SemAngel

Crikey! What the UK Election Fiasco Tells us about Enterprise Voice of Customer

I won’t pretend to be an expert on UK politics and even less on UK polling. But in the wake of the disastrous performance of pollsters in the UK predicting the outcome of the general election there, I think it’s worth reflecting on the lessons to be learned. If you’re not familiar with the broad storyline, it goes something like this. In the days leading up to the election, pollsters were reading a toss-up between the incumbent Conservatives and the Labour party with expectations of a divided Parliament and much confusion. It didn’t go down that way, with the Conservatives winning a flat out majority of seats in a fairly decisive victory.

Now, the polling wasn’t as far off as that simple story may imply. There is a powerful disconnect between seats and raw voting percentages (as witnessed strikingly in the Scottish elections). You can win much less than 50% of the vote and still win far more than 50% of the seats, meaning that poll numbers aren’t necessarily reflective of seat wins. But there’s little doubt that the pollsters had the election quite wrong.

After the election, Nate Silver (who I happen to think is pretty frigging great at this stuff) and team weighed in with a series of blogs which discussed the errors in their model. There’s a ton of interesting stuff in this discussion, but of particular interest to me was the following quote:

“Polls, in the U.K. and in other places around the world, appear to be getting worse as it becomes more challenging to contact a representative sample of voters. That means forecasters need to be accounting for a greater margin of error.”

[There’s also a supporting and fascinating redux by 538’s Ben Lauderdale which shows how a huge part of the error in the prediction was driven by a seemingly very reasonable choice about which of two alternative questions around party voting likelihood would better represent actual preference. 

They chose to use (as seems reasonable), a much more specific version of the voting question, but it turned out that the very general version was closer to capturing reality. I’m not sure there’s a clear lesson here (I doubt it’s always true that the general form of the question will work better) EXCEPT that if you have two seemingly similar questions that yield very different results, you’d best beware!]

If you’re a data analyst, I’d expect these discussions into the mechanics of voter modeling to be pretty fascinating. But of far more importance to non-analysts should be the troubling implications of Mr. Silver’s take on polling and its declining accuracy. Because here’s the thing – his comments apply just as surely (maybe even more surely) to opinion research done for commercial purposes. There are real differences between political opinion research and our commercial variants. But they both suffer from the growing challenges of getting a good sample.

In fact, when you get right down to it, the biggest difference between polling for political work vs. commercial research is that the political pollsters have a real proof point. When the votes are counted, they know if they were right or wrong.

Your enterprise survey research almost certainly doesn’t have a proof point. If you’re voice of customer opinion research is fundamentally skewed, how would you even know?

Not only does that tell me that your commercial polling likely has all the same errors as the political guys, it probably means it’s far worse. Why? When you don’t have a proof point, you’re that much less incented to make sure you get the results right and you have far less opportunity to correct your mistakes.

I strongly suspect that the work that team’s like Silver’s do is more careful and better than the overwhelming majority of the survey work done in the commercial sector. If that’s true, and if they are having a hard time getting it right, think about what it means for enterprise voice of customer research.

I often get significant push-back from enterprise analysts skeptical of online voice of customer. I get that. And what I’m saying may be reasonably taken as grounds for that skepticism. But those same skeptics aren’t taking at aim at the increasing challenge of getting accurate VoC results in the offline world. I’m pretty sure that commercial opinion research is significantly less accurate now that it was twenty years ago (just try random digit dialing these days!) and it may well be harder to get a representative sample offline than online. Certainly, I see no grounds in today’s world for assuming the opposite. Getting a good sample is hard and getting harder. Without a good sample, you are in constant danger of drawing the wrong conclusions from the data. And don’t even start on that tired and utterly incorrect idea that you protect yourself from this by “just looking at trends.”

So what’s the solution?

First, I think people need to reevaluate their biases around offline vs. online surveys. Traditional attitudes around online surveys and their biases revolve around a couple of issues that I think are largely historical. Back when in the days when intercept surveys first became popular, it was understood that online samples were unrepresentative of the broad population. True then, but nowadays online populations in the U.S. are probably quite a bit more representative out-of-the-box than is easily obtained from most traditional techniques (canvassing, mail, phone and mall intercepts all have huge biases these days). Of course, putting a survey on your own website automatically introduces a significant selection bias. But it’s now routine and easy to pop surveys on 3^rd party platforms that eliminate that bias entirely. Many social media platforms, of course, do have significant biases in their user population and likely in your fan base. But given the incredible reach of platforms like Facebook, there’s no reason why you can’t build out excellent samples based on the top social networks. In all these cases, what you retain in the online world is the ability to collect large numbers of respondents very rapidly and with far less cost than with traditional techniques. I’d the be the last person in the world to argue that online voice of customer isn’t challenging. But it's a bit frustrating to see the offline world get a free pass on the same or worse set of problems when it comes to sampling.

