Category Archives: Big Data

Data Storyology

It’s conventional wisdom by now that, with all the data we have to analyze, we have to find the “story.” Experts like Tufte have done wonders to improve our capabilities regarding data visualization and presentation — but that’s different from the understanding the story that the data is telling.

A recent HBR blog post titled How to Tell a Story with Data offers the following points of advice: 1) Find the compelling narrative; 2) Think about your audience; 3) Be objective and offer balance; 4) Don’t censor; and 5) Edit, edit, edit.

My take: My points of advice differ. And I think we more rigor (dare I say methodology) regarding data storytelling.

—————

I don’t have an issue with “find the compelling narrative” and “think about your audience” but these points are actually part of a broader process that the article doesn’t define.

Think of data storyology — the art and science of telling stories with data — as having two broad components: 1) Finding the story in the data, and 2) Telling the data story.

If I were to draw a picture, it would look like a yin/yang diagram, not a flow.

Finding the story in the data is an iterative process that involves utilizing data management and statistical tools to cut and analyze data. But in also involves applying human judgment and experience to figure out what the “story” is.

The HBR blog author describes “finding the compelling narrative” as:

“Giving an account of the facts and establishing the connections between them. The narrative has a hook, momentum, or a captivating purpose. Finding the narrative structure will help you decide whether you actually have a story to tell.”

I wish he would have left that last sentence off. If you find a narrative structure, you have a story. Whether or not that story is worth telling is a different issue.

Finding the narrative structure is more than “giving an account of the facts and establishing a connection,” however. In fact, the “account of the facts” is probably the least important part of the story because it’s the part that many people either already know or think that they know.

The interesting part of the narrative is the why, who, and when (more so than the what).  The “what” is the plot, but the “why” is what gives the plot some depth. And just as poor character development in a book diminishes the quality of the book, leaving out the “who” in a data story produces an incomplete (and potentially boring) story.

—————

Finding the story is just the Yin part of the equation. Telling the story is the Yang.

This is where the “think about the audience” part comes in. Good data storyologists (or data artists) often define or uncover multiple stories in the data. Those stories likely have different levels of appeal to different audiences. Telling the story starts with defining who the audience is for the data story, and which of the data stories that were defined is most relevant, or how those stories tie together.

—————

At this point, however, my opinions veer from the blog author’s.

Telling the data story is anything BUT being objective and balanced. Data storyology is about educating, influencing, and motivating people. As a data artist, the last thing you want to do is be objective and balanced. You want to draw upon your insights, opinions, and experience — which are all subjective — to tell the best story. The article says that “a visualization should be devoid of bias.” Perhaps a point for future discussion, but I think that this is simply impossible.

The article also says that “Balance can come from alternative representations (multiple clustering’s; confidence intervals instead of lines; changing timelines; alternative color palettes and assignments; variable scaling) of the data in the same visualization.”

First off, this is a very narrow interpretation of “balance,” in that relates to just visualization. Data storyology is about more than just data visualization. Visualization is not the story.

In addition, I would encourage any budding data storyologist to “censor like hell.” The absence of censorship equals data dump.

—————

With a story and an intended audience, there’s still the art of telling the story.

A number of years ago, the analyst firm I worked for brought someone to train us on the art of storytelling. Still one of the best training sessions I’ve ever had.

The story trainer told us to think about the development of a story in terms of the story’s impact on the audience’s mood, and to strive to achieve the following mood pattern:

To summarize, think of the story development as: 1) Stuff is happening (neutral mood), 2) Things are going to get worse (or the things that are happening will cause problems, doom, despair) 3) Stuff happens or will happen to make it all better.

Story example: Little red riding hood is walking in the woods (#1), she gets captured by the big bad wolf (#2), she gets saved by the Woodsman #3).

Data story example (in financial services): Consumers are fed up with paying the high cost of checking accounts (#1), new providers are coming into the market to steal banks’ customers and drive profitability even lower (#2), banks can deploy new technologies and marketing analytical techniques to provide new forms of value to consumers to retain them and make them more profitable (#3).

—————

All the talk about the rise of data scientists misses the boat, in my book. We need people who can take the data, and not just find the story in the data, but the tell the story in a way that educates, influences, and motivates people. That’s not science — it’s art. It’s data storyology.

Gonna Manage Big Data Like It’s 1999

I started working at Forrester Research in 1997. In retrospect, I think I got hired not because I demonstrated great potential to be an analyst, but because my boss and colleague needed a sucker to join the team and write a report that nobody else wanted to write.

