where you would talk to your PC, your handheld device, and say, "World Wide Technology feed me this," and it will come back, right?

Nippert: Absolutely. You won’t even have to lift a finger anymore.

Cool things

Crincoli: It would be interesting to loop in what they are doing with Cortana at Microsoft and some of the machine learning and some of the different analytics behind Cortana. I'd love to see how we could loop that together. But those are all really cool things that we would love to explore.

Gardner: But you can’t get there until you solve the initial blocking and tackling around content and unstructured data synthesized into a usable format and capability.
Humanizes Machine Learning
For Big Data Success
Nippert: Absolutely. The flip side of controlling your data sources, as we're learning, is that there are a lot of important data sources out there that aren’t good candidates for enterprise search whatsoever. When you look at a couple of terabytes or petabytes of MongoDB data that’s completely unstructured and it’s just binaries, that’s enterprise data, but it’s not something that anyone is looking for.

So even though our original knee-jerk is to index everything, get everything to search, you want to able to search across everything. But you also have to take it with a grain of salt. A new content source could be hundreds or thousands of results that could potentially clutter the accuracy of results. Sometimes, it’s actually knowing when not to search something.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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5 Reasons to Invest in Technology and Data Protection for your Phone

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As our lives progressively become more entangled in the digital realm, our smart phones and tablets are becoming increasingly more indispensable. For some, they’re nothing short of a lifeline.

The Internet of Things has made our devices our connection to everything. With the push of a button, we can contact anyone from almost anywhere. In a few clicks, we can be searching the Internet. Download an app and you can keep an eye on your home from anywhere in the world. Scan your phone at a register and you can pay for groceries.

These devices are essential parts of our everyday lives. As their importance has grown so has the need for enhanced protection. If you think you can get by with the bare minimum the five points below may convince you otherwise.

Devices Are Expensive

If you got a smart phone for free through a special deal, then you may not realize how expensive the device actually is. An iPhone is going to cost at least $649 new. iPhone cases are a small expense compared to fixing a cracked screen or having to replace a phone altogether.

But damage isn’t the only concern. The high price tag makes your mobile device a prime target ...


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Six Cloud Based Tools to Develop Mobile Apps

Six Cloud Based Tools to Develop Mobile Apps

All thanks to the blooming world of technology you find myriad app building platforms out there best for creating and developing apps on your own. Going by the present rage, it has become mandatory to create applications for both the web and mobile platforms. Now what’s do you need to build the best apps? Well, the right tools, of course? But just as you look in the market, you come across a plethora of tools, thus making it tricky to decide on the most appropriate.

Here, through this platform, we are introducing the best cloud-based tools helpful to the developers in the process of building amazing apps. These app development platforms will enable you to create a masterpiece even when you don’t know coding. Let’s explore them all:

Knack

In the present times most companies look for Big Data solutions; however Knack, being an interesting platform, has introduced an apt solution with a cross between Filemaker Pro and Caspio for little data. Thus, it enables the users to create their online databases and build simple web apps so that it’s easier to develop apps that go along with your data. This platform allows functionality like search, custom forms and data display. It is ...


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How Blockchain Can Help Combat Climate Change

How Blockchain Can Help Combat Climate Change

Blockchain technology will enable us to decentralise the web and decentralise many, if not all, the services that are offered via the web. A decentralised internet was how the internet was originally envisioned, but somehow in the past 25 years, it ended up in the hands of a few very powerful companies. As Sir Tim Berners-Lee said during the Decentralised Web Summit in 2016:

“The web was designed to be decentralised so that everybody could participate by having their own domain and having their own webserver and this hasn’t worked out. Instead, we’ve got the situation where individual personal data has been locked up in these silos.”

Fortunately, Blockchain will allow us to bring back the power to the users and to create a decentralised society. Already, Blockchain challenges many industries, of which the financial industry will see the largest impact of Blockchain in the coming years. The question, however, is how Blockchain will have an impact on other global problems. In a new series of Blockchain posts, I will dive into different problems we face and see if and how the Blockchain could help solve these issues. I will discuss problems such as climate change, poverty, refugee crisis, voting fraud, censorship ...


