5 Data Fails in 2014

5 Data Fails in 2014

When it comes to data breaches, leaks and blunders, 2014 had more than its fair share. Many major brands took data-related hits that became PR nightmares, scrambling marketing departments into damage control mode as they tried to repair tarnished images.

In the spirit of learning from past mistakes so as not to repeat them, here’s a look at some of last year’s standout data fails and what advertisers/marketers can do to avoid them.     

1. Bank of America’s “What’s in a name?” Blunder  

As reported last February in a Time.com article, Lisa McIntire, a feminist writer living in San Francisco received a credit card offer from Bank of America addressed to: “Lisa is a Slut McIntire”. In response to the mislabeled mail, McIntire told The Daily Intelligencer that the letter “initially made me nervous because I’m a feminist writer on the Internet. But I don’t think it’s personal.”  Bank of America has yet to comment on the cause of the junk mail mistake.  

The lesson for marketers who buy mailing list data from third parties for Big Data advertising purposes is to make sure that the data is clean and problem-free before launching campaigns.

2. Facebook’s User Manipulation Scandal  

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6 Tips for Landing a Job in the Big Data Industry

6 Tips for Landing a Job in the Big Data Industry

The information explosion is driving demand for qualified individuals in the booming Big Data industry. According to a recent study by the UK-based business analytics company SAS Institute, the number of employees that organizations will need to carry out Big Data tasks is expected to grow by more than 240 percent by 2017. What does that number mean for the U.S.? As more and more companies turn to Big Data analytics platforms such as cloud-based Hadoop to collect, manage and mine mountains of rich data for competitive advantage, the demand for Big Data employees will dramatically outpace supply. According to a 2011 report published by McKinsey & Co., by 2018 the U.S. could “face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions.” This huge talent gap represents a tremendous opportunity for those looking to pursue a career in the booming Big Data industry, provided they have the right qualifications. Based on a review of credible online sources that outline the qualifications Big Data employers are looking for in new hires, here are 6 tips for landing a job in the Big Data industry.

1. Have a Solid Skill Set

In a recent IEEE.org article, Dennis Shasha, a researcher in pattern recognition and database mining and associate director of NYU Wireless at New York State University, pointed out three important skills that are needed to be effective in handling Big Data. According to Shasha in the article, First is an understanding of databases and how they manage large amounts of data. Next is knowledge about machine learning and data mining, which allows inferences to be made from the data. Last comes statistics, so you can estimate the reliability of your conclusions.” In addition to those three skills Shasha also added that, “It’s important to understand the field in which the data is going to be used. This allows you to ask the right questions and design the right experiments to produce additional data.”

2. Have a Curious Mind

Speaking of asking questions, being inquisitive by nature is an important trait, according to a recent InformationWeek article. In fact, a background in philosophy couldn’t hurt, points out Jeff Remis, branch manager of the national IT practice at Addison Group. “Having the background in Big Data helps you find the patterns hidden in the chaos,” says Remis, “but to understand the patterns takes someone with a trained philosophical mind. Philosophers are curious people who use logic and theory to tell a story with the data, which is what companies are looking for from analytics teams."

3. Be Willing to Get Your “Hands Dirty”

While data science theorypresents a view of Big Data as tidy, unstructured Zettagytes of data are messy and complex. That’s why Mike Driscoll, CEO of Metamarkets, a San Francisco based data analytics company, says he prefers to hire candidates who aren’t afraid of what he calls “the grimy work- the coal mining of the information age which is to extract, transform and load data.” As quoted in a recent InformationWeek article Driscoll said that, “the practical experience of building databases and handling "messy" data in the real world is a sign of someone who's ready and willing to dive in and "learn the art" of data science.”

4. Have a “Hybrid” Degree

In the aforementioned InformationWeek article, Mike Driscoll also indicated that a number of aspiring data scientists are pursuing “hybrid degrees”, such as a political science major with a minor in math. Driscoll feels that that’s “an excellent plan of action”, being that, “There are certain domains or areas of knowledge that are difficult to learn on your own and outside of a formal educational process---those domains are often the hard sciences."

