Don’t Hire Data Posers

There are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world where the line between “real” and “fake” is ever-blurrier, some may not even care for such differences.

So, in the interest of not wasting too much time with posers, what should decision-makers and marketers consider? Allow me to share a few pointers here:

  • Buzzwords: Posers love buzzwords. They will change their existing presentations, relevant or not, to fit into the trend that is considered to be hot on the scene. In the past, “CRM” was the “open-sesame” word for a while. “Big Data” is still not dead yet, and “Personalization” is currently popular in many circles. “Insights” is the latest one, but most who claim to provide eye-popping insights — sometimes “automatically” with some “must-buy” toolsets — are at the level of stating the obvious in forms of colorful charts. When a title of an article, whitepaper, seminar or solution is filled with buzzwords, stay away from it. It is too bad that some real deals could get buried along with posers in the purging process.
  • Overpromise, Underdeliver: There is no magic bullet that solves all marketing and data problems single-handedly. Be afraid of the ones who claim that their method, toolset or solution will make all of your dreams come true. I haven’t seen a case where such a claim did not lead to disappointing results with busted budget allowance. Be very afraid of those over-promisers.
  • One-Trick-Ponies: Every data player has his or her specialties. Especially in a complex field like analytics, it’s very hard to be a master of just one aspect of it. But, that doesn’t mean that it’s desirable to have only one-trick ponies around. Yes, the game of analytics is a team sport, but we also need players who at least understand components other than their own specialties. Problem-solving is not just a series of statistical analysis or elaborate reports.
  • Toolset-oriented: Related to the previous point, too many data players are locked in one or two sets of tools. We all have our favorite toolsets, but no one can build a house with just hammers. What we need are creative types who can prescribe solutions to specific situations and challenges, not the ones who try to fit all types of problems into their toolbox.
  • Salesy: Even doctors push for certain services when they want to recover the cost of expensive equipment, but it would be unethical for doctors to perform medically unnecessary procedures for profit. Data players and analysts should be seen that way, too. Be aware of the ones who think of their profit before yours. I’ve seen consultants who just drag on and on with assignments, hiding behind some complex algorithms that would make only marginal differences in the marketplace.
  • Complicaters: OK, this isn’t a real English word, but I am sure you’ve encountered this type. They’re the ones who would make things overly complicated for no good reason. There are lots of math geeks and process-oriented analysts who would take a long re-route, wasting everyone’s time and resources (many would also like to “share” the details of their mathematical journey, too). Those kinds are highly likely to be data plumbers, getting satisfaction in moving data around, not necessarily in getting business results. In reality, answers that the decision-makers seek should be in their simplest forms.

If you are looking for data scientists, analysts, program developers, data manipulators, processing vendors, outsourcing partners, data strategists, consultants or whomever you need to work with to realize value out of data, do not just consider their external credentials, marketing collaterals and pricing models. Over-promises lead to disappointments, and blown budgets and timelines lead to blame-games.

In the middle of meltdown-level fiascos in the data business, there always is the incompetency factor. One may blame the process, team structure, platforms, software, methods or quality of data itself, but the data and analytics business is really a game of capabilities.

Imagine an orchestra that sounds really horrible. If the conductor just moves around the musicians’ seating chart and replaces all of the instruments, will they sound better all of a sudden? Never in 100 years, if musicians don’t know how to play the music properly!

It’s difficult to weed out the posers in this world. But we can at least try to nip some of them in the bud. Even analysts should not hesitate to call out incompetent ones, as posers eventually will give a bad name to the whole industry.

So start with cutting out the ones who cannot finish a sentence without using buzzwords.

Author: Stephen H. Yu

Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at stephen.yu@willowdatastrategy.com.

11 thoughts on “Don’t Hire Data Posers”

  1. Hi Stephen:

    I enjoyed this piece! Can you weigh in on your opinion of preparing for a career as an authentic data scientist by attending a boot camp after college, such as NYC Data Science Academy? My son is interested in pursuing a career as a data scientist, and he does NOT want to be a data poser.

    1. Get him started by browsing everything on kdnuggets.com. If he has enough drive, he can figure it out from there. I’d also look into general assembly.

  2. My trusted editor inserted the picture in the article, and I’ve already received emails inquiring what is wrong with the poser in it. I don’t generally admit that I am a Trekkie in public, but I happen to know the answer, so allow me to share it with readers of this fine publication. Basically her right Thumb should be separated and she shouldn’t tilt her head like that (Star Fleet Academy is no cheer leading squad). There is no communicator or badge of rank on her science officer’s uniform, either. In the Vulcan greeting ritual, it is also customary to say “Live long and prosper” along with that famous hand gesture. So, “Live long and prosper” to you (to which you respond “Peace and long life”.)

  3. You hang out with too many marketing people who have adopted these titles from those who actually do them. I’ve been to plenty of conferences and events where it’s obvious that they know what they’re talking about. I’ve made hundreds of contacts, from data scientists, analysts, DBAs, data engineers, data architects, MDM/data governance experts, hadoop/spark developers, etc. But never really found one that was in marketing, and nor have I met marketing people who actually have the background that the others mentioned do. It’s still two very different professions that have yet to be properly merged, but it’s difficult because marketers just lack the technical mindset.

  4. Great article Stephen, you are definitely not a poser or a complicater, and it is always a pleasure to work with you. I agree that fancy toolsets only help solve the problems that people use their head to understand, define and decide to tackle. Take care my friend.

    1. Thanks, John. I still remember some whiteboarding sessions that we ran together. Problem statement first, then data and analytics, not the other way around! Cheers!

  5. Great, great article Stephen –and who else would have written it 🙂
    “Caveat Emptor” –I’ll be sending your article to more than a few friends and Clients who’ve been victims of said posers.

  6. Pingback: Why Buzzwords Suck

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