Second, people need to find a proof-point for their voice of customer data. If you’re going to pay attention to it and let it influence your decision-making, you need to find ways to test its accuracy and predictive power. This isn’t just important to your peace of mind. It’s important because without those proof points you have no way to improve your VoC. Go read Lauderdale’s description of the likelihood to vote questions they used and tell me which you would have chosen! Not only will establishing proof points give you a clear path to improving your Voice of Customer research, I venture to suggest that it might also push you to improve the actionability of that research. If your opinion research is too fuzzy to yield predictive models, it problem isn’t very interesting to begin with!

Unlike Silver and his mates, you won’t find much discussion in the enterprise space around the accuracy of our enterprise brand tracking and product surveys. It isn’t because they are better. It’s because nobody knows if they are any good at all and without being forced to, nobody is anxious to put them to the test.

When it comes to enterprise VoC, perhaps it’s time to call an election.

London's Digital Analytics Hub

[I'm going to be in London the first week of June for the Digital Analytics Hub - running workshops on Segmentation and Data Modeling and participating in the Conference. I took a few minutes to answer some questions from the organizers around my sessions and the Conference in general. Check it out and if you are EU-based, check out the Hub - it's a great event.]

Hi Gary, we are looking forward to welcoming you back to the DA Hub. 

One of your workshops at this year's DA Hub is named "Getting Digital Segmentation Right". The name implies that many organisations do not get segmentation right. What is the reason and what is missing in most segmentation frameworks?

GA: Well, digital segmentation is hard. Segmentation in general isn’t trivial – even when you have great variables to work with. But over the years, people have gotten pretty used to segmentations that are based on business relationship (high-value customer, reseller, etc.) and demographics. What’s challenging is that those segmentations aren’t usually available or easy to recover in digital and, even when they are available, aren’t all you need. Building segmentations from digital data turns out to be fraught with challenges – and most often falls victim to the fact that many of the digital variables we use lack meaning (page views, time on page, etc.) and others are strongly influenced by the structure of the website.

I plan to cover techniques for getting more information out of digital behaviors so that segmentations come out much richer, how to think about visit vs. visitor segmentations, and some of the ways you can approach anonymous segmentation.

 

As a discussion you picked the topic of "Getting Budget for Digital Analytics." Does that imply that analysts should develop a degree of sales skills? Who do you expect to attend this discussion?

GA: I’ve sure had to build out my sales skills over the last fifteen years. Back in 2008, my business partner and I used to joke that we’d never made a sale to someone we hadn’t already worked with. That’s definitely changed.

But when I think about this topic, it’s really for the manager of digital analytics. Any manager/director/vp role in the enterprise is largely about politics and sales and it doesn’t matter whether you’re the manager of analytics or manager of toilets flushing; that’s probably why lots of people don’t want to rise to those roles. So I don’t think selling is an unusual ask.

Having said that, I’m not thinking of this session as Sales 101 though I do expect that some people will have useful tips and for those of us to whom selling isn’t second nature that can actually help quite a bit. A huge part of selling though, particularly when you aren’t a natural, is figuring out how to demonstrate value to the organization. When you have a big impact, you’re more likely to get budget. When you democratize effectively, you’re more likely to get budget. In other words, part of this discussion is going to be about how to make sure you make the biggest impact possible AND how you make that impact known. I expect to focus a lot on communication of value – but I wanted to make sure that people understood why that communication is essential to their own personal interests.

 

You launched X Change in 2007 as a purely conversational conference. It is still a pretty unique format for an analytics conference. We have been running the DA Hub with that format in Europe for the past couple of years. Can you tell us in your eyes how the format has evolved over those years and some of the differences between the Conference in Europe and in the U.S.? 

GA: 2007 seems like a long time doesn’t it! Here in the U.S., I think a couple of things stand out in terms of the way the Conference has evolved. Probably the most important change hasn’t been anything internal to the Conference – it’s been the gradual evolution in maturity of digital analytics practice. Enough small changes add up to a fairly giant shift, and when I reflect on the knowledge and sophistication of the practitioners and the programs they represent today compared to 2007 it’s an immense change. That’s important because in a conversational conference, the sophistication of the participants is a huge part of the experience.