5. Leverage Related Skills

In a June 2014 article on InformationWeek, Adam Stillman, senior technical recruiter for Eliassen Group points out that both younger and veteran job applicants should not be, “bashful about highlighting relevant skills that may be indicators of probable success in the Big Data world.” In a tight labor market, Stillman suggests that prospective hires that lack hands-on Hadoop experience, but show that they, “have Very Large Database (VLDB) and relational database management system (RDBMS) chops,” could stand a better chance of being hired.” The key, according to Stillman, is for the candidate to “demonstrate strong theoretical knowledge in the area of Big Data.”

6. Be Flexible

A number of web articles point out the importance of being flexible in searching for and obtaining a Big Data job. For the relatively inexperienced, this may mean taking a full-time job to get important practical experience with the Hadoop ecosystem from MapReduce to Oozie that can help lead to the “dream job.” The willingness to travel to a new area in order to obtain a position in Big Data is also important. Big Data is definitely hiring and the prospects for employment in the Big Data field are bright. Employing these tips and others can help Big Data candidates to find the best fit in an exciting industry that is rapidly growing and evolving.

Is Big Data The Key to Crisis Management?

Is Big Data The Key to Crisis Management?

When was the last time you thought about the steps your company is taking to avert a potential crisis or quickly respond to one of those crises? And when was the last time you took an in-depth look at your company’s vulnerabilities, as opposed to simply acknowledging them and then moving on?

These may not be the most exciting questions to ask, nor the most popular ones. Nonetheless, they are three questions that executives need to constantly be asking if they want to succeed in today’s competitive marketplace. Having a deep understanding of your company’s weaknesses and vulnerabilities is one of the most important things that can be done. Every company has a weak spot or two (or three), and at some point in the future they’re going to be forced to deal with it. How the company has prepared for that certainly will go a long way in determining how successfully they navigate it. Too many times companies are poorly prepared and pay a steep price for it — many even go out of business.

Fortunately, in 2014, there is an incredible array of tools that companies can use to prepare for the rainy days to come. One of the most powerful, of course, is Big Data analytics. The power of Big Data analytics gives companies the capabilities necessary to pinpoint, with great accuracy, areas of internal and external concern. It takes much of the guesswork out of crisis management, and it does so in real time.

So, what are some steps companies can take to avoid and deal with crises using Big Data? Here are just a few.

1. Identify weaknesses

It is paramount to a company’s success that they identify any and all areas that could be potential vulnerabilities. A simple and effective way to do this is to look at the areas that would cause great harm to the company, its shareholders, and the customers if there were a security breach and/or a loss of information. Additionally, companies can implement Big Data at this stage to identify other areas of weakness that to the human eye would go unnoticed. The key to the whole process is being focused and thorough. It does the company no good if they try to paper over the cracks or pretend the weaknesses don’t exist. It may seem ok for the short term, but over time it’s going to come back to haunt them, often at a large cost. Big Data also gives companies more items they can monitor in order to identify weaknesses, items like interactions, signals, movements of goods, and activities. Through these numerous different sources, they’ll be in a much better position to make any needed changes.

Using Big Data analytics, companies can easily identify and analyze factors that could potentially cause distress to the company. Whether it’s economic levels, war zones, natural disaster zones, high theft areas, or another criterion, companies can zone in on areas of concern. With enough advanced analytics, businesses will be able to weed out bad data, which can reduce the uncertainty regarding identifying weaknesses. Companies can also take this risk assessment one step further by looking closer at third party contractors and providers. Target recently suffered a massive security breach thanks to attacker infiltrating a third party. This example shows third party vendors may open up larger companies to unnecessary risks, so the larger company needs to use Big Data to identify the weakness quickly. Again, the best part is that all this can be done very quickly and with great accuracy with Big Data.

2. Look at past results and potential future results

Once a company has identified areas of concern, it can then go back to its data and look at how those areas have affected the company in the past, and what is expected to occur in the future. With those results, it’s easier for a company to identify negative outcomes that would occur if a disaster of some sort were to occur.