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7 Trends of the Internet of Things in 2017

7 Trends of the Internet of Things in 2017

IoT is one of the transformational trends that will shape the future of businesses in 2017 and beyond. Many firms see big opportunity in IoT uses and enterprises start to believe that IoT holds the promise to enhance customer relationships and drive business growth by improving quality, productivity, and reliability on one side, and on the other side reducing costs, risk, and theft. By having the right IoT model companies will be rewarded with new customers, better insights, and improved customer satisfaction to mention few benefits. 

With all this in mind, let’s explore some of the trends of IoT impacting business and technology in 2017:

1) IoT and Blockchain Will Converge

Blockchain is more than a concept now and has applications in many verticals besides FinTech including IoT. Blockchain technology is considered by many experts as the missing link to settle scalability, privacy, and reliability concerns in the Internet of Things. Blockchain technology can be used in tracking billions of connected devices, enable the processing of transactions and coordination between devices; allow for significant savings to IoT industry manufacturers. This decentralized approach would eliminate single points of failure, creating a more resilient ecosystem for devices to run on. The cryptographic algorithms used by ...


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Big Data Highlights Broad Human Behavior Patterns in New Study

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Researchers from the ThinkBIG project at the University of Bristol have concluded that, by looking at patterns in huge data sets, including newspaper content, individual Twitter feeds and Wikipedia habits, it's possible to spot things about collective society that might not be detected otherwise.

It’s a remarkable revelation, made possible by the project’s leader, Nello Cristianini — a Professor of Artificial Intelligence — and other scientists. During the study, they used big data to study the digital habits of large numbers of people to draw conclusions about how people behave, and to find notable similarities across demographics.

Trends in Newspaper Content Across Time

Researchers carried out two different studies before asserting their recent findings. The first involved looking at newspapers from the United States and the United Kingdom, published between 1836 and 1922. They found that the ways in which people devoted themselves to either work or pleasurable activities was largely dependent on seasons and weather patterns. As you might imagine, the word "picnic" showed up particularly often in newspapers during the summertime.

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What You Are Too Afraid to Ask About Artificial Intelligence (Part III): Technologies

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As we explained before, the recent surge of AI and it is rapidly becoming a dominant discipline are partially due to the exponential degree of technological progress we faced over the last few years. What it is interesting to point out though is that AI is deeply influencing and shaping the course of technology as well.

First of all, the Graphics Processing Units (GPUs) have been adapted from traditional graphical user interface applications to alternative parallel computing operations. NVIDIA is leading this flow and is pioneering the market with the CUDA platform and the recent introduction of Telsa P100 platform (the first GPU designed for hyperscale data center applications). On top of P100, they also created the first full server appliance platform (named DGX-1), which will bring deep learning to an entirely new level. Very recently, they also released the Titan X, which is the biggest GPU ever built (3,584 CUDA cores).

In general, the most impressive developments we observed are related to chips, especially Neuromorphic Processing Units (NPUs) ideated to emulate the human brain. Specific AI-chips have been created by major incumbents: IBM has released in 2016 the TrueNorth chip, which it is claimed to work very similarly to a mammalian brain. The chip is made of 5.4 billion transistors, and ...


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The concept of running enterprise IT like a utility is far from new. Vendors were promoting it alone with the other “new economy” stuff of Internet 1.0. However, the concept was been slow to take off—partially because vendors really didn’t follow through with the pay as you go acquisition model, [...]
What Big Data and the Internet of Things Will Look Like in 2017

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As companies continue to adapt to changes in big data analytics and the Internet of Things (IoT), there are certain trends which will shape 2017 that companies looking to maximize their ROI and productivity need to monitor.

The IoT and big data analysis have a broad impact on businesses, affecting areas such as risk assessment, risk management, and business strategy. According to a Business Insider report, proper IoT implementation and usage could boost business profit by lowering operational cost, increasing productivity, and enabling market expansion and new product development. However, businesses face well-documented challenges, such as security, that currently hampers the proper implementation and utilization of the IoT and big data analytics.

Some of these challenges and opportunities will be better explored in 2017. Here are some improvements expected in 2017:

Enhanced IoT Security

The security risk is one of the key challenges that has hampered the wide adoption of the IoT despite its potential. The risk to the interconnected devices, the platform, the communication, and the operating system is still a serious threat for many businesses. Enhanced securities that can shield IoT devices from physical tampering and information attacks, encrypt communications, and prevent battery-draining denial of sleep attacks will be available is 2017.

