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Big Data: Most Annoying Buzzword Of The Year

A Snarketing post by Ron Shevlin, Director of Research at Cornerstone Advisors

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.

Ron ShevlinRon Shevlin is Director of Research at Cornerstone Advisors. Get a copy of his best-selling book, Smarter Bank: Why Money Management is More Important Than Money Movement. And don't forget to follow him on Twitter at @rshevlin.

All content © 2017 by The Financial Brand and may not be reproduced by any means without permission.

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  1. Big Data is so overused nobody knows what it’s supposed to mean. Like when someone sends every email flagged as high priority. They might as well send them tagged as invisible.

  2. Tremendous post and a great follow-up to what I was trying to get across in my Financial Brand post last week (‘Big Data: Big Opportunity in Banking . . . or Big B.S.?’ Even though Gartner and IBM have tried to define the term, I have not met any two bankers who define the term in the same way (If you can’t define something, how can you build strategies around it?).

    In addition, while most would agree that ‘Big Data’ most likely involves unstructured data such as social media and mobile interaction, how can any organization grab for the next shiny object before they solve the issue of email address and mobile phone number collection, the integration of data silos that exist between retail, small business, commercial, mortgage, credit card and insurance product lines and leveraging the wealth of insight available in the payments area of the bank?

    Finally, there are many more pressing priorities today in banking than to expand the scope of analytics that are not being done extremely well on the foundational level at many banks. I would think investing is improved payment solutions, new branch distribution models and a better customer experience across channels would yield a better ROI in the short run.

    I enjoy writing and speaking about Big Data, but usually as it relates to doing better with what you have before trying to boil an ocean.

  3. Well said Ron (and Jim). You know what you’re talking about and say it as it is. Thank you.

    If you’ve worked in financial services long enough then you will realise that data has always been used within marketing. It’s shouldn’t be about hype, it’s about business and customers.

    If a payment scheme generating trillions of rows of data can find insight through using sampled data and make strategic decisions based on this then I don’t see why boiling the ocean (as Jim says) will ever be necessary. Market researchers only ever ask relevant sample populations for their input: ergo… ‘Data Scientists’ will never need to analyse all the data.

    A robust social media strategy should negate the need to constantly analyse all the unstructured data in real-time, retrospectively or predictively. I’m happy to be proven wrong on this one if anybody wants to correct me.

    If you can’t access and apply insight and proven value-add then you’re just playing BD/BS and not running an effective Marketing department.

    My thoughts on why using data is important, but it has little to do with the BD/BS hype.

  4. Social media has transformed what has traditionally been a 1-on-1 communication between a customer and a bank to a 1-to-many broadcast from the customer to the whole world which merely pertains to and includes the bank. For the bank in question, it’s really not big data but, for all its competitors, it’s surely an entirely new source of a massive amount of data that was never available before. Nothing wrong in using it, I think. To take a concrete example, let me use our social intelligence platform to pull up a few tweets that bear negative sentiment about the respective bank / insurer mentioned in it:

    1. Angry_BaldGuy: Invested 2.5L in HDFC Life And today after 3 years my fund value is lesser than what I had invested. Should have put my money in FD instead.
    2. bbharadwaj: HDFC bank doesn’t issue a private ltd company a credit card for 6 months, how do internet startups work around this to pay for aws?
    3. pradyotghate: after 30 minutes on the phone and multiple attempts, ICICI Bank rep tells me that their system is down and PIN cannot be generated #FAIL
    4. sruthirk: Dear ICICI Bank, how do you plan on doing address verification if there’s no one at home?

    I don’t deny that a bank / FI should make investments in improving things in a lot of foundational areas. I may even concede that they might eventually yield ROI. But, why is it wrong for this bank / FI to, in parallel, target each of the above persons dissatisfied with their respective banks / FIs and try to win them over to itself? Although it might amount to guerilla marketing that not all banks / FIs might be culturally comfortable with, this one costs much less and delivers much quicker results compared to many foundational improvements. I know a bank that does exactly this using a combination of a social intelligence platform and a small team of people working in what it calls its “digital customer acquisition team” and it has achieved quite a bit of tangible business results in a very short time and with OPEX-only costs.

  5. Excellent post and especially liked if you create a Chief Data Scientist Officer position, I will hit you in the head with a hockey stick.

  6. Thanks, Steve. I don’t want come across as advocating violence, but… 🙂

  7. Rena Cheskis-Gold says:

    As a trained Social Scientist (i.e., Sociologist, Economist, Demographer, Political Scientist, etc.), I am amazed at the sudden interest in ‘Data Scientists.’ So, with all the whining that people with liberal arts degrees aren’t prepared for jobs, now we’re going to make up a new job that does….exactly what a social science degree trains one to do, but you’re not going to hire us? I’m going to go count up the reasons this bothers me and make a chart out of it.

  8. Rena: Thanks for your comment. Another research firm (whose name I won’t mention) recently predicted that Big Data would spur the creation of a ton of new jobs. I find it hard to believe that Theater majors are the best people to fill those roles. 🙂

  9. Nearly Normal says:

    Here’s a thought. The sample size analysis of which you speak presumes that there are reams of data on which to build a model, and then to reapply. This is always looking in the rear view mirror. Accuracy issues aside (please don’t imply that what marketers do today is a super-successful science; it’s still a bit of voodoo with a huge diversity of people, minds, systems) this will also be slow in recognizing patterns because you will by definition trail data collection.

    Also, it depends on how you analyze Big Data for your organization. As a bank, if I could sense a growing negative sentiment on a daily basis, then I could — the very next day with the help of marketing, digital banking and potentially compliance teams — respond with something useful. Not only in my social media, but across my channels. Instead of waiting for retrospective data, or for someone to alert me. There are several budding “flagging” tools that monitor such unstructured data today. It’s not just about negative tracking, it could also identify positive patterns and allow faster product packs and customizations.

    I’m completely in agreement that Big Data is neither happening nor vital in the next 5 minutes before banks learn to “walk”. But the potential power of “running” with Big Data is hard to ignore.

  10. Nearly Normal says:

    Theatre majors are the perfect fit, some on! 1.73 billion Google hits for “Big Data” clearly aren’t enough. I think this industry needs some drama.

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