So I joined Forrester and wrote my first report on the hot topic of the day: Knowledge Management (it was a terrible, terrible report).

I interviewed executives from about 50 companies about what they were doing about knowledge management. What I heard was confusing. For the most part, what these companies were doing with IT and data was pretty much what they had been doing for the prior 10 years.

What was different (in 1997), was that now these initiatives were called “knowledge management initiatives.”

There were two key success factors (or barriers) critical to the success of knowledge management initiatives: 1) employees with the right skills in knowledge management, and 2) management support and commitment.

After all, sucking the “knowledge” out of people’s heads and making it available to everyone else in the organization wasn’t easy, and wouldn’t be successful if management didn’t sufficiently invest in it.

—————-

Roll the clock ahead 16 years and you’ll find that nothing has changed. Except the labels.

In a creditunions.com article titled Big Data At A Growing Credit Union, an interview with a credit union executive went like this:

Q: Can you define Big Data?

A: Not really. But in a way, Big Data is what people have been doing all along — looking at and analyzing data. I don’t know the tipping point where a credit union moves from generally looking at data and is suddenly in Big Data.

Q; Can’t data also overwhelm and slow decisions?

A: It can unless you achieve the balance of talent and training. If you put the right data in the wrong hands you can be swimming in that data forever. You’ve got to get people to the point where they understand what’s relevant and what’s not, and that takes time.

Q: What do you feel is critical to success with Big Data?

A: You have to have directors and senior managers who are supportive and understand there are revelations this data can provide.

You’ll pardon me if I can’t help but think that this all sounds vaguely familiar.

—————

If you can’t define Big Data — other than saying it’s what “people have been doing all along” — you are not going to get management support for the efforts.

If you think Big Data represents a different way of managing your business, but you can’t articulate that difference to your employees, you will not get broad employee support for the efforts.

Management is usually willing to fund some initiatives to try something promising. Employees, on the other hand, are generally loathe to change unless the pain of the existing is too much to bear. You might argue that they’re willing to change if the potential upside is appealing enough, but I’m not so sure about that.

Jumping on the management fad bandwagon is a prescription for failure. It trains employees to put everything they want to get funding for under the fad banner, and diminishes whatever potential value really lies in the core of the new concept.

————–

My take: You won’t find anyone more supportive of using data to make business decisions than me. But the path to becoming more data-driven doesn’t mean managing like it’s 1999 and jumping on the fad bandwagon. 

2013: The Year Of The Digital Wallet

Having named Big Data the most annoying buzzword of 2012, you might be wondering what I think the early candidates for most annoying buzzword of 2013 are.

Actually, I know that nobody is wondering, but that’s not going to stop from me telling you (after all, what is the purpose of a blog, if not to push one’s own ideas on the rest of the world, whether the rest of the world wants them or not).

So, here you go: In 2013, everybody and their mother will launch a digital wallet.

Mass adoption of mobile payments (in the US, at least) isn’t quite here yet, but that won’t stop tons of technology providers, financial institutions, and large retailers/merchants from offering mobile apps that they will call digital wallets.

In fact, I might just refer to my blog as a “digital wallet.” It”s digital, and allows me to store things (ideas), and isn’t that what a wallet does? Let you store things?

—————

Bank Systems and Technology ran an article on 5 Predictions About the Future of Digital Wallets that bears some analysis. Included in its list of predictions are the following:

“By some estimates, 70% or more of all e-commerce transactions are abandoned at checkout, valued at roughly $18 billion a year. There are many reasons for this … But no one likes the 16-digit hunt-and-peck. No one. Which is why the onramp for digital wallets is likely to come, not in the form of mobile transactions in cafes and gas stations, but rather, online, because, ironically enough, that’s where the friction is.” — Tech writer Jeffrey O’Brien in a recent Mashable.com article

My take: Disagree. First off, people don’t abandon carts online because they have to type in their credit card number. They abandon for a number of reasons, one of which is that many online retailers only show the best price for an item if you put in the cart. Second, if people didn’t want to do the “16-digit hunt-and-peck, they could easily store their card number online, and let it be auto-filled. I’m not doing that, but hey, you can if you want.

—————

“Banks should think twice before going down the path of launching their own branded independent wallets. For some, it might make sense, but many others will likely be better off focusing on making their payment credentials available and top of wallet in the wallets already out in the market, as well as enhancing and extending their mobile banking platforms with value-added services, including payments.” — Senior Analyst with another research firm.

