How Analytics Is Evolving Like the Medical Field

How Analytics Is Evolving Like the Medical Field

It used to be that a doctor was a doctor for the most part. Even a century ago, unless you lived in a large city, people likely had a town doctor who handled most every type of ailment and guided most any type of treatment. Given the limited medical knowledge and lack of sophisticated treatment options during this time, these generalists could often provide a level of care that was comparable to the best available. Today, that is no longer true in medicine and a similar trend is playing out in analytics.

The Proliferation of Specialists

Today, the medical profession still has general practitioners who are usually our first line of defense. However, there are also specialists focused on a wide range of specialties within medicine. Radiologists, surgeons, orthopedists, and more are among those we interact with on a regular basis. Even within these specialties, there are sub-specialties. For example, brain surgeons and heart surgeons. While all medical professionals go through the same basic training, many then focus on training in a specific area.

When I started in analytics just a few decades ago, most of us were similar to the town doctor. For the most part, we were generalists who would apply ...


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Moving Beyond Predictions – Second Order Analytics

Moving Beyond Predictions – Second Order Analytics

Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.

Identifying The Action Is The Next Step

Once I have a prediction, simulation, or forecast, the next step is to identify what action is required to realize the potential value uncovered. Let’s consider the example of using sensor data for predictive or condition-based maintenance. In this type of analysis, sensor data is captured and analyzed to identify when a problem for a piece of equipment is likely. For example, an increase in friction and temperature within a gear might point to the need to replace certain components before the entire assembly fails.

Identifying the problem ahead of time sounds great. All we have to do is to identify when something is going to break and then fix it before it breaks. Doing so saves ...


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Why Predictions Are Not Enough

Why Predictions Are Not Enough

In recent times, I have read a number of articles lamenting the frequent lack of value resulting from large-scale analytics and data science initiatives. While I have seen substantial value driven from many efforts, I have also seen examples where the results were very poor. My belief is that oftentimes the problems can be boiled down to one basic mistake. Namely, thinking that generating predictions, forecasts, or simulations is enough. It is not.

Predictions Are The Starting Point…

Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find patterns. These algorithms are typically then used to generate one or more of the following:


Predictions about future events. For example, who is most likely to respond to a given offer?
Forecasts of future results. For example, what sales can we expect from the upcoming promotion?
Simulations of various scenarios. For example, what will happen if I shift some of my budget from paid search to television advertising?


There are other uses of algorithms and nuances between different types of predictions, but for our purposes here these three examples suffice and illustrate the point.

In each case, the output is information about what might be expected in the ...


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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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Ethical Implications Of Industrialized Analytics

Ethical Implications Of Industrialized Analytics

As analytics are embedded more and more deeply into processes and systems that we interact with, they now directly impact us far more than in the past. No longer constrained to providing marketing offers or assessing the risk of a credit application, analytics are beginning to make truly life and death decisions in areas as diverse as autonomous vehicles and healthcare. These developments necessitate that attention is given to the ethical and legal frameworks required to account for today’s analytic capabilities.

Analytics Will Create Winners & Losers

In my recent client meetings and conference talks, such as the Rock Stars of Big Data event in early November, a certain question has come up repeatedly. When I discuss the analytics embedded in autonomous vehicles, I am often asked about the ethics and legalities behind them. A lot of focus is given to the safety of autonomous vehicles and rightly so. If the automated analytics in the vehicle don’t work right, people can die. This means a lot of scrutiny is being, and will continue to be, placed on the algorithms under the hood. I often make the point that the technology for autonomous vehicles will be ready well before our laws and public ...


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Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?

Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?

I recently had someone ask me, “For years we’ve talked about changing analytics from 80% data prep and 20% analytics to 20% data prep and 80% analytics, yet we still seem stuck with 80% data prep. Why is that?” It is a very good question about a very real issue that causes many people frustration.

I believe that there is actually a good answer to it and that the perceived lack of progress is not as bad as it first appears. To explain, we need to differentiate between a new data source and/or a new business problem and existing ones we have addressed before.

Breaking New Ground

Whenever a new data source is first acquired and analyzed, there is a lot of initial work required to understand, cleanse, and assess the data. Without that initial work, it isn’t possible to perform effective analysis. Much of the work will be a one-time effort, but it can be substantial. For example, determining how to identify and handle inaccurate sensor readings or incorrectly recorded prices.