I’ve also seen a dramatic shift in where that expertise is lodged. Back in 2007, most of what expertise there was existed in boutique consultancies. Today, the vast majority of expertise is on the enterprise side. That’s not because we vendors have gotten stupid or lazy – it’s just reflective of that same growth in maturity. There ought to be a lot more expertise on the enterprise side because that’s where the most practice is. I think that shift helps the format too. One of the things I’ve always tried for in the U.S. X Change is to create a no-sales, no pressure, truly collegial environment. Vendors generally do a great job of helping with that, but frankly it doesn’t hurt that these days the enterprise folks can drive those conversations themselves.

When I think about the differences between the Hub and X Change, it often comes down to the same types of things: in the EU, the practitioners are a little less mature and more of the knowledge is on the vendor side. That’s changing, but it takes time.

 

Finally, this year we have 60 discussions, our highest number ever. Which three or four discussions are you most looking forward to?

GA: Regular readers of my posts will probably laugh to hear that my sessions include:

• Integration of Web Analytics Data into your Customer Data Warehouse,
• Defining Your Audience – Segmentation, Cohorts & Engagement”, and
• A/B & MVT – Do you use it to Fix or Define your Strategy?

Not like I ever talk about these topics, right? In fact, I think I’m better prepared for some of these other discussions than I am something like "Getting Budget for Digital Analytics" that I'm leading.

 

The DA Hub is taking place on June 2nd and 3rd at Stamford Bridge stadium in London, home of newly crowned English champions Chelsea FC. Join Gary at the conference or sign up to one of his two workshops on June 1st (part of the DA Hub analytics and conversion rate optimisation training day). For more information and to register visit the website at www.digitalanalyticshub.com

Analytic Roles in the Enterprise – What’s with the Taxonomy and Meta-Data Systems?

In my last post, I took a shot at defining what a data science role actually is or might be. My goal wasn’t to try and figure out from the hugely varied, largely confused and often contradictory public discourse a definition based on consensus. Instead, I wanted to layout my own vision for the actual roles required in an organization to do analytics and see if any of them fit the broader, fuzzy data science term reasonably well. Given that approach, I had to define all the roles involved in enterprise analytics efforts (outside of IT and program management type stuff), which was perhaps a little bit ambitious. Here in snapshot form is what I came up with:

 

Based on this spectrum of roles, I advocated for a definition of the data scientist role focused on the “Statistical ETL and Analytics Foundation”. This role uses advanced analytics to describe the fundamental patterns in the data which are then standardized in a higher-level data model and consumed and used to drive subsequent business analysis. In digital, this type of work would be the creation of the visit-types (what we call a Two-Tiered Segmentation) or units-of-work that describe what the low-level web or mobile behaviors represent. With something like the IoT, this would be the core interpretation of what the usage pattern meant (e.g. for a fitness app whether a pattern represented somebody climbing stairs or riding an escalator).

What I like about this description is that it identifies a somewhat new type of advanced analytics activity that’s deeply related to big data and isn’t well done or represented in the enterprise. In other words, it helps fill a real gap in the analytics organization and isn’t just a fancy and expensive rename for your existing analysts. I also like this because it removes much of ambiguity about what you need when you hire a data scientist. You don’t need a data journalist, a database architect, an ETL specialist or a full-stack analyst. Ideally, you need a pretty strong statistical modeler with knowledge of pattern-matching or algorithmic techniques appropriate to your data and enough sense or experience to understand the types of patterns that might be important. I hope that sounds a little less impossible to find.

Surprisingly, I didn’t get a huge amount of push-back on this definition though there was a fair amount of debate about the use of the term data scientist at all. What I did get, though, was an interesting question around one of the other roles: Taxonomy and Meta Data Systems.

Here’s the query:

“I was wondering if you could expand upon the "Taxonomy & Meta-Data Systems" section a bit for me as I'm not sure I understand exactly what you are referring to. I'm not really sure what is being classifying here and what the meta-data is referring to. How does it differ from the ETL in the previous step? Would it be possible to provide concrete examples relating to this step?”

This role wasn’t central to my thesis and, on re-reading the post, I have to agree that I didn’t do a great job explicating it.