My take: Damn, I hate to publicly disagree with a competing research firm. But I am.

What value-added do banks provide their customers today? Security? Guarantee that money will be moved when it’s supposed to be, and there in the account when it’s supposed to be?

Those are pretty low on the Maslow hierarchy of banking value. People want added convenience to do the things they do, and help making better financial decisions, big and little. That’s the promise of a digital wallet — convenience and advice. If banks don’t brand their own digital wallets, someone else will — and deliver more value to consumers. I didn’t say that every bank had to build their own digital wallet. But providing and branding their own wallet will be a big battlefield over the next few years.

—————

“It’s about providing an entire shopping experience and ensuring people get relevant offers for what they are buying. And that doesn’t happen unless you have data.” — PayPal President David Marcus

My take: 99% right. The first and third points (about the shopping experience and having data) are spot on. But people don’t care about getting “relevant offers.” That’s marketing-speak.

People care about getting the best price for what they’re buying, and choosing the best product for them when there’s a choice. That’s the shopping experience. If a better price is available somewhere, people want to know. If the digital wallet offers them the ability to find the better price, then people will use the digital wallet.

The question is: How does that “better price” get into the digital wallet? Well, it could be price comparison functionality. Or someone could make an offer.

The notion of relevance is misunderstood. Marketers have delusions of relevance. Relevance can’t be quantified (and therefore not measured).  It’s a highly subjective, and worse, transient condition.

—————

So there you have it. My prediction that 2013 will be the year of the digital wallet.

I know I’m going to be right. 

Today, everybody who wants to push Big Data slaps that label on everything that happens (regardless of whether or not it’s really “big data” or not). Next year, I’ll be able to claim that everything that FIs, retailers, and merchants do in the area of shopping and banking falls under the banner of Digital Wallets.

Now, if I could only figure out what the stock market is going to do.

Data Scheissentist

[Note: You might want to look up the word "scheisse." I'd suggest starting with the Urban Dictionary].

In an article titled What is a data scientist and do you need one?, eConsultancy asks “So what does a data scientist do, why are they so coveted, and does your company need one?” The article quotes IBM’s definition of a data scientist:

“A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.”

My take: Bullschiesse. (Or, per Electrouncle, “Bullenscheiße”).

The definition implies that today’s business (or data) analysts don’t have strong business acumen, and/or don’t have the ability to communicate findings to business and IT leaders in a way that influences decisions.

Nonsense.

There may be business/data analysts that don’t have the business acumen and communication ability, but there are plenty that do.

However, by stating that “what sets the data scientist apart” is business acumen and influence, IBM creates a specious argument. How do wannabe-Data Scientists prove that they have business acumen and influence? If it’s like qualifying for social media guru status, I guess all one has to do is publish a blog post heralding the transformative effects of Big Data.  

—————

The eConsultancy article attempts to distinguish between data scientists and analytics experts. According to eConsultancy:

Data scientists: 1) May be involved in the design and development of systems that collect and process large amounts of data using tools like Hadoop and programming languages like R; 2) Need to have a deep understanding of statistics and probability; 3) Are capable of designing and testing predictive models; 4) Provide the greatest value by answering the questions “Where are we likely going?” and “What would we need to do to go somewhere else?”; and 5) Will realistically need to acquire a high level of domain expertise.

Analytics experts: 1) Analyze smaller or more specific sets of data typically collected by third-party tools; 2) Primarily use existing services and applications that provide visualizations of data collected; 3) Do not require a formal scientific background; 4) Are best capable of answering the questions “Where have we been?” and “Where are we today?”; and 5) Should have some domain expertise.

My take: My experience (from working at database marketing firm Epsilon, and from interacting with analytics people in various marketing services providers in my role as an industry analyst), suggests otherwise.

The Analytics folks I’ve worked with easily meet all the criteria in the definition of a data scientist (with the exception of having expertise in Hadoop). They’re hardly limited to small(er) or specific data sets, data collected from third-party tools, or using “existing” services. Most of the Analytics experts I’ve worked with have a PhD in Statistics, which I imagine meets most people’s definition of a “formal scientific background.” And the Analytics experts I’ve worked are generally focused on the question “What should we do?” not “where have we been?”

—————

There is one area in which many of the Analytics experts I’ve worked with fall short, however. They’re not particularly good at incorporating “new” types of data into their models. By “new” I mean data like channel behavior, as well as qualitative, social media-like data.

