Hi all, in this blog mini-series, I want to take you on a journey of how you can make your data-driven projects succeed. Especially of how you bring the most important factor of any initiative or change process into the mix: the people. In this first part, I will outline some of the challenges and provide some food for thought. In future posts, we will discuss cultural aspects, strategy, management mindset, and how to get everyone on board. I’m happy for feedback and any discussion in the comments or via direct message.
So, who is this guy?
I’m Boris! I work for a company in Germany called INFORM. As the head of sales and presales for INFORM DataLab my team and I strive to create better lives for our customers. We do this by creating better working environments, sustainable and reliable decision-making processes to help people reach their goals, and thus their businesses to succeed. I have started my journey in automation and digital decision making more than 15 years ago as a working student and with a short detour and a Ph.D. in chemistry ultimately became a business intelligence consultant and solution architect.
Why do people use Analytics?
If you’re in a similar position and know the following situation, leave a thumbs-up: Awesome Integration project, many happy hours, happy customer champion. First analytics app is deployed, maybe even more than one. People are trained in using the application, project successful, mission accomplished, let’s celebrate and all move on…. But what happens then? They use the magical solution as a VERY EXPENSIVE excel interface. So instead of drilling into the data and becoming explorers of information, they right-click on that super-complex, conditionalized, multidimensional visualization and hit: export to Excel. This is something that I saw in more projects than I would like to admit, so I started to ask myself…
“Why do people use Analytics?”
And the sad answer – in many cases – is: because they have to.
It could be, their management encourages them to start getting more numbers for a meeting or because of a recent occurrence, where the lack of transparency created a problem. Maybe they need to have a presentation ready because some regulation or audit demands it. In only a few cases there is a genuine, thorough interest and understanding of the value hidden in data. Mostly, when people get excited about analytics, it is because of the numbers, not the explorative possibilities.
OK, so now what?
I believe people should start to use analytics because they want to. Which sounds great, but can only be achieved, if we change the understanding and perception of data as a whole. I have heard a great metaphor once: data should not be the exhaust of business. But that’s how data is often still perceived. It happens while business is in practice. Receive an order -> create a record. Produce a part -> create a record. But that‘s not really doing data justice, is it? The perception of data needs to change. Not into marketing language like „the new gold“ or similar platitudes. But to something, that needs to be catered to, maintained, protected, developed, and put to work. Much like the workforce in a company. The change that needs to happen is not mainly based on data or technology – as we know, buying a tool doesn’t change the way people engage with data. The change needs to happen in these people‘s heads and therefore: their environments.
The fundamental use of data and something that everybody ultimately stands to profit from is data-driven decision making. Decisions that are based on facts and not just gut feelings will lead to a happier, calmer life – not just at work. I usually claim we‘re in the sleep-well business. The bane of our generation and the main reason for unhappiness is uncertainty. Everyone knows the feeling of having made a crucial, split-second decision based on gut feeling and then pondering whether it was right or not on your way home, during dinner and while trying to fall asleep. While it would be presumptuous to think we can eliminate uncertainty altogether, we should still do our best to reduce it to a bare minimum. So, if we want to change how these decisions are made, we need to have a look at how they are made currently. What company culture dictates on how decisions are made and….
Who makes decisions?
Who has heard of the term HiPPO? A decent amount of literature has been published on how decisions are made in companies. The term HiPPO was coined by Avinash Kaushik in his 2007 book Web Analytics: An Hour a Day.
HiPPO is the Highest Paid Person’s Opinion. Beware of the HiPPO in the room is how Forbes describes the largest obstacle to data-driven/fact-based decision making in a 2017 publication. When it comes to making a critical decision during a meeting or similar situation, some evidence is presented, a direction for consensus is reached and then all heads turn to that someone in the suit – who probably has been on the phone for the past 20 minutes – to call the shot.
The problem with that is, that many deciders and managers these days aren‘t hired because of their rational decision making, their data literacy, and understanding of how data works to generate facts to base decisions upon. Most managers are hired because they have a proven track record, they have been hugely successful in their field, their intuition, experience and therefore bias is often what guides these executive decisions. But mind, the HiPPO isn’t always the CEO, it could be a line manager, a foreperson or someone who simply has been there 4 months longer… So if we regularly keep ignoring the facts and overrule evidence with gut feelings, what does this lead to?
It leads to “No Buy-In”
If our leaders do not trust the data – or do not act as if they trust the data. If our leaders overrule decisions by gut feeling, why should anyone else act differently?
No Buy-in from management will ultimately mean no buy-in from anyone. What this means is that we do not need to buy fancy tools and build awesome data storage and pipelines, if we do not change the culture and mindset of those whom most of the information was originally thought for. We need to take responsibility and not only create nice graphics for our presentations or reports, but we also need to bring the exhaust of our business back into the system to start generating value from it. The data is there, we just need to rethink our approach.
Data is not a Project, it’s a Strategy
What is required is that management realizes that becoming data-driven is not about giving someone the job to „digitize“, or do an industry – or whatever – 4.0 initiative. Digitization and data-based decision making are strategies that will lead to a fundamental change of how a company and especially its people work. It is important that a vision is developed that describes the way this transformed company and its people operate. Again this is not about whether you buy a data lake or a data warehouse – not at this time at least – it is about how data is supposed to be perceived, managed, and ultimately USED to drive value for the people and the cause of the company. Once this vision has been established we need to get real and look where we‘re at. As soon as we have a solid understanding of where we are and where we want to go, we can formulate a strategy – a data strategy. This strategy tells us which changes we need to make to our – yes – technology and – also, yes – our data to get where we want to go. It will also point out requirements for processes and – probably most importantly – organization and culture!
Peter F Drucker nailed it, when he said “Culture eats Strategy for Breakfast”. There is nothing that kills a data – or any – strategy faster than cynicism, neglect, whataboutism, infighting, over-ambition, and many other behaviors representative of toxic and disillusioned mindsets. But remember the quickest thing to kill a strategy is a so-called leader, that doesn‘t live by his own rules. If you‘re the one with the HiPPO, (which you are probably not!) that overrules data and fact-based decisions on a regular basis, if you do not give people room to develop their data skills and employ them, if you do not free up budgets for these projects, you do not have to look any further than yourself to understand why all these endeavors towards becoming data-driven fail.
But we‘ll get back to leadership a bit further on. In the next article, we will look at what kind of culture – what kind of fertile ground – we need to provide, to enable these ideas and initiatives to flourish.
Many thanks for your time and for bearing with me until here. I’d be happy to hear your feedback and about your experience in the comments. How successful do you consider the data-driven initiatives you come across to be? How well do people adapt? Are these projects driven by top management? What other challenges do you see and how did you overcome them?