Before I take a crack at explaining the Taxonomy role, I want to caveat that I’m not sure that meta-data and taxonomy are everywhere as important or complex as they are in digital. I’m not sure they aren’t either – but much of what I’m going to talk about is highly specific to digital.

So here goes.

There’s a presentation I developed for X Change that delves into the foundation of digital analytics. At the most fundamental level, digital analytics works by assuming that the content people consume was CHOSEN by them and is indicative of their preference. This assumption is profoundly important. To illustrate how this assumption changes the metrics and approach we use to understanding behavior, I use a little example I call Conan the Librarian (with thanks to imghumour.com).

Before Conan can read (some librarian!), here’s his view of the world.

 

This is the equivalent in digital analytics of metrics based on page views, time spent, and visits. Using these simple metrics, we can get a certain kind of insight into our patrons. But mostly, we are just fooling ourselves.

Once Conan learns to read, he gets a totally different view of the world:

 

In a world where content consumption is driven by user choice, understanding the CONTENT is critical. Conan’s understanding of his patrons after he learns to read is like night and day. He understands what users want, what else they might like, and much, much more about who they are. Things like whether they are in school, have kids, are checking-out for someone else, or are going on vacation just fall out of the content understanding. What’s more, he understands that many of his conclusions about patrons based on the simple consumption metrics were simply mistaken. The “heaviest” reader is actually checking out books for their child. The “light” reader is consuming hard, serious literature.

Learning to read is, here, just a proxy for understanding the content that’s consumed and then self-selected. Building and developing that content understanding is what I mean when I describe a role for meta-data and taxonomy in the analytics organization.

In the digital world, this role is especially important because of the amount of content we have to classify and the potential complexity of that content. We frequently analyze sites that push 100’s or even 1000’s of pieces of new content live each week. Most of the sites we analyze have tens of thousands of pages that can be consumed or hundreds of thousands of products. If you’re going to analyze and understand users of those sites, you need to understand lots and lots of content.

Now I know what a bunch of you are thinking. I have this done already. Or (even worse), this is just data governance. No you don’t (unless you’re a Netflix equivalent) and no it isn’t (no matter who you are). You may indeed have a basic content classification on your site: topic and sub-topic or category and sub-category. That isn’t nearly enough for good analysis. In a post I wrote some time back, I outlined a bunch of different approaches to taxonomy that most people ignore. Give it a read.

But keep in mind that even this only scratches the surface. For most Websites, content is the engine that does the work. You should never stop thinking of ways to classify it. Do you use enough verbs? Is your reader just getting started on a topic? Is your content written at the right reader level (this post comes in at a paltry 10th grade level)? Does the model in the picture have the right color hair? Is the column length right? Does the user care more about brand or price? Is the number of off-links appropriate? Is the ratio of images to text right? Is the product appropriate for gifts? Is the emotional tone appropriate for patients? Is a buyer a luxury shopper? Should a call to action be persuasive or exclamatory?

If you want to answer these questions…if you want to answer almost any question about users and content effectiveness via either analytics to controlled experimentation, you will need to explore and develop taxonomies. That’s not a job for data governance. It’s a job for analytics. Its purpose is analytic. Its understanding is analytic. And, since you mostly won’t we doing manual classifications on large sites, its methods are analytic.

That’s why I’ve carved out an explicit role in the analytics team for meta-data and taxonomies. At least when it comes to digital, I want someone whose sole job is thinking about how to classify content, who is constantly testing for new and innovative ways to do that, and who will make those categorizations available to the data scientists and business analysts who can use them.

If the data science role I outlined around statistical ETL and pattern-building is a significant gap in most analytics teams, this role around meta-data is even less well served and yet, in digital, is every bit as fundamental.

By the way, I couldn’t end this piece without highlighting a comment by EY’s own Micah Greer on the data scientist. “It's a bit of a cheap shot, but even the title of the role is poorly formed, as no proficiency in the enterprise of science is required. Unusually Smart Data Engineer maybe?”

Indeed.

May we all be (like Micah, I might add), unusually smart data engineers!

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(Re)Defining the role of the data scientist

(Re)Defining the role of the data scientist

In the last couple years the term data scientist has become an accepted part of the broad analytics space. Everywhere I look, I see clients building data science teams and looking to hire that most elusive of breeds - data scientists. While I have been (and remain) highly skeptical of the term itself, I’m not opposed to the broader shift. As absurd as it may be that renaming a job role from statistician to data scientist triples its cost and increases its voice, it seems to me that this new reality is a better place to be – and not just for data scientists but for the organizations that hire them. Analytics and data-driven decisioning deserved a stronger voice in the enterprise and the correspondingly increased rewards that entails. If grafting a new title onto the role helps create that reality, then I, at least, don’t see much harm.