In this step, as with the first, Big Data is extremely vital. With it, companies can discover new ways to overcome problems they’ve faced in the past and those they’ll likely face again in the future. Additionally, the analytics allow companies to run sequences that can test suggested solutions, allowing companies to test out their theories without having to go through the actual problem. Of particular value is the use of social data, which can be utilized by businesses to predict future crises based off of the information from past events.

3. Use Big Data to monitor current crises

Every company hopes to never be struck by a crisis, but unfortunately most are. Here too, a Big Data platform is a great tool. With its analytical capabilities, it can be used to monitor the web, social media, consumer sentiment, company sentiment, and a variety of other measurements that may be key to a crisis management plan. Again, it can do all of that in real-time allowing your company to make quick, effective decisions to get back on track. Big Data can also be extremely effective in helping multiple branches and divisions work together when dealing with a major crisis.

Managing risk is difficult. It’s not fun to take an in-depth look at a company’s weaknesses and vulnerabilities, but it’s essential to success. With Big Data, companies can identify those areas quickly and with great accuracy to create more effective plans for averting and responding to disasters.

Post written by Gil Allouche

The post Is Big Data The Key to Crisis Management? appeared first on BigData-Startups.

Gil Allouche

Three Steps for Building a Big Data Dream Team

Three Steps for Building a Big Data Dream Team

In the ever-changing technology environment that we currently live in, it can be easy to get bogged down, both on the personal level and business level, trying to figure out how you can best implement the latest releases. Whether it’s a new phone or tablet or new Big Data software, it’s easy to get stuck.

For a company to succeed it’s got to figure out how it can leverage it’s current business position and products with the new Big Data platforms that are available. A sound understanding of how the technology can improve your product and your offerings is a must before actually going with the technology.

Because of all the hype that surrounds every new technology release, it can be easy to get caught up in the euphoria of the moment and immediately hop on the bandwagon with no real purpose or direction with what you’re doing.

This is especially true with Big Data. In many respects Big Data is extremely complex and difficult to understand, at least the technical aspects, by someone who comes from a non-data background.

All too often a CEO will hear of the abilities Big Data has and naturally, want to implement it into his or her business operations. The problem, however, is that the company doesn’t have the right talent, the right culture, the right understanding and, possibly, the right technology to really make the most of Big Data.

When it comes to getting up and running with Big Data in your company and building your own dream team there are certain steps that need to be taken to ensure complete success.

1. Data needs to be a priority

If a company is going to succeed with Big Data, the entire company has to buy into the Big Data concept — from top to bottom. Otherwise, people will get undercut, initiatives will get derailed, executives will clash and Big Data will go from help to hinderance very quickly.

The CEO sets the example and should expect everyone to follow. And while most CEO’s aren’t going to come from data backgrounds, that’s ok. It’s not necessarily understanding the technical aspects that matter. It’s clearly understanding what the technology can do for the company.

2. Implement the right technology

Big Data in the cloud is completely changing the availability and affordability of Big Data. In so many ways it’s making Big Data a real possibility for companies of all sizes. There’s no expensive infrastructure to install, no servers to maintain, no expensive startup costs and much more flexibility.

Additionally, cloud computing completely changes how CEO’s look at building a data team.  A Big Data in the cloud provider, does so much of the work on the data end that the companies don’t have to worry as much about hiring large data teams full of individuals with advanced degrees in data science.  What companies now have to worry about is hiring the right people.

3. Hire the right people

So, who are the right people? The right people come from any background, but they have a sound understanding of the importance of data and how it can help the company. Your business needs to find talented individuals who can find innovative ways to implement technology to solve your most difficult business solutions.

They don’t have to understand exactly how the technology works, but they must understand how it can benefit you. Obviously, hiring the right people makes all the difference.

It’s tempting to want to hire a large team of data scientists, but with Big Data in the cloud that’s not what you need. The cloud service providers already have that team of scientists. If you hire more, it’s only going to make things more difficult. To succeed, the company needs smarts on how to best use the technology, starting with the CEO and going down.

In today’s business environment, it’s more important than ever that companies get their data strategies right. A Big Data dream team takes time to assemble, but it’s worth every minute and every penny. Remember it’s understanding how technology can work for your company that will make all the difference.

Image: The Next Web

Post written by Gil Allouche

The post Three Steps for Building a Big Data Dream Team appeared first on BigData-Startups.

