New business requires new data
It was early 2017 and it was only weeks after my start at the new institute for data science and entrepreneurship that I began to lose hope. Tons of companies had shown their interest to collaborate in research or with students on company assignments, the interest was truly overwhelming. But all of them had existing datasets that we were supposed to turn into gold.
In this post I will argue that:
- Companies should strive for additional revenue streams that have a positive impact on their continuity.
- Additional or new business requires new data, and this requires a new approach to data science projects.
I have never created something meaningful new with old data. Yes, you can optimize existing processes, cut a lot of costs. But in a time of change, when new technologies emerge in a fast pace, your companies has to innovate. You want to create additional revenue streams, disrupt your industry if you will, to make sure no one disrupts you.
New questions require new data
What is the most relevant business question you need to answer, or what is the most meaningful business goal a company can have? In case of a startup you are testing your raison d’être, your reason to live. Would be nice to have one, right? And in case of an existing company you are focused on the continuity of your organization. How will I survive?
From my workshop “Designing Intelligent Disruption” inspired by former Booking.com colleague Lukas Vermeer
In both cases you need additional revenue, and enough of it. Startups start with nothing, so they will have little data. The need for new data is pretty obvious then. But for existing companies this is different, the data that you have comes from your current operational, marketing or sales process. You have to see that the data you have is strongly connected to your current business model.
Yes, I have also done data science projects optimizing current business models based on existing datasets. I have worked with insurance companies, energy companies, travel tech companies, agrifood startups and many more. Cutting costs went rather easy; often with enormous impact on the current bottom line. But I’d deem them irrelevant.
The data was already collected, it was next to impossible to answer new questions with these datasets. Also, if I was a cynical analyst I could say that entrepreneurs/intrapreneurs should take their responsibility. In stead of letting the technical data scientists solve their problem (here is my data), they should be part of asking the right questions (what data do we need?).
As their CEO I wouldn’t feel much better about the long term fitness of my company, of our ability to survive and our future bottom line. But new business with new data projects are less easy. Innovation based on data science (or anything for that matter) isn’t easy. But this is the challenge we are faced with.
Solving new questions with data requires creativity
How exactly to go about using data science to create new business is something I want to discuss in a follow up post. What I set out to communicate with this post was:
- Companies should strive for additional revenue streams that have a positive impact on their continuity.
- Additional or new business requires new data, and this requires a new approach to data science projects.
To do this well, we will need people to work together. And we should foster creativity in data science projects. It is not about solving technical data science problems, it is about solving the most meaningful business problems.