From the earliest days of my career, some of the most challenging work has been working with new data. For the first couple of analytics on a new data source, the ratio ...


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Breaking Analytics Out Of The Box – Literally

Breaking Analytics Out Of The Box – Literally

The lines between open source and commercial products are blurring rapidly as our options for building and executing analytics grow by the day. The range of options and price points available today enable anyone from a large enterprise to a single researcher to gain access to affordable, powerful analytic tools and infrastructure. As a result, analytics will continue to become more pervasive and more impactful.

Author’s note: I typically avoid mentioning specific products or services in my blogs. However, it is unavoidable for this topic. While I will make mention of a number of my company’s offerings here to illustrate specific examples of the themes, the themes themselves are broad and industry-wide.

Blurred Lines

Given the cost and overhead, it used to be that organizations would have to make an either/or choice when it came to selecting data platforms and analytical tools. Even with the advent of the open source movement, common opinions espoused either avoiding open source altogether or migrating completely to open source options. This either/or choice was a false one time has shown. As it turned out, most organizations now utilize a mixture of open source and commercial products to achieve maximum effectiveness.

From a platform perspective, large organizations typically are ...


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Why You Should Embrace Analytic Athleticism

Why You Should Embrace Analytic Athleticism

With today’s rapidly changing mix of analytic techniques, toolsets, and platforms, it’s difficult for any organization to be confident it is keeping its analytic workforce and skillsets up to date.

I often have clients ask if they need to consider turning over a large portion of their analytics organization in order to adapt to these changes. I firmly believe that this is usually not the case and that the fundamental skills for analytic success are in place. Those skills simply need tuning and updating.

In fact, I see a strong parallel between athleticism and analytic capability. I also see a strong parallel between learning to speak multiple languages and learning to work within differing analytic environments. I’ll explain what I mean by both of these statements in this blog in the hope that it will help make the path forward seem clearer and less intimidating.

Analytic Athleticism

People generally accept the notion of inherent athleticism. This concept says simply that there are people who are athletic and those who aren’t. While anyone can maximize their inherent athleticism with training, someone who isn’t very athletic will never compete at an elite level in any sport. On the other hand, people with an elite level of ...


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They Know What You’re Watching (And Why You’re Watching it)

They Know What You’re Watching (And Why You’re Watching it)

It wasn’t long ago that the entertainment industry had virtually no information on the end consumers of its products. A studio would create a television show, which would then be transmitted over the airwaves or through a satellite or cable feed. Even the broadcasters had virtually no information on which consumers watched what content. Outside of high level ratings and survey data, the industry was in the dark ages when it came to customer analytics and insight and there was little opportunity for customer engagement of any kind.

The First Wave: Set Top Box Data

Less than a decade ago, cable and satellite providers began to collect and analyze “set top box” data in earnest. This data effectively captures every push made on a remote control so that very precise information on what each subscriber watched can be captured and analyzed. What shows do members of a household watch? Do they watch live or record it? Do they commonly watch programs more than once? Do they rewind frequently? Do they pause and finish later? A wealth of information suddenly became available and the best ways to use that information are still being determined even as a lot of value has been driven ...


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Patterns Recur In Analytics Just Like In Nature

Patterns Recur In Analytics Just Like In Nature

I have always loved science and math, and that’s why I got into statistics and focused on analytics for a career. One thing that has always fascinated me is how certain patterns show up again and again in different places across nature and mathematics. When looking at two seemingly unrelated topics, it suddenly becomes clear that there is actually quite a strong linkage between the two and that they are simply different examples of the same underlying concept.

One example of this is the Fibonacci sequence which shows up in nature regularly in places such as the way sea shell spirals grow and the pattern of seeds in a sunflower. I recently came across a terrific example of the concept of similar patterns at work within the realm of data and analytics.

A Recurring Pattern in Analytics

I recently took part in an event (see a summary video here) where professor Eric Bradlow of Wharton gave a presentation about research he’s done on what he calls “clumpiness” in customer purchasing. Eric and I got excited about a tie between Eric’s formal work on customer clumpiness and some work my team had done a few years prior around store sales forecasts. My team had ...


