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.
Call me old-school, but at least in some fields, I believe that “the ability to do things” still matters. Analytics is one of those fields. When it comes to data and analytics, you either know how to do it, or you don’t know how to do it. The difference is as clear as a person who can play a musical instrument and one who is tone-deaf.
Unfortunately, there is no clear way to tell the difference in this data and analytics field. It’s not like we can line up contestants and ask them to sing and be judged here. Furthermore, “posers” often have louder voices — armed with fancy visuals and so-called automated toolsets.
I’ve been to many conferences and sat through countless presentations in my lifetime. It may sound harsh for me to criticize fellow data players and presenters, but let me just come out and say it: A great many presenters and panelists at conferences are posers.
How do I know that? Easy. I asked them. For example, when I stalked some panelists who preached about the best practices of personalization after the session, the answers were often “Well, it is not like we do all those things for real …” Sometimes I didn’t even have to ask the question, as I could tell something is seriously broken in their data and promotion chain by observing their marketing messages as a customer.
The bad news for the users of information — and for consumers, for that matter — is that it takes a long time to figure out things are not going fine. Conversely, we can all tell who is tone-deaf as soon as a singer opens her mouth. It is so hard to tell the difference between a data scientist (i.e., an analyst who provides insights and next steps out of mounds of data) and a data plumber (i.e., supposedly an analyst who moves big and small data around all day long and thinks that is his job), that I admit it sometimes takes a few months — generally after some near meltdowns — for me to figure it out.
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.
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 email@example.com.