Analytics experts like to test the value of new sources in order to prove that the predictive value of a new source is better than existing sources before they willy-nilly change their predictive models to incorporate additional data sources.

However, it is also my experience that the people in many organizations who understand what the “new” data is (especially the social media-related data) don’t have the first clue about how to build a predictive model or use one to drive marketing decisions.

—————

Bottom line: Data Scientist isn’t really a new role. The concept represents an integration of existing roles (business analyst, marketing analytics expert, market researcher, etc.), but most importantly, it reflects the need in many companies to incorporate new types of data, analyze that data, and then use the data to make business decisions. 

The idea that someone is going to magically come along and satisfy the criteria established by IBM in its definition of the role is fantasy. 

Even if this person did come along (and brought along some equally qualified friends), where in the organization would they report? Into IT? Marketing? Don’t you dare suggest creating a Chief Data Officer position reporting to the CEO.

Achieving the implied goals of a data scientist requires better integration between IT, marketing, market research, web analytics, and marketing analytics departments. 

Instead of hyping BS buzzwords, I’d love to see consulting firms and technology vendors help companies achieve that integration. 

Big Data: Most Annoying Buzzword Of The Year

In 2011, I awarded the Most Overused Word in the Marketing Lexicon award to “analytics.” A year ago I wrote:

“If analytics was overhyped and overused in 2011, just wait until next year. 2012 will be the year of Big Data.”

Sure enough, Big Data is the most annoying buzzword of 2012.

——————————

Do you have a spreadsheet with 100 rows and columns? You’ve got Big Data!

Do you monitor mentions of your company in social media channels? You do Big Data!

Do you know how to spell the word data? You’re achieving cross-channel synergies by deepening customer relationships and radically improving marketing ROI through Big Data!

—————

Ironically, you’re not likely to find a bigger fan of data- and analytics-driven decision making than me. But every use of data is not an example of Big Data.

There are two questions you should be asking yourself:

1) What the hell IS (and isn’t) Big Data? and 2) Why haven’t more managers become more data- and analytics- driven before?

I’m not going to bother trying to answer #1. That’s for consultants to demonstrate their thought leadershi+.

But question #2 deserves some analysis (pun intended).

—————

For ages, marketing has been dominated by the branding and advertising disciplines — and not by database, or quantitative marketing.

So why, all of a sudden, would the availability of heretofore unavailable (e.g., social media) data change this?

The answer is that too many marketers are searching for the next new thing, or silver bullet, that will solve all their problems, and create order of magnitude improvements in marketing performance.

Which never happens. Never. Ever.

That doesn’t stop marketers from searching, and it certainly won’t stop consultants and technology firms from coming up with buzzwords in order to find stuff to sell to marketers.

—————

There’s another reason why Big Data isn’t what it’s trumped up to be.

It may be an simplistic way of looking at marketing, but the two components are Sense and Respond:

“The ability to sense consumer needs and intentions based on their behaviors and actions, and to respond with appropriate advice, guidance, and offers.”

Predictive analytics (which is what Big Data is supposed to deliver or provide) can certainly enhance marketing’s ability to sense.

But there’s another part of the equation: Responding. In which of the multitudes of channels that consumers use should be a message be delivered? When? And in what sequence?

Throwing more data, and types of data, at the problem is no guarantee that both sides of the equation will be improved. 

—————

An adjunct to the whole Big Data mishigas (look it up) is the discussion regarding the rise of Data Scientists.

Apparently, this is the hot new job title/career field, and according to one clueless blogger, data scientists will grow up to be CEOs in the near future.

Yeah, right.

Question: How many people have gone from Market Research to the C-suite in large organizations?

Answer: Not a whole helluva lot.

There are a lot of reasons for this. One of which is this: People who rise to the c-level in large organizations generally have P&L responsibility in their background — especially where the P is a lot greater than the L. A finance background is probably the exception to that rule, but there’s a close connection to overall financial and stock performance in that job, so it helps those folks get to the c-level.

What P&L accountability does — and will — a Data Scientist have? None. We’re talking about glorified marketing researchers here. Not that I’m disparaging that (or them) one bit. I’m not just the president of the Data Scientists Club, I’m also a customer.

But we’re not getting to the CEO level in any business organization that exists on this planet. The skills required to lead organizations are very different from the skills needed by data scientists.

Oh, and if you propose the creation of a Chief Data Scientist Officer position, I will hit you in the head with a hockey stick. Because it’s a stupid idea, and because I miss hockey. Basketball sucks.

—————

So, is Big Data totally useless? Of course not. 