Gil Allouche

Does Big Data Really Matter to Your Organization?

Does Big Data Really Matter to Your Organization?

More end-users are acquiring more devices. Industry analysts focused on tracking enterprise software application development and data management have predicted there will be somewhere between 26 and 30 billion devices by the year 2020. Although more device usage may mean more data passing through organizations, it does not necessarily mean there is more useful data that can be translated to a strong ROI. IT managers and CIOs must ask if Big Data can actually result in productive incremental value for their respective enterprises.

What to do about Big Data if you’re confused

There are five steps to take when an opportunity tied to Big Data presents itself.

1. Identify the opportunity
Researchers from MIT and the University of Pennsylvania found data-driven firms performed 5-6 percent better which is a big deal if you consider its impact compounded annually. Companies have the opportunity to generate useful data regardless of size or even type of business.

2. Build Skills
Data science skills are becoming crucial to compete in today’s marketplace and Big Data expertise, in particular, is a scarce resource. It’s important for companies to recruit, train and integrate people with data skills, or they will find themselves at a significant disadvantage.

3. Collect Data
The third step is to make data a priority. Focus on the “three P’s”: proprietary, public and purchased data. The longer you work in any industry, the more you realize that information is power. You can find data retrieval opportunities both for a fee and for free at sites like Data.gov. Don’t ignore the data opportunities all around you.

4. Unify Your Architecture
Use an API driven approach to integrate legacy systems before building a completely new architecture. Find ways to unite your enterprise on all fronts and efficiently handle Big Data.

5. Adopt a Big Data Mindset
Having the right mindset it just as crucial to Big Data success as access to the data itself. Managers will have to defy convention and adapt a new mindset as the traditional, step-by-step strategic planning approach will no longer do.

Responding to Big Data challenges for CIOs

Many CIOs fight the perception that Big Data is a passing fad. CIOs have to tread carefully when talking about Big Data efforts while some even try to avoid using the term altogether because of its negative connotation in their organization. However, the reality is CIOs are now dealing with large volumes of unstructured data whether they choose to acknowledge it or not.

To address Big Data challenges, CIOs need to add to their company’s Big Data skill set by hiring data scientists, mathematicians and information architects.

The need to convince enterprises of the importance of Big Data governance is a struggle for executives but can be overcome with solid research. Although the transitions may seem slow, there are CIOs moving to hire new analytical staff and training existing staff, but the transitions come with their challenges.

Budgets have severely impacted the capabilities of organizations. Such budget challenges have led some CIOs to explore new approaches to data governance since it’s the most critical factor holding up agencies in their efforts to pursue Big Data.

Solutions to managing Big Data in your organization

The number of Big Data solutions available is increasing significantly. Hadoop services in the cloud are making Big Data more accessible to smaller organizations with limited budget and expertise. Newer technologies, such as Apache Spark, are also taking us closer to performing real-time analytics. Evaluate each technology available based on your business objectives, and create your own combination of tools to fit your needs.

Image: ReadWrite

Post written by Gil Allouche

The post Does Big Data Really Matter to Your Organization? appeared first on BigData-Startups.

Gil Allouche

Three Examples of Big Data Applied in Energy Analytics

Three Examples of Big Data Applied in Energy Analytics

One of the hottest topics today is energy — including consumption, discovery and implementation. The importance of energy, especially renewable, reusable and affordable energy, cannot be overstated both at an individual and business level.

Today, more than ever, there are a plethora of uses for energy. So much of what we do today has to be powered by energy of one sort or another. That being said, energy generally isn’t cheap, and in many places it can be difficult to come by.

Entities need more energy than ever before, and they want it at prices more affordable than ever. It’s a tough task to ask, but with the assistance of Big Data and Big Data technology such as Hadoop as a Service that is a very real possibility.

Big Data allows companies to gather, store and analyze extremely large (terabytes and petabytes) amounts of information. There are two ways that companies can implement Big Data.

First, they can choose to install the architecture and infrastructure on site or they can use Big Data in the cloud and have the cloud provider take care of all infrastructure. Whichever way a company decides to go, the benefits that Big Data brings will soon be very evident.