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Pssst … How Much Money For Your Personal Data?

Pssst … How Much Money For Your Personal Data?

We’re all generating a lot of data about ourselves and how we live day to day. There are personal fitness devices, preferences and opinions expressed on social media, details on when we’ve come and gone from the house from our security systems, and more. It isn’t just data that companies are collecting from us, but data that we are directly generating ourselves. What should we be willing to do with it and at what price?

Who Would Want The Data?

Let’s take the example of the data from my personal fitness device and let’s assume for our purposes that I have full control of my personal fitness data. I have downloaded it all into a spreadsheet, so in effect I do have full control even though the manufacturer probably has some claim to it as well through the terms of use agreement. What might I do with this data and who might want it?

There are a wide range of companies and organizations that would love to get access to my personal fitness data. I’m not inclined to share it, at least not for free, but I have been thinking about under what terms I’d share it and at what price. Consider a ...


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Approach Big Data Analytics Like A Lego Kit

Approach Big Data Analytics Like A Lego Kit

A few months back I was having a conversation with a colleague of mine, Brad Elo. We were discussing the importance of operationalizing analytic processes and the need for the use of repeatable and standardized components to enable success. As part of the discussion, Brad brought up a terrific parallel between Legos and analytics that clearly illustrates the importance of approaching the analysis of big data correctly.

Lego’s Journey To Success

The Lego brand is a powerful one today. The Lego brand is behind not just popular building kits, but also television shows, movies, video games, and theme parks. It is hard to believe that such an iconic brand that has successfully innovated over the years came very close to bankruptcy a few years ago (see here and here).

When it comes to Lego’s pre-packaged building kits, there is one aspect that ties closely to the needs of big data analytics. Namely, Lego provides consumers kits that use a combination of both custom pieces and standard pieces to create awesome models which children (and some adults!) can’t wait to get their hands on.

I’d like to focus on two areas where analytics processes can borrow from the Lego model: reusability and increased adoption.

Letting Reusability ...


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Without Things, There Is No Analytics Of Things (AoT)

Without Things, There Is No Analytics Of Things (AoT)

I was recently having a discussion with Richard Hackathorn, an industry strategist & analyst and Dan Graham, a colleague who is deep into my company’s strategy for the Internet of Things (IoT). We were specifically talking about how to enable the Analytics of Things (AoT) and what barriers and opportunities exist today.

During the discussion, it hit me that one of the biggest hurdles faced in trying to purse the AoT is actually just a new iteration of a common, recurring problem that has vexed analytics professionals for years. Namely, we can’t analyze anything related to IoT until the infrastructure investment is made to create, acquire, and make available the data necessary for analysis. I recently wrote about one key aspect of the infrastructure related to automating the tracking and management of all of our things. Let’s address the need for it here.

Analysis Without Data Is …

… impossible! This is obvious, of course. But, it is a very critical point both today and in the past. Early in my career, we had all sorts of wonderful ideas about how to analyze data. However, the data itself was incredibly difficult, if not impossible, to access for analytical purposes. The move from mainframes ...


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Tracking All The Things You Need To Analyze

Tracking All The Things You Need To Analyze

As the Internet of Things (IOT) continues to gain momentum, there is a critical component to success that is missing today. While I’ve written about the power of the Analytics of Things (AOT), without addressing some tactical issues regarding the registering, tracking, and retiring of the things we want to analyze, we won’t be able to get where we need to be. We need to be in a position that allows easy and pervasive access and analysis of IOT data.

Recalling The Early Days Of PC Peripherals

Like me, many of you likely recall the early days of computers where adding any device to the system was a very difficult process. Items as common and popular as a printer could take many steps of detailed installation procedures, including the execution of operating system scripts. Make even one small misstep and you might have major computer issues on top of not getting the new device working correctly. With less common peripherals, the difficulty of getting things working was even worse. In short, connecting any device was a risky, hassle-filled mess.

In today’s emerging IOT world, we have a similar challenge. If I desire to analyze all the data I generate in my house from ...


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Change Your Business One Metric At A Time

Change Your Business One Metric At A Time

Change is hard for most organizations and individuals. Change that goes against historical cultural norms is even harder. In today’s fast moving business world, organizations have to change at a more rapid pace than ever before. Within the company I work for, we are going through a lot of change right now and I see it at many clients I meet with as well. While it may be difficult, change is possible. In many cases, taking a measured, steady approach to change can be more successful than pushing for massive, immediate change.

