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 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 the 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 with 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 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.
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.
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I would suggest to read: “Blink. The power of thinking without thinking.” of Malcom Gladwell.
I think data can never give a complete picture, so don’t forget to use your intuition in decision making.
Book can be found here:
I thought I had read Blink a few years ago but i couldn’t find it on my bookshelf, so maybe i didn’t. Certainly there is a role for intuition or business instinct in decision making. The problem is decision makers tend to think their intuitive powers are much better than they actually are, the Dunning–Kruger effect. As flawed as some data may be, making decisions based on hunches is usually worse. Examples of incorrect intuition and bad decisions tend not to be publicized, although there are many famous examples in history, (http://www.summary.com/book-reviews/_/The-Dumbest-Moments-in-Business-History/).
Thank you for this post! I’m firmly in the camp of being data-driven. However, I’m struggling with one point from the opposing camp.
* Wouldn’t it be too natural to rely on data as crutch to avoid thinking or common sense?
* How would you really separate subject matter expert “intuition” from the statistics “intuition” used to produce the data analysis?
* How can we reconcile that different analysis on the same data can produce very different results and recommendations?
I’m starting to believe that the best way is to navigate fluidly between both sides: something like being data-driven to enhance expert intuition. I’m interested in your thoughts!
(1) I think of data analysis as a way to stimulate thinking about a problem. There are many twists and turns that can be taken in a data analysis, each of which requires some consideration of what the ramifications of an action might be. That takes effort. Turn your statement around and reevaluate it. Isn’t it more likely to rely on intuition and hunches as a crutch to avoid thinking or common sense?
(2) You need both, although both are likely to be subject to the the Dunning–Kruger effect.
(3) True, different analysis on the same data can produce very different results and recommendations. Usually, though, there is some bias by one or the other side that forces the different result. With a data analysis, an independent reviewer can look at both sides, figure out what each did, and find the flaws. With intuition, you can’t do that. It’s one person’s hunch versus another person’s hunch. Both data analysis and intuition may lead to different decisions but data can be fact-checked, intuition can’t.
Just some constructive criticism … Even though I agree with the premises of this article, the tone, especially the parts about why people avoid data, is way too harsh, dismissive, and confrontational. It even puts someone like me who is ostensibly on your side on the defensive.
Basically you’re undermining your own message here by sounding like a condescending technocrat instead of trying to actually bridge the gap and bring people into embracing a data driven culture in their organization.
Anyway it’s a good message, just need to clean out the tone.
Sorry, I didn’t mean to come off as harsh, dismissive, and confrontational … well, maybe the line about control-freak exec is confrontational, I’ll give you that. But just as it is difficult for intuition-based decision makers to understand the usefulness of data, it’s difficult for data-driven decision makers to understand the apparent randomness of playing hunches. What may sound harsh is the nature of the different thought processes.
Data scientists to CEOs: You can’t handle the truth
“… CEOs seem to have a rosier view of a company’s analytics efforts than directors, managers, analysts, and data scientists.”
“…The culture around how data is viewed and data-driven decisions are made has to change. If a data scientist brings all the assumptions and risks to a boardroom conversation only to get chewed up and spat out, the next time he enters that boardroom, he’ll be sure to hide the negative truths.”
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I’d like to ask you some advice.
I am a data engineer who is interested in service, data science.
There are 150 people in my company.
And last 1 year it seems there were no noticable service enhance. They work very hard but not focused on the fundamental problem. They just do work to do their work and still can not see the forest.
I tried to change the culture of company(by report, meetup, data tools development etc) but the result is not good.
Is there any good practice that some organization has successfully evoluated to data driven, data informed culture?
I like airbnb’s blog and I learnd much from them . but now I need more practical, realworld cases.
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