Of the many ways that Big Data can assist in energy analytics, here are just a few.

Going Green

There’s a big push today for companies to go green, and rightly so. Taking care of the earth is important on so many different levels and as a society we are taking important strides to improve how environmentally friendly we are. Nonetheless, there is still plenty of room for everyone to grow.

By implementing Big Data, companies can use it’s real-time and batch processing analytical tools to evaluate their current green strategies and assess if those strategies are actually working and other areas that they can change to green.

Going green shouldn’t be just about PR or about doing what’s “in.” If companies really want to take things to the next level, there needs to be a real commitment to being friendly to the environment. Big Data can help bring that to fruition.

Cutting Back

It’s very easy for companies, big and small, to lose track of energy use. There are so many moving parts that make up a company, that losing track of energy is easy. It’s especially easy to forget if it’s not a priority for the top executives. Nonetheless, energy consumption is a continually rising cost, one that can spell financial hardship for many companies if its not controlled.

On the other hand, if energy consumption is sufficiently monitored and improved, companies can improve efficiency and reduce expenditures. It’s a win-win situation. It’s difficult, however, to do that type of energy monitoring without Big Data. Sure, you can rely on the bill that arrives each month from the city and energy companies. However, if you truly want to take your energy efficiency to the next level, while still cutting back, then Big Data is the way to go.

Energy Preservation

Much of our energy isn’t going to last forever. Fortunately, many advances in technology have provided additional, renewable and reusable energy resources, but even with these, there’s still a need to preserve and protect the energy that we already have.

By using Big Data on a community, state or even national level, governments can better preserve the energy resources that have become so vital to their success. With the tensions that exist between nations, being self-sufficient is more important than ever.

By implementing Big Data, companies and governments can drastically improve their energy preservation while still finding more sources of energy.

Being able to implement environmentally-friendly energy while cutting back use and expenditures and preserving energy are three things that can have a forceful impact on businesses and communities. By harnessing the power of Big Data analytics, this is more viable and affordable than ever before.

Post written by Gil Allouche

The post Three Examples of Big Data Applied in Energy Analytics appeared first on BigData-Startups.

Gil Allouche

The Big Data Dilemma: Is Quality vs. Quantity a Non-Issue?

The Big Data Dilemma: Is Quality vs. Quantity a Non-Issue?

The question of “quality vs. quantity” has become so ubiquitous that it has arguably become cliché. However, this long-standing issue remains a serious topic, especially when dealing with Big Data.

Traditionally we are asked to choose between quality or quantity – one or the other. You can’t have your cake and eat it too; or rather, you can’t have your 5 supermarket cakes and single high-end bakery cake and eat all of them. OK, you could. But you’d definitely pay for it the next day.

However, with modern data, this predicament has been largely resolved. While the main issue in the quality vs. quantity debate has traditionally been concerning the cost of storing massive quantities of data, today data storage is cheaper than ever, and decreasing every day.

Just think about this – in the early 90s, you’d find data storage costing $3,000 per GB (and that’s a lowball number). Ten years later in 2000, it was $20/GB. Today we’re at $0.03/GB. The rise of  cloud storage and habitual decline of downloading (at least in the classical sense) is helping to dramatically decrease the cost of storage.

Today it’s cheaper than ever to collect data – good thing too, because there is a ton of data out there to collect. We’re dealing with ever-increasing consumer web activity and easy means of collecting all kinds of data about web visits, site activity, click-through rates, conversion paths, and more!

With such vast deposits of data, the main issue becomes: what do we do with all this data? The value of a Big Data platform is contingent on what processing that data leads to. What sense can be made of the data? You could have a gazillion petabytes of data, but if no understanding can be siphoned, it’s all useless.

Does your Big Data analysis allow your business to make more intelligent business decisions? Does it lead to new product innovations? Can it help you lower costs? Conduct better transactions? Improve your ROI? Better understand customer behavior? Quantity is meaningless if it doesn’t drive actionable value.

Big data isn’t so different from a Monet painting. Examine your data too granularly and you’ll only see a bunch of dots and blotches before your eyes. Step back a bit for the bigger picture, and you’ll be able to make sense of your data at the macro level and decipher meaning. Still, you’ll obtain the best value when viewing from just the right angle and the perfect perspective.