The ...


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A PISA-felmérés kapcsán – adatelemzés feladat

A PISA-felmérés kapcsán – adatelemzés feladat

Pár pillanatra feltódult a magyar online médiában a PISA-felméréssel kapcsolatos hírek hada, mondván, hogy Magyarország leszerepelt, stb. Az Index ki is emelt pár feladatot, hogy az olvasók is tesztelhessék, mit is mér a PISA-felmérés, és hogy hogy teljesítenének rajta. Nade a poszt apropója nem is ez, hanem konkrétan az egyik feladat, ami alább olvasható, illetve az Index kiemelt feladatai között is szerepel:

14207170_29684942a585817e9508b9eefd70e1f5_wm.png

De mi is ezzel a probléma?!

Tegyük fel, hogy csak a csapadéktől függ a dolog. Mit mond nekünk ekkor a napsugárzás?! Ha nem függ tőle, akkor az égegyadtavilágon semmit... Ugyanez igaz fordítva is: ha csak a napsugárzástól függ, akkor sem mond semmit a csapadékmennyiség oszlop. A számokból tehát az alábbi két narratíva vezethető le, ebből kell választani:

  • Ha több a napsütés alacsonyabb lesz a talajnedvesség, és igazából nem függ a csapadékmennyiségtől.
  • Ha több az eső magasabb lesz a talajnedvesség, és igazából nem függ a napsugárzástól

A gond ott keresendő, hogy a fenti feltevések közül mindkettő lehetséges, sőt, (szerintem) leginkább egyszerre mindkettőtől függ a dolog, nem csak az egyiktől; innentől pedig a feladatra nem lehet helyesen válaszolni, mert egyik válasz sem igaz.

Ha a konstruktivitás jegyében feltesszük, hogy az fog kijönni, hogy a dolog az egyiktől egyértelműen jobban függ, mint a másiktól, akkor az alábbi példákat megvizsgálva juthatunk arra, hogy nem is annyira tud "egyértelmű" lenni ez a "jóság"-definíció.

Bonyolultabb összefüggések (kis csalással *) a talajnedvességre:

  1. Alapvetően 1.3%. Minden 100 mm csapadék növeli ezt 7% százalékkal, de 2GJ/m^2 átlagos napsugárzás felett minden további GJ/m^2 csökkenti 2.7 százalékkal.
    Képlettel: 1.4 + 7 * p/100 - max(0, r/1000 - 2) * 2.7
    • 2 GJ/m^2 naponta simán visszaverődik a növényekről, az alatt nem változtat, felette egyenletes a befolyás
    • A csapadékmennyiség egyenletesen befolyásol.
  2. Alapvetően 5%-os. Minden 100 mm csapadék ezt növeli 7.4%-kal, de minden GJ/m^2 napsugárzás pedig csökkenti 2.7 százalékkal.
    Képlettel: 5 + 7.4 * p/100 - r/1000 * 2.7
    • Ugyanaz mint az előző, csak nincs a 2GJ/m^2 korlát...

Melyikre lehet - egy középiskolás ismeretei alapján - azt mondani, hogy valószínűbb, mint a másik? Melyiktől függ? Melyiktől függ "jobban"?

* kis csalással: itt még negatív értékek is kijöhetnének talajnedvességre, ha a fenti képletet alkalmazzuk, de mivel nem vagyok kompetens a talajnedvesség reális értékei tekintetében, inkább nem finomhangoltam a dolgokat ilyen irányba; illetve azzal csak a képlet lenne bonyolultabb, a lényegen nem változtatna.

Mentségek - és miért nem :)

Ha a 440-es és 450-es számok úgy vannak szánva, mint "nem releváns különbség", akkor érhető, hogy erre a következtetésre jut a költő. Ámde miért kéne egy középiskolásnak azt tudni, hogy a százalékban mért talajnedvességet befolyásolja-e relevánsan 10mm csapadék?!

Igen, ki lehet találni, hogy a kérdező mire gondolt, de elvileg nem erről szól a feladatsor. Lehet hivatkozni "Occam borotvájára" is: többnyire a legegyszerűbb megoldás a helyes. De pont az a helyzet itt, hogy a kitöltőnek kritikus gondolkodással és ésszel kell állnia a feladatokhoz, éppen ez az egész felmérés legalapvetőbb elvárása.