The Tortoise & The Hare

Many of us read Aesop’s fables when we were young. One fable that always stuck with me is that of the Tortoise and the Hare. The premise was that after being teased about his slow speed, the tortoise challenged the hare to a race. After establishing a massive lead on the tortoise, the hare stopped to snack and have a nap. As a result, much to the dismay and disgrace of the hare, the tortoise won the race.

There are various interpretations of the lessons that can be learned from this story. But for today we’ll focus on one specific lesson. Namely, to win, you need ...


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Why Netflix Asked The Wrong Analytics Question

Why Netflix Asked The Wrong Analytics Question

One of the legendary events in the history of analytics was the original Netflix prize. The event led to a terrific example of the need to focus on not only theoretical results, but also pragmatically achievable results, when developing analytic processes.

For those who aren’t familiar with the story, not quite 10 years ago, Netflix was having trouble achieving the desired improvement in its recommendation algorithms. There were smart people working on the problem, but progress had slowed as they used all the tricks and techniques that they knew. As a result, Netflix decided to do something that was, at the time, novel and unexpected.

A Simple Analytics Challenge

Netflix took a large piece of its data, anonymized it and made it available on the web for anyone to access. The analytics question was simple: can you build an algorithm that beats the performance of the Netflix baseline process by at least 10%. Success would be judged based on how the algorithm performed on a holdout sample that only Netflix had access to. Be the first to succeed and a $1 Million prize would come your way!

Many individuals and teams stepped up to the challenge. Over many months, teams experimented and compared results. ...


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All-Channel Marketing Is NOT Omni-Channel Marketing

All-Channel Marketing Is NOT Omni-Channel Marketing

I was on a panel for the news media community at the Teradata Partners conference a few weeks back. Our discussion centered upon how marketing is changing in today’s big data world. As the discussion started, something hit me like a ton of bricks. Namely, doing marketing across all channels is not at all the same as true omni-channel marketing. While it sounds like a semantic game at first, I will explain why it is much more than that.

What is All-Channel Marketing?

All-channel marketing is the easy way out. It is the path of least resistance that unfortunately way too many organizations go down. All-channel marketing is simply executing the same old marketing approaches through every possible channel. It isn’t using each channel for its strengths. It isn’t having different channels add unique value to the customer. It isn’t making the most of the new data and analytics that are available today with some of the newer channels.

So how does all-channel marketing work? Let’s say my organization has been delivering customized email offers for a few years now. We make the decision to get into marketing via social media. So, we now post the same offers on our Facebook account or ...


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How Analytics of Things (AoT) Will Help Us Analyze the IoT

How Analytics of Things (AoT) Will Help Us Analyze the IoT

The Internet of Things (IoT) is a topic that is continuing to rise in buzz and interest. The IoT has been rapidly arriving around us whether we realize it or not. However, the sensor technology behind the IoT and the data it generates is outpacing our ability to consume, analyze, and drive value with it. This must change, and it certainly will in light of the focus that organizations are putting on analyzing IoT data.

Without Analysis, IoT Data Is Worthless

To date, a lot of effort has been put into creating sensors, deploying them, and generating masses of data. However, lagging behind that effort is the analysis of the data. As with any data, no value is driven without analysis and action. It would have been better if more thought was given to how to utilize the data generated prior to creating sensors that stream it out. Given that we are where we are, the best path forward is to begin to aggressively analyze the data of the IoT. This is what I, and others, have begun to call the Analytics of Things (AoT).

The Promise of the Analytics of Things

The value of the AoT is already proven in a wide variety ...


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Ants, Padlocks, & Cyber Security

Ants, Padlocks, & Cyber Security

If you’ve followed the news recently, I don’t need to tell you that cyber security is a topic of major importance today. It seems that every week there is another revelation of a security breach at an organization thought by many to be a leader in data and network security. The breaches span governmental agencies and virtually every industry. Ironically, even a company that makes its money selling security hacks to governments and other organizations was itself hacked and its secret hacking code made public!