When you get that right angle, that super sweet spot, the benefits can be massive. Companies relying on delivery systems are now utilizing GPS truck data to track delivery routes, speed, performance, and scheduling. UPS used this kind of Big Data  to optimize routes and save massive amounts of time and money. In 2011 they saved 8.4 million gallons of fuel by trimming 85 million miles off daily routes. Considering that cutting only one daily mile per driver saves them $30 million, the cumulative savings are more than a little impressive.

Amazon is the ultimate example of the magic that can happen when Big Data is utilized properly. Amazon uses massive amounts of collected consumer data to predict what products customers are most likely to purchase after viewing specific items.

It’s easy to see why large data quantities are useful and valuable. But do we still need to worry about data quality? Huge data quantities make individual errors largely unimportant. So long as the majority of data is accurate, a few corrupted pieces of data will be as inconsequential as a few specks on a windshield.

However, in some cases data cleaning is an essential step in filtering out erroneous pieces of data. In order to determine just how important quality is for your Big Data, it’s important to consider the cost of corrupted data, especially when bad data can have tremendous impact on individual scenarios. For example, as Jeff Kelly notes, when you are using Big Data to determine medicine dosage for critically ill patients, you need to be relying on good data, not just mostly good data.

Ultimately, the importance of data quality will depend on:

A.How much effort will be required to correct erroneous data?
B. What is the data being used for, and what are the consequences of using bad data?

If the results of a few bad data pieces aren’t dire, it’s probably not worth the parsing. If you’re making decisions based on large quantities of data, you’re likely to be fine with a few minor blips. Honing in on narrow data segments is when you’ll want to be more cautious and consider paying more attention to quality.

What do you think is more important when it comes to Big Data: quality or quantity?

Post written by Gil Allouche

The post The Big Data Dilemma: Is Quality vs. Quantity a Non-Issue? appeared first on BigData-Startups.

Gil Allouche

Cloud vs. In-house: Which Hadoop Option is Right for You?

Cloud vs. In-house: Which Hadoop Option is Right for You?

Companies across the globe are beginning to realize the immense value that Big Data can add to their business. More and more of them are implementing Big Data each day. If your company too, is on the verge of putting Big Data to work, then there are a few things you need to know. There are two ways to go about using Big Data — establishing it on your company’s premises or using a provider that offers a Big Data platform in the cloud. In the past it used to be that companies only had the option to establish it on site, but that is no longer the case. Each business is different, which means that while one company may prefer to install Big Data on site, another may wish to use Big Data in the cloud. Here are four factors to consider as you decide which way to go.

Cost

Cost is an extremely important factor, and many times it’s the determining factor. So, what are the cost differences between Big Data on site and Big Data in the cloud?

Big Data in house requires companies to install costly infrastructure in order for the data to be gathered, stored and analyzed. It’s generally a multi-million dollar process that’s paid up front. Because of that, in the past many small businesses were unable to implement Big Data due to the huge startup costs. Now, with Big Data in the cloud, those beginning costs are mostly eliminated. It’s much cheaper to get going with Big Data in the cloud. Additionally, Big Data on site generally requires a team of experts to monitor the equipment and to handle the data gathering, storing and analyzing. Again, that’s something that many companies don’t have and can’t afford to hire all at once. Big Data in the cloud takes care of that for the companies. There are also no maintenance fees with Big Data in the cloud. There are, however, monthly fees for the use of Big Data in the cloud that companies need to be aware of, but they generally only charge for what you use.

Security

One of the greatest advantages that Big Data on site gives to companies is an added measure of data security. All the data is stored locally on premise and is much easier to monitor. The information is very secure. The company knows at all times who is accessing the data and how it’s being used. With Big Data in the cloud there’s always an inherent risk. That being said, Big Data in the cloud is still extremely safe. Reputable cloud storage companies have taken the necessary steps to ensure your data is safe and secure. Industry standard encryption methods, along with other security measures ensure you won’t lose your valuable data. It’s not as safe as on site, but it’s close.

