Watch the Attitude, Data Geeks

One-dimensional techies will be replaced by machines in the near future. So what if they’re the smartest ones in the room? If decision-makers can’t use data, does the information really exist?

Data Geeks
Data geeks may be the smartest people in the room, but maybe not if decision-makers don’t know what to do with their information.

Data do not exist just for data geeks and nerds. All of these data activities are inevitably funded by people who want to harness business value out of data. Whether it is about increasing revenue or reducing cost, in the end, the data game is about creating tangible value in forms of dollars, pounds, Euros or Yuans.

It really has nothing to do with the coolness of the toolsets or latest technologies, but it is all about the business — plain and simple. In other words, the data and analytics field is not some playground reserved for math or technology geeks, who sometimes think that belonging to exclusive clubs with secret codes and languages is the goal in itself. At the risk of sounding like an unapologetic capitalist, data don’t flow if money stops flowing. If you doubt me, watch where the budgets get cut first when going gets rough.

Data and analytics folks may feel secure, as they may know something in which non-technical people may not be well-versed in the age of Big Data. Maybe their bosses leave techies alone in a corner, as technical details and math jargon give them headaches. Their jobs may indeed be secure, for as long as the financial value coming out of the unit is net positive. Others may tolerate some techie talk, condescending attitudes, or mathematical dramas, for as long as data and analytics help them monetarily. Otherwise? Buh-bye geeks!

I am writing this piece to provide a serious attitude adjustment to some data players. If data and analytics are not for geeks, but for the good of businesses (and all of the decision-makers who may not be technical), what does useful information look like?

Allow me to share some ideas for all the beneficiaries of data, not a selected few who speak the machine language.

  • Data Must Be in Forms That Are Easy to Understand without mathematical or technical expertise. It should be as simple and easy to understand as a weather report. That means all of the data and statistical modeling to fill in the gaps must be done before the information reaches the users.
  • Data Must Be Small, not mounds of unfiltered and unstructured information. Useful data must look like answers to questions, not something that comes with a 500-page data dictionary. Data players should never brag about the size of the data or speed of processing, as users really don’t care about such details.
  • Data Must Be Accurate. Inaccurate information is worse than not having any at all. Users also must remember that not everything that comes out of computers is automatically accurate. Conversely, data players must be responsible to fix all of the previous mistakes that were made to datasets before they even reached them. Not fair, but that’s the job.
  • Data Must Be Consistent. It can be argued that consistency is even more important than sheer accuracy. Often, being consistently off may be more desirable than having large fluctuations, as even a dead clock is completely accurate twice a day. This is especially true for information that is inferred via statistical work.
  • Data Must Be Applicable Most of the Time, not just for limited cases. Too many data are locked in silos serving myopic purposes. Data become more powerful when they are consolidated properly, reaching broader audiences.
  • Data Must Be Accessible to users through devices of their choices. Even good information that fits the above criteria becomes useless if it does not reach decision-makers when needed. Data players’ jobs are not done until data are delivered to the right people in the right format and a timely manner.

Who are these data players who should be responsible for all of this, and where do they belong? They may have titles such as Chief Data Officer (who would be in charge of data governance); Data Strategist or Analytics Strategist: Data Scientist; Statistical Analyst or Program Developer. They may belong to IT, marketing, or a separate data or analytics department. No matter. They must be translators of information for the benefit of users, speaking languages of both business and technology fluently. They should never be just guard dogs of information. Ultimately, they should represent the interests of business first, not waving some fictitious IT or data rules.

So-called specialists, who habitually spit out reasons why certain information must be locked away somewhere and why they should not be available to users in a more user-friendly form, must snap out of their technical, analytical or mathematical comfort zone, pronto.

Techies who are that one-dimensional will be replaced by a machine in the near future.

The future belongs to people who can connect dots among different worlds and paradigms, not to some geeks with limited imaginations and skill sets that could become obsolete soon.

So, if self-preservation is an instinct that techies possess, they should figure out who is paying the bills, including their salaries and benefits, and make it absolutely easy for these end-users in all ways listed here. If not for altruistic reasons, for their own benefit in this results-oriented business world.

If information is not used by decision-makers, does the information really exist?

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.

5 thoughts on “Watch the Attitude, Data Geeks”

  1. Wow, Stephen, you tell it! Data geeks are in great demand these days, and command wonderful compensation packages, but they–like ordinary marketers–are in service to the business. Good reminder for us all.

  2. Lot of energy there Stephen and well-directed as usual.

    However many times this has been repeated, Lester Wunderman’s famous observation: “Data is an expense: knowledge is a bargain” sums it up perfectly. When the techies deliver knowledge that is usable, great! When they simply build elaborate data sand castles, no one benefits.

  3. Really nice points here. I am totally on board with all these points. Machine learning is usurping data geek authority and control already. It shines a light on creating business value from data. Geeks focus should be to curate the input and output of any ML process, to ensure analytical process is transparent and understandable, and that it is aligned with creating the business value that decision makers are looking for. If it is considered information, then it must have value. But if information is not used by a decision maker then it must have no value, and cannot be considered information. (If a tree falls in the forest, and no one is there to hear it, it makes no sound!) But the value potential is always there if the data is relevant to marketing goals. Tuning and prying into data, seeking value where others do not see it, will give decision makers a competitive edge. Attitude adjustment or not, business value can be found in data.

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