Súlyosabb probléma

Ha ezt tényleg így csinálják a gyakorlatban. :D

Sokkal szembetűnőbb viszont az, hogy azt várnák a kitöltőtől, hogy lineáritást feltételezzen. Arra KELL gondoljon a sikeres válaszadáshoz, hogy "hú ez kétszer annyi majdnem, ez meg csak 2-3%-kal több". Pedig a valóságban tisztán lineáris kapcsolat nem nagyon van természetes dolgok között... és még csak - ha jól látom - nem is SI mértékegységek vannak a táblázatban...

Nézzük a jó oldalát!

Legalább nem hőmérséklet van a feladatban... példafeladat: melyik befolyásolja jobban a talajnedvességet: a hőmérséklet Celsius fokban vagy a hőmérséklet Kelvinben?

átlaghőmérséklet (°C) talajnedvesség (% átlaghőmérséklet (Kelvin) átlaghőmérséklet (Fahrenheit)
7 28 280.15 44.6
13.5 18 286.65 56.3

(Az arányok nem véletlenül egyeznek a feladat adataival. A talajnedvesség kapcsán viszont a valósággal való bármilyen egyezés a véletlen műve.)

Zárszó

Szóval alapvetően nem lenne baj azzal, ha feladatban ilyen következtetéseket kell levonni, de ha edukációs céllal várunk el megalapozatlan állításokat, attól rossz lesz a kedvem. Nem segít a szituáción (irányomban), ha ezt mindeközben "adatelemzés"-nek nevezzük. :)

Megjegyzés: természetesen gyanakodtam, hogy a fordítással lehet valami, de az OECD oldalán megnézve az angol verziót, rá kellett döbbenjek, hogy nem.

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Follow Up Data Vault EXASOL Webinar

Follow Up Data Vault EXASOL Webinar

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In July 2016 Mathias Brink and I had given a webinar [LINK] how to implement Data Vault on a EXASOL database. Read more about in my previous blogpost or watch the recording on Youtube.

Afterward I became a lot of questions per our webinar. I’ll now answer all questions I got till today. If you have further more questions feel free to ask via my contact page,via Twitter, or write a comment right here.

Goal Setting Guide: Retail Resolutions for 2017

Goal Setting Guide: Retail Resolutions for 2017

We set personal resolutions for the New Year, so why shouldn’t we do the same in our businesses? Retailers around the world are spending this time reflecting on the previous 12 months and setting the goals that will ensure 2017 is an even better year.

If you’re having trouble ruminating on your goals, take some inspiration from retailers around the world.

Boost Your Use of Data

Many retailers conduct market research when looking to open their business and then never use that information again. This year, one of the most solid resolutions you can make is to utilize customer data in your everyday operations.

The easiest way to collect data is through your point-of-sale terminal. Chances are you already prepare some sort of end-of-day, -week, and -month report. One of the biggest mistakes you can make is to file this information away and never look at it again. Those reports are a data mine and can help your business identify and capitalize upon many customer trends.

Information in these reports can include:



Which day of the week logs the most purchases so you can tailor promotions to match that day or target others that may be lagging;


The number of products customers are buying so you can create ...


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Machine Learning: Bridging the Gaps in IT Data Silos

Machine Learning: Bridging the Gaps in IT Data Silos

In today’s complex business world – where many organizations operate in silos, data is plentiful and it’s challenging to get a big-picture view of the entire IT landscape – how can enterprises better manage, analyze and interpret tremendous amounts of data?

The next big thing in ITOA – machine learning – is providing a viable solution. Machine learning studies how to design algorithms that can learn by observing data, discovering new insights in data, developing systems that can automatically adapt and customize themselves, and designing systems where it’s too complicated and costly to implement all possible circumstances, such as search engines and self-driving cars.

There’s been a significant increase in machine learning applications in ITOA due, in large part, to the ongoing growth of machine learning theory, algorithms, and computational resources on demand. Many organizations are finding that machine learning allows them to better analyze large amounts of data, gain valuable insights, reduce incident investigation time, determine which alerts are correlated and what causes event storms – and even prevent incidents from happening in the first place.

In fact, machine learning can help cure a variety of IT pains with the following technologies:

Clustering

Imagine a large, global corporation, with tens of thousands of servers ...