It does trouble me, however, that many of our clients don’t have a strong grasp on the role of these data scientists in the enterprise and how that role fits within existing analytics groups. It also troubles me when organizations create massive disparities between data scientists and analysts who are working on the same teams and who are largely equal in value. One pernicious consequence of over-investing per-capita in data science can be the loss of all your best analytics talent that isn’t blessed with the right title. That isn’t a good thing at all.

In this post, I want to lay out in broad terms the roles in a modern analytics team, how they fit together, and which, if any, might reasonably be described as data science. This isn’t something I’ve worked through into a polished deliverable, but here’s a quick mental snapshot of the core analytics functions (not including PM, Management, IT support, etc.) common in today’s enterprise:

 

Intake & Production ETL: The intake of data into analytics systems and the initial base ETL to ready them for use as well as the ETL necessary to operationalize analytics. This function is heavily IT focused and is often done with either traditional ETL tools (like Informatica), with Hadoop open-source processing engines (like Sqoop or Storm), or with SQL engines (including Hive).

Taxonomy & Meta-Data Systems: Someone has to manage the taxonomies, hierarchies and meta-data that drives analysis and reporting in the organization. That someone is all too often a blank space in the enterprise, but as companies begin to recognize the critical role that robust classification plays in the enterprise this role becomes more real. IT organizations have traditionally owned meta-data infrastructure and business has usually owned meta-data definition. But analytics is the proper owner (and key stakeholder) in the definition, creation and maintenance of classification systems.

Statistical ETL & Analytics Foundation: Transforming big data into usable data for the enterprise almost always involves the statistical interpretation of patterns in the data to make them understandable. Exploring and then locking down these patterns into the core building blocks for analysis is tackled by these folks. In a way, I’ve been exploring the role of statistical ETL and building an analytics foundation in this whole broad series. If you don’t standardize the approaches to these big data streams, not only will every analytics be much harder to execute, but each will be built on a shifting surface that makes comparability and repeatability nearly impossible.

Descriptive Analytics & Reporting: With Descriptive Analytics & Reporting, we begin to actually deliver analysis to the business. This role is a traditional digital/business analytics role whose function is to explore data, answer business questions, and drive optimization and controlled experimentation in the enterprise. The tool-kit for this is typically data visualization and basic statistical analysis (cross-tabulation, correlation, variation) as well as our core digital analytics SaaS toolkit.

Model-Based Analytics (Descriptive, Predictive, Prescriptive): Here at EY we tend to describe analytics maturity as moving from descriptive to predictive to prescriptive and to imply that these are fundamentally different techniques. If taken as a statement about the level of maturity around the use of analytics, I think this kind of makes sense. But I’m not really a fan of it when taken as a statement about types of analytics (where the techniques often blend together) nor do I fully accept the underlying and implicit assumption that prescriptive models are more valuable than descriptive models. It may be that a model that is explicitly prescriptive is more intuitively easier to operationalize, but a customer segmentation that is purely descriptive can be used to drive very powerful personalization. To me, the salient role here is that of building business models, be they regression models, decision trees, clustering models, SOMs, etc. And whether or not they are descriptive, predictive, or prescriptive it doesn’t seem to me that the role much changes. I'd say the same thing, incidentally, about "machine learning". Some people take a broad enough view of what constitutes machine learning that it would clearly fall in this category. Others might be tempted to define it as a narrower set of statistical analysis techniques. But even with a narrower definition, I see no reason why it wouldn't or shouldn't fall in this same role and domain.

Data Journalism: At the very best analytics organizations these days, there’s a recognition that the analysts who fulfill these two previous roles (Descriptive & Model Analytics) often aren’t well suited to build the communication vehicles for the data to the rest of the organization. What’s more, there’s a growing realization that the communication of the analytics to the organization is singularly important and is most definitely NOT solved by data democratization. Hence the growing understanding of the need for a data journalism role that involved the compact, powerful communication of analytics findings to consumers in the organization. This role is more often about writing, graphic design, and communication than analytics expertise, but it also demands practitioners who are comfortable with and passionate about data.

Most enterprise analytics organizations already have ETL programmers, descriptive analysts and report builders and modelers of one sort or another. What’s commonly lacking are taxonomy/classification owners, statistical ETL creators and data journalists.