Current capabilities

An important point to consider when making this decision is your current Big Data capability. Do you have personnel to support on site implementation? Do you have a team that can oversee all aspects of Big Data? Do you have a team that can ensure proper maintenance and workflow? If you don’t have these things, can you afford to hire them? There are significant staffing needs for in house Big Data. Big Data in the cloud also has staffing needs, but they’re far less extensive. With Big Data in the cloud, companies can really focus on what’s most important — making sense of the information gathered and implementing it to improve business.

Scalability

The more that data becomes available, the more important scalability becomes. Simply put, scalability is the flexibility a company has to increase or decrease its data-gathering capabilities. It’s much harder to scale with Big Data on site. If you have more data than usual, you need to install more infrastructure which can be extremely costly. If you have less data, then you’re stuck with costly, unused infrastructure. Big Data in the cloud allows to to scale up or down with incredible ease and without negative financial implications.

Remember each business is different. Some may prefer the security and control that comes with Big Data on site and they have the resources to afford that. Others may prefer the flexibility and ease provided with Big Data in the cloud. Either way, it’s important to implement Big Data. Your company will reap the rewards.

Post written by Gil Allouche

The post Cloud vs. In-house: Which Hadoop Option is Right for You? appeared first on BigData-Startups.

Gil Allouche

5 Applications of Big Data in Government

5 Applications of Big Data in Government

One of the greatest strengths of big data is it’s flexibility and universal application to so many different industries. Along with many other areas, big data in government can have an enormous impact — local, national and global. With so many complex issues on the table today, governments have their work cut out trying to make sense of all the information they receive and make vital decisions that affect millions of people. Not only is it difficult to sift through all the information, but it’s sometimes difficult to verify the reality of the information itself. Faulty information can have awful consequences.

By implementing a big data platform, governments can access vast amounts of relevant information important to their daily functions. The positive effect it can have is nearly endless. It’s so important because it not only allows the government to pinpoint areas that need attention, but it also gives them that information in real time. In a society that moves so quickly from one thing to the next, real-time analysis is vital. It allows governments to make faster decisions, and it allows them to monitor those decisions and quickly enact changes if necessary. Here are just a few of the areas that big data can positively affect at the government level.

Transportation

Every day millions of Americans are on the road driving. There are so many different nuances to driver safety, from roads to police officers, weather conditions and vehicle safety that it’s impossible to control everything that might cause an accident. However, with big data governments can better oversee transportation to ensure better roads, safer roadways, better routes and new routes.

Healthcare

Healthcare is a very complicated issue these days, and not just here in the United States, but also across the world. With so many health systems that rely on government subsidies and support, there is a potential for resources to be wasted or to be unfairly allocated. With big data, governments can have a much clearer picture of where the money is going and why. It means they can also assume better control over resources. They can also analyze more effectively the needs of the citizens and from there make the necessary changes to provide the citizens with the best possible services for the best possible prices.

Education

Education is another extremely hot topic across the country. What can be done to improve education? There are a lot of different things to be done, and up-to-date, relevant information is vital to this. Big data helps governments understand more about educational needs on a local and federal level in order to ensure that the youth of the nation are getting the best possible education in order to serve the country in the future.

Agriculture

How do you keep track of so much land and livestock that exists in our country and across the globe? All the different crops that are grown, the animals that are held and so many other complicated issues come together in the agriculture world to form a very difficult job for the government. It’s hard to monitor because of the vast numbers. Big data is changing the ways governments manage and support the farmers and their resources. It’s ability to gather huge amounts of information and analyze them quickly makes all the difference.

Poverty

There is so much poverty in the world. It’s extremely difficult to combat, and we’ve been trying to do so for thousands of years. Big data gives governments tools to discover more effective and innovative ideas on how to decrease poverty across the world. It’s easier to pinpoint areas with the greatest need and how those needs can be met.

Big data technology is vitally important for governments across the world. It can’t solve every problem, but it’s a step in the right direction. It’s giving leaders the tools necessary to enact important changes that will be of benefit for citizens now and in the future.

Post written by Gil Allouche

The post 5 Applications of Big Data in Government appeared first on BigData-Startups.

Gil Allouche

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