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When Big Data Can’t Predict

When Big Data Can’t Predict

Most people think that in the age of big data, we always have more than enough information to build robust analytics. Unfortunately, this isn’t always the case. In fact, there are situations where even massive amounts of data still don’t enable even basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to the basics instead of getting fancy. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but let’s explore why it isn’t.

Scenario 1: Big Data, Small Universe

One example where issues arise is when we have a ton of data on a very small population. This makes it tough to find meaningful patterns. Let’s think about an airline manufacturer. Today’s airplanes generate terabytes of data every hour of operation. There are a lot of benefits that can come out of analyzing that data in terms of understanding things like how the engines are operating under differing conditions. However, at the same time, some exciting analytics like predictive maintenance can be difficult. Why is that?

Realize that even the biggest aircraft manufacturers only put out a few ...


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The Future is Cloud Database: What every Oracle Remote DBA Expert Should Know

The Future is Cloud Database: What every Oracle Remote DBA Expert Should Know

With each growing day, the cloud is getting more complicated and crowded. The number of new ‘as a service’ packages being introduced is overwhelming. You have probably heard of Data as a Service (DaaS) and Database as a Service (DBaaS). Well, the latter is set to revolutionise the entire IT industry. As expected, Larry Elson, the CTO of Oracle had already seen this coming and made predictions in the process. ‘Database is our largest software business and database will be our largest cloud businesses’, he said. What does this mean to Oracle remote DBA experts? What does the future hold for this group of database professionals? It might as well be the ideal time to know.

More Businesses

DBaaS comes with the possibility of the entire database being visible online. With this, the user will be able to access a schema or the complete dedicated database depending on his preference. For companies, this is a new opportunity of amassing more data in a faster and convenient way. With most of the processes being automated, there would be little need for full time database administrators to handle the same. That is where remote DBAs handling Oracle get in. With cloud computing, there would ...


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Seven HTML5 Web Applications to Help You Study IoT data

Seven HTML5 Web Applications to Help You Study IoT data

As technology continues influencing how we live, IoT (Internet of Things) is becoming more of a handy and real concept. It is not just that our refrigerators, door locks, alarms, coffee machines and air cons are getting smarter and intelligent but IoT has also taken over everything from street lighting, field sensors, and security systems to health monitoring and more. It is not easy to ignore how data science and smarter robotics is becoming all pervasive. However, the increasing influx of these data systems is also bringing on new challenges in the enterprise environment. Modern businesses are now more frequently indulged in data aggregation, visualization and interaction with a variety of information sources. Luckily, there also exist programmable APIs that offer a front-end dashboard to interact with this data. A common feature of all these configurations we work in is a web browser that helps us to deploy and interact with web applications.

One of the most logical choices in this case for enterprises is creating a web application using HTML5 and JavaScript. Currently, the most widely accepted and adopted technology, HTML5 offers a framework that is used by web developers to create powerful and flexible user interface. Further, these RESTful ...


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6 Things Keeping Businesses From Fully Embracing the Age of Data

6 Things Keeping Businesses From Fully Embracing the Age of Data

We’re in the midst of a new era of marketing, sales analysis, product development, and dozens of other areas of business—all thanks to the powerful insights that data can offer us. With data, we can learn more about our strategies, find better improvements, and ultimately operate more efficiently and with more satisfied customers. Though most data analysis and development methods revolve around digital strategies, even traditional marketing and advertising methods like print services can benefit from the influence of big data.

Even so, there are some businesses that have resisted building new data collection or interpretation systems, or else haven’t adapted to the new world that this available data brings. With the potential for a more efficient, more effective system in almost every application, what’s stopping these businesses from moving forward?

Factors Preventing Adoption

These are some of the biggest reasons why many businesses still refuse to adopt any big data systems:

1. Fundamental misunderstandings of data.

​​One of the biggest limiting factors is the simple misunderstanding of what “big data” is and how it can be used to get to know your customers better. Because it’s a heavily circulated buzzword, some business owners have no idea what practical processes and benefits are actually ...


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Australian Innovation Shines at BigInsights Data Innovation Awards

Australian Innovation Shines at BigInsights Data Innovation Awards

Both, Australia’s Federal Assistant Minister for Industry, Innovation & Science, the Hon Craig Laundy MP, and NSW Minister for Innovation & Better Regulation, the Hon Victor Dominello presented the 'BigInsights Data Innovation Awards 2016' on December 6, 2016, at the University of Sydney.

