Commercializing Your Data Science Team

I’ve noticed that many organizations and CTOs end up with very academically oriented Data Science Teams. The focus of these teams tends to be on authoring white papers, exploring new techniques for analysis, and playing with new algorithms for modeling.

These teams tend to focus much less on business impact. Which of course can negatively impact the success of a product line.

I believe this scenario is common in part because so much of the nature of data science is exploratory. Therefore, these teams function fairly differently than classical product engineering teams. Very often there are no sprints and there may even be no requirements development given the experimental nature of the work.

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Of course, there are scenarios where academic research is warranted. But in this context, we’re thinking about investing in Data Science teams in SaaS-type companies for the purpose of developing growth-driving features in their product lines.

(If your product is internal then don’t think of it as financial growth, but whatever internal success metric that makes sense for your business, such as increased internal user efficiency.)

So, how do you commercialize a Data Science team that has gotten itself mired in an academically oriented culture instead of a business-oriented one?

Here is a quick list:

1. This first one is easy. Start hiring data scientists that come with a strong commercial background.
2. Are you sure you’re setting commercially oriented business objectives for the team? As you know, it takes a dev funnel with the right things at the top to drive success downstream.
3. Ensure the team isn’t on an island. One way is to graft them into product teams. If that’s not an option at least make sure they are well connected with other stakeholders.
4. Have the team develop business cases for their work.
5. As part of business case development or at least project scoping, teach your team of data scientists how to project ROI.
6. Reviewing outside case studies of commercially successful data science projects can be an effective way to change the culture towards a business mindset.
7. Consider sending the team to training that emphasizes the business side of analytics and data-based product development.
8. Incentivize commercial thinking by tying in performance reviews and comp (bonuses) to product growth/success instead of academic pursuits.
9. Celebrate commercial wins.
10. Create a feedback loop so that after projects are done you can assess their business impact and work with the team to parse through the business results.
11. Encourage the team to look for simple solutions and not necessarily technically impressive ones. This means hyper-alignment to clear business goals.

This is a pretty good list but clearly there will be pitfalls and challenges along the journey of upgrading the culture.

?? My rule of thumb is that for every 1 year the team has been together and stuck in the academic “rut,” that it will take at least 6 months to get the team fully out of it.

That means if your team has been around for 3 years in the academic model don’t expect the transformation to happen any faster than 18 months.

To pull it off you need the above tactics plus a lot of patience.

The biggest pushback you’re going to get from your team is that you’re focusing on the wrong things and the approach they’ve been using is the right one that “works.” And furthermore ‘how dare you’ take them away from their academically-oriented study and research. That’s their job (!) they will say.

You’ll gently have to remind them that the data science team has to at least pay for itself. That’s the minimum. The real sweet spot is 3x’ing or 5x’ing the investment in data science each year.

Closing Thoughts

You’re not going to reach that by writing a lot of white papers.

It will come down to new feature and capability releases that makes a difference to the bottom line of the product and business.

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