What is leading to this seeming deluge of security breaches? Are the breached organizations simply falling down on the job? I would argue that it isn’t that simple. In fact, there are several factors that make securing networks and data today an incredibly difficult task.

Padlocks & Lock Picks

Consider for a moment the physical door locks that are employed around the world on every house, building, or car. Over the centuries, increasingly complex locks have been created. Instead of the simple keys from the past, we now have very complex, machine milled keys for our doors. Often, there is an electronic passcode as well. With all the improvement in lock technology over the years, we’ve certainly eliminated ...


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Ants, Padlocks, & Cyber Security

Ants, Padlocks, & Cyber Security

If you’ve followed the news recently, I don’t need to tell you that cyber security is a topic of major importance today. It seems that every week there is another revelation of a security breach at an organization thought by many to be a leader in data and network security. The breaches span governmental agencies and virtually every industry. Ironically, even a company that makes its money selling security hacks to governments and other organizations was itself hacked and its secret hacking code made public!

What is leading to this seeming deluge of security breaches? Are the breached organizations simply falling down on the job? I would argue that it isn’t that simple. In fact, there are several factors that make securing networks and data today an incredibly difficult task.

Padlocks & Lock Picks

Consider for a moment the physical door locks that are employed around the world on every house, building, or car. Over the centuries, increasingly complex locks have been created. Instead of the simple keys from the past, we now have very complex, machine milled keys for our doors. Often, there is an electronic passcode as well. With all the improvement in lock technology over the years, we’ve certainly eliminated ...


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If I Text You Tonight, Will Your Analytics Text Me Back In The Morning?

If I Text You Tonight, Will Your Analytics Text Me Back In The Morning?

It’s a classic scenario. Two people meet at a party. They chat and then exchange information. However, they never speak or meet again. It is as though the contact information was never exchanged. So, what happened? Was there never intent to follow up? Or, did the information get lost, forgotten, or placed in a pile that never got acted upon?

There is a similar scenario I see play out often when it comes to text analytics. Most text analytics are focused on analyzing text right now for immediate tactical insights. Then, the text is virtually forgotten, if not literally deleted. Think about it. Sentiment analysis is a summary of general attitudes right now. Customer service organizations track feedback to identify emerging product problems. Marketing managers look at customer feedback to identify if a promotion is getting the hoped-for attention.

Whether social media, email, online chat, or transcribed phone calls, text is being used more and more frequently today. While the previously mentioned analytics absolutely provide value and are worth pursuing, there are additional opportunities that should not be missed. By throwing this information away after the initial analysis is completed, you may be missing opportunities to impact the business in very creative ...


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Discovery Analytics: It’s Not Hacking, It’s R&D

Discovery Analytics: It’s Not Hacking, It’s R&D

I spend a lot of time these days talking with companies about the need for a formal approach to enabling what is often called “discovery analytics” or “exploratory analytics.” What I find is that many people have a fundamental misunderstanding of what discovery analytics is all about. There is one analogy that I have found to be effective in getting people to better understand the concept. In this blog, I’ll walk you through that analogy.

It Isn’t Aimless Hacking!

Many people get very concerned when I begin to discuss discovery analytics as being not fully defined, constantly evolving, and remaining fluid. They tell me that what I’m saying sounds a lot like a mad scientist sitting down running random experiments in the hope of finding something useful. I do not espouse such an approach, I can assure you!

On the contrary, a discovery process should always start with a specific high priority business problem in mind. There should also be at least a general idea of how to address the problem effectively through analytics after some initial brainstorming. At that point, a discovery process is started to explore how well our ideas actually do address the problem. Typically, a discovery process involves one ...


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What Angry Birds Can Teach Us About Analytics

What Angry Birds Can Teach Us About Analytics

The past couple of years, my kids participated in The Hour Of Code. If you haven’t heard about the initiative, check it out. Basically, a wide range of Silicon Valley titans teamed up to provide kids with age-appropriate introductions into the world of programming. It is a very impressive program and millions of students participated in it this past year.

As I watched my kids go through some exercises to move Angry Birds characters through a maze or to fight a battle in a castle, I was drawn to the programming interface provided. It is called Blockly. I hadn’t seen it before, but it was a terrific way for kids to learn to program. Why? Because it allows them to visually focus on the logic that they needed to create as opposed to the syntax details of any given language. I’ll first explain this concept in more detail and then move into how it can help us with analytics.