Attended by 150 industry professionals, the BigInsights Data Innovation Awards recognise teams & end users that are doing ground-breaking work using Data Analytics & IoT to deliver business outcomes. (www.dataawards.org) Hitachi Consulting and University of Technology Sydney were the key sponsors.

The Awards attracted interest from over 35 organisations, from which 18 entries were received. They were judged by a team of independent industry experts that conferred 6 Awards in the five categories that clearly demonstrated best practices in developing and deploying Analytics or IoT techniques.

The Winners of the Awards 2016 BigInsights Data Innovation Awards were:


Best Industry Application of Data Analytics - Ambiata Pty Ltd


Scalable Machine Learning and Experimentation System for Personalised Advertising


Best Industry Application of IoT - Internet of Light Technologies Pty Ltd


Light Net - creating intelligent buildings and smart cities through a new global communications network of connected intelligent Lighting


Best Industry Application of AI/Cognitive - Strategos Pty Ltd


Stratejos - an artificial team assistant that helps teams know the ...


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Don’t Let America Send Your Startup Home Broke

Don’t Let America Send Your Startup Home Broke

Silicon Valley is the epicenter of the startup world, but it’s also a valley of shattered startup dreams. Many international founders arrive with hopes of raising multiple rounds of funding and   creating explosive growth, but most startups rarely “make it”. Silicon Valley has a unique culture that can be difficult to understand, not to mention the insane competition. The chances to grow a startup from idea to successful exit in Silicon Valley are lower than of a bartender in Los Angeles turning into Hollywood movie star.

Don't Come to the US to Raise Money

You weren’t expecting that were you? Here’s a typical story: an international startup creates some momentum in its home country and eventually develops the desire to come to Silicon Valley for funding to start scaling growth. The startup spends a few months networking, meeting key people and decision makers. They generate lots of discussions, promises, expectations and advise. But no checks. The founders run out of money and return home disappointed. Sadly, this happens all the time.

Imagine going to a friends party with a goal to get laid. Approaching every attractive person with a direct request for sex is not going to be a very good strategy, not ...


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How Much of the Average Workload is Handled Through The Cloud?

How Much of the Average Workload is Handled Through The Cloud?

By 2020, it is estimated that consumer cloud storage traffic will be 1.7 GB per month, per user. In the next four years, 2.3 billion users will be using personal cloud storage. It is no surprise then, that most people are now taking advantage of cloud storage in both personal and professional capacities. But storage is not the only benefit of cloud technology—it is gradually seeping into every aspect of the average workplace.

But how exactly is cloud technology affecting the average workload? Is the cloud really as revolutionary as people make it seem, or just an overhyped fluke? The truth is that there is no exaggeration here, cloud technology is not only useful for the average employee, it is quickly becoming essential. Here are just a few of the ways in which the average workload is handled through the cloud.

Training

Some of the first people to jump onboard and fully take advantage of the cloud were training companies. Through taking advantage of mobile learning management systems (LMS), they were able to provide certain tools that have completely transformed employee onboarding and training across the board. The cloud allows for companies to create and customize their training programs to fit the specific needs ...


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Yep, I’m Writing a Book on Modern Data Management Platforms

Yep, I’m Writing a Book on Modern Data Management Platforms

Why?

Over the past couple of years, I have spent lots of time talking with vendors, users, consultants, and other analysts, as well as plenty of people from the data management community about the wave of new technologies and continued efforts aimed at finding the best software solutions to address the increasing number of issues associated with managing enterprise data. In this way, I have gathered much insight on ways to exploit the potential value of enterprise data through efficient analysis for the purpose of “gathering important knowledge that informs better decisions.

Many enterprises have had much success in deriving value from data analysis, but a more significant number of these efforts have failed to achieve much, if any, useful results. And yet other users are still struggling with finding the right software solution for their business data analysis needs, perhaps confused by the myriad solutions emerging nearly every single day.

It is precisely in this context that I’ve decided to launch this new endeavor and write a book that offers a practical perspective on those new data platform deployments that have been successful, as well as practical use cases and plausible design blueprints for your organization or data management project. The information, insight, and guidance that I will provide is based on lessons I’ve learned through research projects and other efforts examining robust and solid data management platform solutions for many organizations.

