It’s hard to be a data-driven organization – Here is why!

Data Science   |   
Published April 26, 2016   |   

Why is it so Hard?
Do you work for a data-driven organization or one that claims to be a data-driven organization, or one that wants to be a data-driven organization? You probably do, whether you work for a big retailer or a small service provider. Every organization wants to believe that they use the information to make decisions in an unbiased manner, although not every organization actually does that. It’s definitely not easy getting to be a real data-driven organization. At a minimum, an organization has to address five issues:
Funding. Being data-driven is a top-down decision because it must be supported by adequate funding. Without funding, all you can do is talk about how you’re data-driven. Talk is cheap; funding is commitment.
Data. Organizations should have standard processes that generate relevant business data of appropriate granularity and quality. There should be owners for each type of data who are responsible for the data quality, availability, and security. Small organizations can implement these concepts in less elaborate ways than large organizations. For example, one person may oversee all data operations in a small organization compared to a department of experts in a large organization. Even micro-sized organizations can have ready access to data. All it takes is an internet connection that allows searching for data and analyses others have posted.
IT Support. Generating, storing, accessing, analyzing, and reporting on data requires software and hardware resources, connectivity technologies, and communications capabilities. Again, one person can do everything or there can be a whole department of technicians supported by vendors and contractors. An organization just has to have enough consistently available support that it can rely on.
User Skillset. To be of any use, data has to be converted into information, and information into knowledge. One person can do everything but it’s better if there is a team of data scientists because no individual is likely to be familiar with all the different types of data analysis that might be appropriate. In an ideal situation, all employees would have some knowledge of data analysis techniques, even if it’s just a required statistics course they took in college. It’s easier to run a data-driven organization if everyone understands the roles data and business analytics have in their daily work and the organization’s objectives.
Decision-making Culture. The most important aspect of successful data-driven organizations is the attitudes of individuals making decisions. If they would prefer to rely exclusively on their intuition to run their organizations, the organization won’t be data-driven no matter how much funding, data, support, and employee skills there are.

Why do some individuals avoid data?

It may seem counterintuitive that some people avoid using data for their decision-making. They will guess, speculate, make assumptions, and argue for hours about matters that could be resolved quickly and convincingly by using data. They’ll follow hunches to decide what they want to do and then claim success based on little more than a few cherry-picked anecdotes. If you suggest looking at data, you might be asked: “what do we need data for?” They’ll caution you against “information overload” and “paralysis by analysis.” They might tell you “that’s not what the big boss wants.” They’ll find all sorts of excuses. In the end, you can lead your boss to data but you can’t make him think.
Why do these people avoid collecting and analyzing data to address problems, especially in the current age of pervasive technological connectivity? There are a few possibilities.


Some people actually have a fear of information, possibly related to a fear of numbers (arithmophobia), technology (technophobia), computers (logizomechanophobia or cyberphobia), ideas (ideophobia), truth (alethephobia or veritaphobia), novelty (kainolophobia or kainophobia), or change (metathesiophobia). More likely, they might fear that they are incompetent to make a decision, perhaps associated with the Peter Principle. They might say “Let’s do it the way we did it before,” or “let’s not rock the boat.”


Some people just aren’t comfortable with numbers. Artists, for example, tend to be more comfortable with creative spatial and visual thinking compared to engineers who tend to be more comfortable with logical and quantitative thinking. Perhaps it’s a right-brain versus left brain phenomena, perhaps not. Think of how you make a major purchase. If you compare specifications and unit prices for each possible brand or model, going back and forth and back and forth, you’re what is called an analytical buyer. If you just buy the product in the red box because it has a picture of a cat on it that looks like one you own, you’re what is called an intuitive buyer. The same goes for decision-making. Some people trust their hunches more than they trust numbers.


Some people aren’t accustomed to solving problems with data. They don’t know how to collect and analyze data. They wouldn’t even know where to start. They might talk to a few co-workers for anecdotal information but wouldn’t know how to generate representative data. They don’t know that data may already exist. They don’t understand how readily available some information is on the Internet. Even then, they wouldn’t know how to use data to make the decision. They might defend themselves by saying available information is not actionable.


Some people just want to control everything they can. They might already have a preferred decision and don’t want any information that might call their hunch into question. Or, they may not know what they want to do but they don’t want any information that might limit their options or prevent them from controlling the debate. They may be control freaks. They may be subject to biases attributable to illusory superiority like the Dunning–Kruger effect.

How can reluctant decision-makers be encouraged to be data-driven?

If you’re in an organization that is making the journey to being data-driven, changing the culture of decision-making will be your most formidable obstacle. The easiest problem to fix is ignorance. Training, encouragement, coaching and mentoring, and peer support combine to enlighten. The fears and inherent natures of some decision-makers are harder to address. Again, encouragement and personal support will encourage change. Control freaks are the most problematic. They are intransigent, as any of their exes will affirm. Don’t make them a focus of your efforts to change your decision-making culture. You’ll be disappointed.
Here are some actions you can take to support the adjustment.
If you work in upper management, the most important thing you can do is communicate your expectations and lead by example. Recognize that not every decision must be based on data. Sometimes data is just the starting point for a visionary leader’s intuition. Make funds available for actions that will support the initiative, like training in data analysis and decision-making. Require managers to at least bring data with them to the table when arguing their points. Challenge speculation. Help them through the process of incorporating information into their decision-making process by coaching and mentoring. Finally, recognize and reward staff members who take the lead in using data.
If you work in middle management, you’re probably the primary focus of the cultural change your company is trying to make. The most important thing you can do is accept the inevitability of the change and recognize you don’t have to do it all yourself. Communicate to your staff what things they can do to support the new decision-making strategy, like collecting and analyzing data. Approve funds for staff training and data collection/analysis activities. And again, recognize and reward staff members who take the lead in providing you with data.
If you work as a member of the staff, the most important thing you can do is collaborate with your co-workers in collecting and analyzing data. Help each other. Congratulate those who provide good examples of data collection, analysis, and reporting. And of course, take as much training as you can and use your initiative to interject data into activities you are working on.

Be patient

Be patient
Changing an organization’s culture from intuition-based decision-making to data-driven decision-making is a long evolutionary process. It won’t happen by the end of next quarter, or next fiscal year, or for that matter, maybe ever. You won’t necessarily even know when you’ve achieved the goal. But, if you start to see that decisions work out better and are more defensible than in the past, you’re probably there. That’ll make everyone in the organization happier.
This article originally appeared here. Republished with permission. Submit your copyright complaints here.