Welcome back! If you’re new: welcome, to catch up, check out part 1 of this mini-series here. In last week’s post, we started out on a path to understanding why some data-driven endeavors hit a dead end or at least run into a lot of friction with some people in a company. We figured it’s a mixture of perception of data and analytics, leadership, culture, and change processes. Enough with the issues, let us look at solutions.
As you might have realized from the first post, we’re going off on a bit of a tangent here to see what an ideal world for data-driven business transformation might look like. In some cases, the picture I am painting may look a long way off, in others, we might be a lot closer. What is important to me, is that this is not meant to be off-putting. Establishing a data-driven business approach in business area or a subunit, and then grow it from there is far better than not doing anything at all. In a later post, we’ll look at how you can inspire people to buy into the data-driven practice, and generating Quick-Wins is an integral part of that. With that out of the way, let’s begin!
For novel ideas, behavior, and approaches to flourish we need to create a fertile ground that will support and sustain the change we envision. In the case of becoming data-driven, a few aspects have to be anchored in a company’s culture. I will focus on three that we have seen to be most important. These are definitely not exclusive to success but form a solid foundation. And here’s the disclaimer: I am describing cultural states that are helpful to encourage and enable data literacy in a company. Cultural change is HARD and requires a bit more than what will follow. Consider what is discussed as supplemental to the grand vision. Forming the strategy to realize that vision, is what will have to happen afterward. So let’s get cracking with our attributes…
To help data-driven projects to succeed, company culture should be open. What this means with respect to data is, that we should all embrace and welcome that data – though governed – is widely available. This means that accountability will increase and therefore responsibility will have to as well. This would only be a negative thing if we were to employ personnel that would use data to dig up dirt on a rival or get an edge for a promotion. Any such toxic behavior must be shut down and sanctioned immediately. It is important that it is a common understanding, that everyone profits from open and widely available data, to make fact-based decisions. If you were required to quickly assemble a car for a customer project or by management request, it would be detrimental if you were required to jump through hoops for every wheel, nut and bolt you needed to build the product. Or even worse, have someone sit on the stack of steering wheels, afraid to give them out because he might have made a less than perfect one or is scared to lose control of making or storing steering wheels. An open culture with responsible people is very important.
Openness needs to be structured, planned and governed. It does NOT mean anarchy. If we provide an open playing field, we need to spark curiosity and enable people to use the room they have just won.
An inquisitive culture will sustain curious people. It pains all our hearts to see the explorative, discovery features of tools like modern data analytics solutions go unused. If you need information in an instant, KPIs and dashboards are a true help, but the real value is hidden in looking behind these numbers. Daniel Kahnemann once said “No one ever made a decision because of a number. They needed a story.” And that sums it up perfectly. We need to encourage and enable people to follow the white rabbit. This means: give them skills but also resources, especially time, to be inquisitive. Dig deep into data, understand how data works, maybe how to extend the current data set to see the whole picture. A target culture should be inquisitive.
Should you think, “Yea, but not <insert name here> from accounting” – remember: people are curious. It is what separates humans from other species. Everyone is driven by a hunger to find out more, to go somewhere, or learn something new. Though not everybody shares the same passion and interest, especially not when it comes to gadgets or tech. A person who is in the right job in a company will have passion and a genuine interest in what they are doing. And therefore have an intrinsic motivation to look behind the curtains, oftentimes they only lack the right tools, skills, and especially time in their schedule to follow through. Enable an inquisitive culture and your people will want more data and more ways to utilize it!
Being iterative is the final of the three aspects I’d like to discuss. Being iterative, in a nutshell, means that we are not afraid to fail. I could bore you with another cheesy quote about how you will fail seven times and get up eight… or how Henry Ford said that „Failure is merely an opportunity to try again, but this time more intelligently.“.
I guess you already know feedback is important. It is important in many ways, but especially in the original sense of the word: the way where you take what you have learned from past experience and feed it back into your design. All growth is based on learning and the same goes for data-driven projects. Your people shouldn’t be scared into giving up when their data science algorithm doesn’t immediately work. Also, once it works that new knowledge needs to be made available platform-wide (being open *wink, wink*) and used for improvement. Bringing iterative and inquisitive together is what forms the scientific method: observe, measure, formulate a hypothesis, experiment, fail, modify hypothesis, go again. I have a Ph.D. in organic process chemistry, believe me, I know a thing or two about failed experiments… Considering failure as something vital to progress and not as something bad should be part of a culture that is built to create the basis to become data-driven.
Being open, inquisitive and iterative should be fundamentally anchored in company culture. As stated above, these aspects are not exhaustive and by no means exclusive. But, if we have at least something resembling the core ideas of this framework, we should be able to fill it with our people. At best with data-literate people. And since we don‘t want to – and probably can’t – cut and replace our workforce, we need to transform it.
You‘ve probably come across the keyword but here it goes again:
What does it mean? The definition is: Data literacy is the ability to read, work with, analyze, and argue with data. I will not go into too much detail about what all this means – it might be a topic for another day.
In addition to the cultural shift that will provide an environment for your data-literate workforce, we need to get the people to embrace the idea and develop a motivation to become data-literate themselves. There is an entire future part of this blog dedicated to how to inspire followers, for now, I want to pick up on another thing, that is important and often overlooked. The way data literacy is tackled these days is mostly a “My workforce needs to be more data literate”, so top-down, approach by management. But that is not enough. EVERYONE in an organization needs to develop an understanding, at least on a basic level of what it means to read, work with, analyze, and argue with data. Especially leadership needs to be able to make judgment calls based on data and results that are presented to them by analysts. And while I am all about trust, leaders need to understand what these numbers are based on. They don’t need to understand that a random forest is not for chopping wood, or how neural networks actually work. But they need to understand if the method and data that the presented facts are based upon are flawed or incomplete. So, becoming data-literate means EMPOWERING EVERYONE in the organization.
What kind of leadership is it that we need to transform and inspire our people and then lead them to a data-driven future? This is something we will look into in the next blog, which will be dedicated to data-driven leadership and the rise and role of Chief Data and Chief Analytics Officers.
Thank you – again – for your time and attention. As last time, I am looking forward to a discussion and open feedback in the comments. What does your company culture look like? Is there a digitisation strategy as well as room for it? How open, inquisitive and iterative are you and your company? What other aspects of company culture do you consider vital to becoming data-driven?