What Is Blockly?

Blockly provides drag and drop icons (that look like blocks of course!) that the kids use to complete a given Hour of Code task. Perhaps the task is to get an avatar through a maze on the screen. By pulling “move ...


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All Is Not Lost: Finding Value In Marketing Attribution Data

All Is Not Lost: Finding Value In Marketing Attribution Data

In my last blog, I laid out some facts that call into question the extensive effort many organizations put into attributing individual customer sales to individual marketing touch points via common attribution methods. To summarize, Suresh Pillai, head of Customer Analytics & Insights for Europe at eBay, showed that all reasonable attribution algorithms led to effectively the same aggregate credit to each marketing lever and also the same credit as a random method.

If in many cases it isn’t possible to achieve a result different from a random allocation, is all lost? The answer is no. While the detailed customer data utilized for attribution may not be as useful as expected for traditional attribution purposes, it still has a lot of value. Let’s discuss how.

Leveraging Attribution Data

Data and, therefore, information, is valuable. I assume that anyone reading this will agree with that assertion. At the same time, any given piece of information may not be relevant or helpful for any specific purpose. In other words, information has value when placed in the right context. Suresh’s conclusion (and my own) as outlined in my April blog simply states that customer level data about which touch points preceded a purchase do not necessarily ...


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The Perils Of Marketing Attribution

The Perils Of Marketing Attribution

One of the hottest topics in analytics today is marketing attribution. Attribution, for those unfamiliar, is the process of assigning credit to various marketing efforts when a sale is generated. In the modern world, this is no easy task. There are myriad ways to touch a customer today and the goal of attribution is to tease out the impact that each touch had in convincing you to make a purchase.

Was it the email you were sent? Or the Google link you clicked? Or the banner ad you clicked when visiting a different site? Or the ad you saw with your video on YouTube? Or one of many other potential touch points? Or is it a mix? It is quite common today for a customer to have been exposed to multiple influences in the lead up to a purchase. How do you attribute the relationship?

The question is not simply academic because it has real world consequences. Budgets are set based on performance. So, the person in charge of Google advertising has a huge motivation to ensure that they get all the credit they deserve. Also, accurate attribution will allow resources to be properly focused on the approaches that truly work best. ...


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Analytics and Exponential, Unpredictable Growth

Analytics and Exponential, Unpredictable Growth

If I gave you the choice of winning either $1,000,000 or one penny doubled every day for a month, which one would you pick? The million dollars sounds pretty good, doesn’t it? However, by the time day 30 comes along that penny doubling will be worth more than $5 million due to the power of exponential growth.

When considering which new analytics to tackle, organizations often focus on what it will cost in the short term to build, test, and implement the various options and the expected returns that can be generated. There is nothing wrong with this, but in the age of big data, I’d like to suggest that there is another criterion that should also be considered: how much growth is expected in the volume of the data utilized for the analysis and how will it impact the costs and analytics processes in the future? The issue is not as simple as just storing the data, but also actually impacting the business with powerful analytics that must scale with the data.

Will the data required for a new analysis follow a linear, easy to predict growth curve or an exponential, impossible to predict growth curve? It is important to consider ...


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Is Your CEO Out of Touch or Being Misled?

Is Your CEO Out of Touch or Being Misled?

In January, The Economist revealed the results of a major study aimed at identifying how businesses that are successful at being data-driven differ from those that are not.

Some of the findings are quite expected, and there are a few surprises. For the most part, data-driven organizations seem to be doing a lot of the very things you’d expect: providing wide access to data, supporting the use of data for decision making, support from the top, etc. The infographic below summarizes what has been termed “the virtuous circle of data,” which is the path to successfully becoming a data-driven organization. Visit the study’s website to read the full report, watch a webinar, and more.



A Troubling Result

One specific result I’d like to delve into is the fact that CEOs have a much rosier picture of how data-driven their organizations are than do those down the chain. A few of the key statistics are:


While 47 percent of CEOs believe that all employees have access to the data they need, only 27 percent of all respondents agree that they do.
Similarly, 43 percent of CEOs think relevant data are captured and made available in real time, compared to 29 percent of all respondents.
CEOs are also ...


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