There are plenty of opportunities to make smarter business decisions by using new and different types of data. 

But it will take years — years — for companies to develop and integrate “big data” competencies in their companies. The claims of Big Data ROI that are thrown around are BS — complete and total BS. 

—————

Congratulations, Big Data. You’re the winner of the 2012 Snarketing 2.0 Most Annoying Buzzword of the Year. Here’s hoping you don’t repeat in 2013.

The Gut: The OTHER Biological Data Storage Device

In an article on the HBR blog titled Marketers Flunk the Big Data Test, the authors write:

“A recent study of nearly 800 marketers at Fortune 1000 companies found the vast majority of marketers still rely too much on intuition — while the few who do use data aggressively for the most part do it badly.”

The article goes on to state that “on average, marketers depend on data for just 11% of all customer-related decisions.”

My take: Nonsense. The gut — i.e., intuition — is getting an undeserved bad rap.

—————

First, let’s deal with the “11%” claim. This is a nonsense number. What exactly is a “customer-related decision”? Question: How many decisions did you make this week? Answer: You can’t possibly come up with a reasonable number, because: 1) decisions come in all shapes and sizes, and 2) you don’t consciously think about the decisions you make on a day-by-day, hour-by-hour, minute-by-minute. 

In truth, I bet the toughest decision you made this week was what to have for lunch. 

So for marketers to say they “depended” on data for just 11% of “all” customer-related decisions begs the question: Who’s tracking what is and isn’t a customer-related decision? The answer, of course, is no one.

—————

Second, let’s go to the statement that “marketers still rely too much on intuition.” 

This begs the question: How much is too much? Is there really a correct balance between intuition and data?

The bigger — and more important — issue here is that the authors (and I would guess, a lot of other people) don’t really understand what intuition is. 

Intuition is data. 

You gut is a data storage device.

While we like to think of our brains as computers that can store and access data, we should think of our guts as a data storage device — but what our gut stores is unstructured data.

Our gut remembers when we tried something that no one thought would work, but did. Our gut understands human nature in a way that can’t be quantified.

It’s ironic that Big Data proponents often explain Big Data in terms of unstructured data, but then dismiss marketers’ use of their intuition.

Intuition isn’t “winging it” or randomness. It’s the sum of our experiences. Our intuition may be wrong — but then, so might be our analysis of the “quantifiable” data (or just “data” in the terminology of the article’s authors).

—————

The authors make an interesting (but unsupportable) assertion:

“In today’s volatile business environment, judgment built from past experience is increasingly unreliable. ”

Why is it “increasingly” unreliable? Was it ever reliable? How can they prove their statement?

The implication of their statement — if it’s true — is that marketers need to rely more on data. 

But there’s a problem here: Data just doesn’t appear. Someone has to decide to collect it, store it, analysis it, and use it. How do they decide what to collect, store, analyze, and use? Well, I guess, they could run tests to determine which data elements are most effective in decision making. 

Database marketers certainly do this a certain extent. But if it’s new types of data — like the Big Data proponents talk about — then someone has to make a decision about what to collect in order for it to be tested. Again, I have to ask: How are they going to make their decision?

The answer, of course, is their intuition. Their gut belief about what will work and what won’t. If they’re smart they’ll adjust their gut instincts if they fail. 

But, you see, there it is again: It’s the gut that’s storing the critical data elements to make decisions. 

(Motley) Fools Shouldn’t Write About Big Data

MSNBC published an article titled What Banks Can Learn From Google and Facebook with the subtitle “Data mining is still a stumbling block for banks, but tech companies could point them in the right direction.” The author writes:

“Big Data is huge right now, but many companies are struggling to harness the potential of this new phenomenon. Chief among those slow to adapt to new methods of parsing information are banking institutions, which are in danger of being left behind in this brave new world of data management. Despite the myriad uses Big Data could provide for the banking industry, these institutions are somewhat sloggy in their implementation of the technology. Not only would using such information take the guesswork out of the creation of banking products for consumers, but it can also be used for risk reduction. For instance, banks such as Ally Financial have used data-mining consultants to help reduce losses on subprime auto loans.”

My take: The inconsistencies and unsupportable assertions in this article make it an embarrassment to high quality journalism.

First off, exactly what proof does the author have that financial institutions have been “sloggy” in their deployment of Big Data or data management technology? None, of course.

The bigger problem, here, is that — once again, like so many other articles polluting the Internet — the term “Big Data” is used without any definition or qualification, as if its definition was universally understood and accepted. Which it isn’t.