Does the term data scientist fit any of these open roles?

It’s possible to think about a data scientist as a “full-stack” analyst who crosses all of these functions. It’s also reasonable to think of a data scientist as a cross between the last four roles (Statistical ETL, Descriptive Analytics, Modelling and Journalism) or to think about a data scientist as someone who is comfortable with newer machine learning algorithms but who would otherwise probably be described in the same bucket as the modelers – basically as a new tech statistician.

It’s subjective of course, but I don’t like any of these definitions. The idea of a full-stack analyst who spans ETL to Data Journalism is a pipe-dream. Sure, there may be a few people in the world who can do that. Good luck hiring one. Enterprises create departments and functions precisely because most people don’t excel at widely different tasks and, secondarily, because it’s rare that the integration of those tasks outweighs their differential value. If you have someone who’s really good at building models, how much time do you want them to spend on traditional ETL or reporting?

On the other hand, the statistical ETL role can plausibly be described as a data science role. In fact, I think it’s probably the best way to think about data science because it captures a gap in the common organization and skill set of today’s enterprise that is deeply linked to current big data analytic problems.

Here’s an example of what I mean by statistical ETL and how this role might play out.

Today’s utilities are struggling with the one of the paradigm cases of big data – smart meter readings. In the old days, of course, a meter-reader would come around every few months and write down how much gas or electricity you consumed. That data point – 3 month usage – is traditional data. Analysts don’t need help or assistance in baking it into reports or models. With smart meters, however, the situation has changed. Smart meters send very frequent (every 5-15 minutes) updates of usage to create a stream of detailed data.

On the one hand, it’s no trick to use that detailed data either. You can just sum it into a 3 month number and you have exactly the same data you’ve always had without a meter reader to source it (this kind of summing doesn’t work with digital data – one of the reasons why digital is such a good paradigm for big data). Still, one can’t help but feel that summing up 15 minute readings into 3 month readings isn’t the only or best thing you could do with this data.

By having the data in small time increments, we can ask and answer questions that were never possible before. Questions like which customers use unexpectedly large amounts of power on the weekend, have insufficient insulation, should change from electric to gas appliances, would benefit from a space heater, or should get up a little earlier on the weekend suddenly become approachable. These questions aren’t ANSWERABLE in the original meter reader data but they might answerable with smart meter data.

Answering those questions, however, isn’t primarily a traditional model building exercise. Instead, it’s an exercise in pattern matching. I’ve written before that the fundamental difference between traditional data and big data is the introduction of sequence, time and pattern into the basic level of analysis and this is a perfect example. No individual meter read has any meaning. But by measuring the pattern of reads, it’s often possible to understand the underlying behaviors in considerable detail. For example, by looking at the pattern of gas and electric consumption around dinner hours on moderate temperature days, you could identify houses that use electric vs. gas appliances.

Not only is this type of analysis very different in technique from traditional modeling, there is strong reason for it to be done as a foundation for subsequent analysis. In digital, the two-tiered segmentation we recommend is the fundamental set of building blocks that analysts can then use to model other business problems. You wouldn’t want each analyst to make their own visit intent segmentation. You want your best pattern-analysts to identify the patterns, instantiate them over the data, and then provide them to the business analysts and modelers to use in subsequent reports and models.

It’s the same with smart meter data (or IoT data or any other type of big data). The pattern building exercise is distinct from the business reporting and analytics exercise and it fits pretty darn well our intuitive understanding of what a data scientist is.

All sorts of people call themselves data scientists. Enterprises can call any type of analyst or group their data science group. It’s just semantics. But if you’re looking to understand where in the analytics value-chain your organization has a glaring and potentially fatal gap, my guess is that it’s right here in this foundational activity of creating the fundamental building blocks of analytics based on detail data that needs to be patterned.

Understanding this gap and defining it as THE role for data science makes it clear exactly what skills you need for that role (data journalism isn’t one of them) and what kind of person would suit. It also, I hope, helps you assess the relative value of this role and weigh it more reasonably against the other skills in your analytics group. People who can do this type of work are harder to find than people who can do basic ETL or reporting and maybe even harder to find than good modelers. But they aren’t all that exotic and they aren’t clearly more valuable than a good business analyst who can take numbers and apply them creatively to the business to build an optimization program. This is a case where knowing what you need can help you get what you want and, perhaps, even do so a little more affordably.