Data is the “new” gold. Your organisation must have it, because it’s a must-have for everybody. So, you hire 10 data scientists to produce some magic. And then… nothing.
Read this if:
- You’re not getting value out of your data
- You cannot tell which are your top five selling products
- Your retention for data specialists is shorter than three years
Your expensive consultant has promised you amazing upsides. “Big data is the future.” “If you improve the visualisation of the data on your dashboards, it will lead to faster insights and better decisions.” “You can predict the future with AI.” And the best one: “you can develop the new unicorn service your customer didn’t even know they needed!”.
What do you end up with? Frustration. And worse: your data strategy can actually slow down your overall company growth. We have seen this happen so many times. This article from Abhijeet Sarkar on Medium.com confirms some of our lessons from the past.
Let’s start by mentioning that the land of data science is very confusing because of all the “fugazi”. The term “data science” is the best marketing fugazi trick out there, making you believe that it can solve all your problems. Sorry to burst that bubble. The truth: data is here to help you make better decisions for the sake of your customers.
Abhijeet has uncovered the fundamental relationship between a company’s maturity in respect to its data strategy, and the effect of data maturity on growth.
First you need to understand how data-mature your organisation is. You can measure where your organization stands by using the Dell Data Maturity Model (DDMM). There are four levels:
- Data Aware
- Data Proficient
- Data Savvy
- Data-driven (at ONBRDNG we preferer to use “Data-informed”, but for the sake of the DDMM let’s stick with Data-driven)
To understand the level of your organisation, it’s best to look at which of the “pains” you are experiencing. Look at the picture below. Do you recognise any of these situations? Can you remember someone telling you that your organisation is data-driven? Of course!
Now you probably know that your organisation is not data-driven, but somewhere between data aware and data savvy. That’s nothing to worry about. It’s perfectly normal, most companies don’t make the transition past data savvy. And most organisations try to skip a level.
However, this is something your organisation needs to fix. When you skip levels, you don’t build the foundation you need to produce real value. You have started to take action (AI, machine learning, data mining) and to hire (data scientists), but your organisation is simply not ready for this yet. Instead of building cool things, your data scientists are mostly cleaning data (hence the frustration and retention issues). Do these frustrations from a data scientist look familiar? To make matters worse: Abhijeet compared a failing data strategy and a typical growth curve of a company. What he found: when an organisation is falling behind on its data strategy, overall growth will slow down. As shown in the diagram below, the maturity of a company’s Data Strategy (its Data Maturity Curve) often lags behind its Growth Curve. You think you’re the blue line, but unfortunately the dotted line comes closer to the truth.
At ONBRDNG we don’t believe it’s quite as straightforward as Abhijeet describes it. But we have certainly experienced that when an organisation can’t deliver on its data ambitions, it will definitely slow down its overall potential growth.
What now? These are our suggestions:
- Be honest with yourself and your colleagues: you messed up, you’re going to correct this.
- Be brave. Fix the basics first. Cure inefficiencies in people, processes and business unit integration. This is hard, because it will feel like you’re falling behind the competition. Trust us, it’s a winning move. Perhaps this quote from Dan Ariely (Duke University) will make you feel better: “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it“.
- Focus on best practices and the technologies that enable them. This will improve efficiency in the decision-making process long before Big Data, AI, or any other tech hype.
Need help fixing the basics? Or insights from people who have actually been in the same position? Let’s talk!