Two walls on the road to #datadriven and 3 ways to go across them

There are no organizations that are data-driven as a whole.

As there is a continuum of the maturity of data use in decision making across different companies, there is the same continuum inside of the organization, if it’s large enough and has long enough history.

I like to put that across two axes, decision making power vs. data analytics capabilities. It will usually look like this.

People, roles, departments in the company will be scattered all around. And usually, the deeper you are in the data the further the job description is from formal decision making.

The truly data-driven organization has both:

  • Access to relevant data, skill to process it, and critical mind to derive input for a final decision out of the raw figures
  • Influence over resources and processes that comprise core value stream of the company

This gives the #datadriven quadrant. Though the picture above is quite distorted: it’s usually quite a small box in any big company.

Now, in most cases, the majority of the people are in the other quadrants. How to get them into the right one?

Lots of sources go a long way explaining how to change people and get them a right mindset, so they get into the data-driven decision making heaven. It’s all great, but I have not seen a single person arguing against this, yet it’s hard to achieve on a sustainable meaningful level.

I see two reasons for that to be a challenge.

One is the time & tools & skills required to have data analytics capability — that’s the vertical line on the graph. Lowering that threshold takes the right toolset and a data accessibility program.

We have particularly great experience with Tableau, like many other organizations. I have seen same success stories with Qlik. There are other tools on the market that allow it, and the key is to lower the amount of time average business user have to spend on a tool to get needed data.

The second reason is the impact that particular function or role can have on the business outcomes.

There are teams that run reports and analytics, and who play a supporting role without power to influence business core value stream. The capability without decision power gives you a reporting factory: stating the facts and making a case for internal optimizations or cross-departmental communication.

There are fantastic examples of analytics done there. People who care about their job do have an insane level of deepness in their data work. It is daunting to see that the impact of their work and insights is very much underutilized.

Empowering these people is a complex organizational task.

Delegation of decision making to the proper levels is, perhaps, the most obvious step — but it requires removal of unnecessary controls while still maintaining predictable outcomes.

Putting analytics in front of the person who runs the process and needs to make a however small decision right now is what makes greatest impact. That’s where the embedded analytics and decision support plays in.

I find it helpful to structure all the projects around these two directions. It does not cover all the complexity, nor it applies to all and every situation. But that gives teams a good perspective on what we are trying to achieve.

P.S. There is another dimension, of course: investment into education, data literacy, hard skills. And no matter if you focus on toolset, data access, and embedded analytics, you should not forget about this. Recommended reading on the topic: Data Fluency: Empowering Your Organization with Effective Data Communication

What is your experience in your organization? Is your C-suite in the #datadriven state? Leave your comment — I’d like to hear your opinion.

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