In the following months, I will be working hard to deliver a book that serves as a practical guide for the implementation of a successful modern data management platform.
The resources for this project will require crowdfunding efforts, and here is where your collaboration will be extremely valuable.
There are several ways in which you can participate:

  • Participating in our Data Management Platforms survey to obtain a nice discount right off the bat)
  • Pre-ordering the book (soon, I’ll provide you with details on how to pre-order your copy, but in the meantime, you can show your interest by signing up at the link below)
  • Providing us with information about your own successful enterprise use case, which we may use in the book

To let us know which of these options best fits with your spirit of collaboration, and to receive the latest updates on this book, as well as other interesting news, you just need to sign up to our email list here. Needless to say, the information you provide will be kept confidential and used only for the purpose of developing this book.

In the meantime, I’d like to leave you with a brief synopsis of the contents of this book, with more details to come in the near future:

New Data Management Platforms

Discovering Architecture Blueprints

About the Book

What Is This Book About?

This book is the result of a comprehensive study into the improvement, expansion, and modernization of different types of architectures, solutions, and platforms to address the need for better and more effective ways of dealing with increasing and more complex volumes of data.

In conducting his research for the book, the author has made every effort to analyze in detail a number of successful modern data management deployments as well as the different types of solutions proposed by software providers, with the aim of providing guidance and establishing practical blueprints for the adoption and/or modernization of existing data management platforms.
These new platforms have the capability of expanding the ability of enterprises to manage new data sources—from ingestion to exposure—more accurately and efficiently, and with increased speed.

The book is the result of extensive research conducted by the author examining a wide number of real-world, modern data management use cases and the plethora of software solutions offered by various software providers that have been deployed to address them. Taking a software vendor‒agnostic viewpoint, the book analyzes what companies in different business areas and industries have done to achieve success in this endeavor, and infers general architecture footprints that may be useful to those enterprises looking to deploy a new data management platform or improve an already existing one.

How to Gear Up For A Driverless Future

How to Gear Up For A Driverless Future

There’s been a lot of talk lately about autonomous cars with countless companies proclaiming they’ll be the first to release this revolutionary tech. Competition is heating up between companies like Volvo, Google, Uber and Tesla to name a few. Uber piloted self-driving cars in Pittsburgh last month, overseen by a Driver and Engineer. Singapore-based software developer nuTonomy has also launched trials into self-driving cars with hopes to launch them in 2018 for public use.

Each year, a mammoth 1.2 million people are killed on the world’s roads, with 90% of these tragedies being down to ‘human error’ – drunk driving, tiredness, road rage, visual impairment. The technology used in creating the driverless car makes it possible to reduce the number of fatalities.

There are numerous systems working together to power driving the car. Lidar Sensors, highly accurate sensing systems that work by rotating and bouncing pulsating laser light off of the surroundings. This produces ‘time-of-flight’ measurements of range, accurate to the centimetre, producing millions of data points per second. The car is able to maintain a precise position thanks to ultrasonic sensors in the wheels. Altimeters, gyroscopes and tachymeters are also at work to make sure the car keeps the correct and ...


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4 Ways Big Data and Machine Learning Are Helping Conservation

4 Ways Big Data and Machine Learning Are Helping Conservation

The interdisciplinary field of computational sustainability is using machine learning algorithms to analyse and extract valuable insights from sets of Big Data gathered from environmental fields. It’s not just about having large data sets or advanced pattern finding algorithms – it’s how we use them. The following projects highlight how Machine Learning and Big Data are helping conservation efforts of all kinds.

Earthcube Project

The ambitious Earthcube project has been in development the past five years and aims to produce a living 3D replica of Earth to serve scientists of different disciplines. It has been built upon interconnected projects that use computer science, big data, and geoscience among various other branches of learning. There is a gold mine of data when it comes Earth sciences that have been collected over years of study and research that has the potential to benefit environmental causes greatly. Earthcube funds a variety of projects, such as the Coral Reef Science & Cyberinfrastructure-Network (CRESCYNT). By using species databases, image analysis software and 3d mapping they can monitor the decline of the coral reef’s structural changes, disturbances, coral disease, sea temperatures and coral bleaching. The research will allow for a greater understanding to help protect and preserve what ...


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