In addition, the author refers to FIs’ sloggy implementation of “the technology.” Exactly what “technology” would that be? BI software? Data management apps? Customer databases? Web analytics tools? If she’s referring to these technologies, than FIs have hardly been “sloggy” in their deployment of them.

It’s nice that she offers an example of how FIs could use “big data” to “take the guesswork out of the creation of banking products for consumers” as if credit card issuers haven’t — for decades — been using statistical analytical techniques to identify prospects and the types of cards they’d be most likely to accept. 

It’s also nice that Ally Financial (it must kill them to be called a “bank”) is using data-mining consultants to help reduce losses. I bet that they’re the only financial institution in the United States that uses consultants! Of course, do I need to mention that that just because Ally is using data-mining consultants, that doesn’t mean that what they’re doing qualifies as “big data” consulting. Which we don’t know, because the author didn’t define what Big Data is.

Later in the article, the author writes:

“Financial institutions aren’t the only ones that grapple with the best way to manage and utilize vast amounts of data. A new study by data management giant Oracle notes that many companies are aware of Big Data’s potential but are at a loss regarding the best way to use the mass quantities of data they are now gathering.”

MSNBC takes pains to criticize financial institutions for not capitalizing on Big Data, but readily admits that FIs “aren’t the only ones that grapple with the best way to manage and utilize vast amounts of data.” If FIs aren’t the only ones, then why single them out?

And even further in the article, we find this passage:

“Google and Amazon.com stand out as companies that really know how to gather and utilize data. Google, for instance, was instrumental in the development of Hadoop, an open-source platform that excels at analyzing data, as well as MapReduce, used in conjunction with Hadoop. And who can forget the fine Google paid for gathering too much information for Street View, not to mention the latest $23 million assessment for tracking Safari users?”

I completely agree that Google and Amazon stand as firms that know to gather and use data. But by citing the fines that Google paid for the misuse of that data, doesn’t it make financial institutions look good for not engaging in consumer unfriendly tactics that could violate privacy rules?

The author throws a bone to Citibank by reporting:

“Other banks (besides Ally Financial) are beginning to explore new possibilities in this area. Citigroup has been surveying users on its Facebook page on their feelings about social banking on the site, presumably in preparation for offering its services in conjunction with the social-media site sometime in the future.”

And this has what to do with Big Data? Nothing, of course.

Bottom line: Hey MSNBC! Tear down this article! (How do you like my Ronald Reagan impression?). It’s a poor excuse for business journalism.

Big Data Delusions

Avanade announced the results of a survey of more than 500 business executives and IT leaders, which revealed that:

“The investments companies are making to manage big data are paying off. Eighty-four percent of respondents believe big data helps them make better business decisions. And 73% of companies have already used data to increase revenue by growing existing revenue streams (57%) or creating entirely new sources of revenue (43%). Evidence shows big data has become pervasive – more employees in businesses have greater access to increased technology options for managing and analyzing data.”

According to the study, however, “the benefits of big data come with meaningful challenges:”

“Eighty-five percent of respondents report obstacles in managing and analyzing data, including being overwhelmed by sheer volume to data security concerns to not having enough dedicated staff to analyze the data. The majority of stakeholders (63%) also feel their company needs to develop new skills to turn data into business insights.”

Oh really? These widespread challenges didn’t seem to hinder the majority who think they’re making better decisions and generating more revenue.

My take: This is complete and utter delusion.

The advent of Big Data is way-too-new for a majority of firms to: 1) have made investments; 2) have deployed solutions; and 3) have measured the impact of those investments, and then claim that they’re making better decisions and creating new revenue streams.

—————-

Contrast the findings from this Avanade study to the eConsultancy study I reported on recently. eConsultancy found that only about one in three execs believe that web analytics, analyzing social media comments, or analyzing customer surveys to be very effective in helping them discover problems or issues with the customer experience.

If these approaches aren’t that effective, then what exactly are respondents to the Avanade survey doing differently to make such great decisions and drive up revenue?

—————-

The real problem here is the lack of definition for Big Data.

I don’t doubt that “more employees in businesses have greater access to increased technology options for managing and analyzing data.” But how is that Big Data? Maybe what many of those employees are accessing is Little Data, or Medium Data.

Bottom line: Misuse of the term Big Data has become so pervasive that it practically renders the concept meaningless.

I do doubt, however, that that will stop technology vendors and consulting firms from making exaggerated claims, supported by dubious research.