Implementing a Value-First Approach to Data Science Team Culture
We all know culture’s important. It’s been mercilessly drummed into leaders everywhere. As a DS org leader, it’s your duty to actively shape one too. But with so many ways to go about it, what do you focus on?
Sure, all of the usual wisdom applies. Champion taking ownership, adaptability, being humble, listening well, being frank, eating last... And for the love of all that’s holy, getting some sleep. But for DS teams there’s one aspect of culture that I would argue needs extra care and attention from the leader: delivering value.
It’s not Me, It’s You
You see, data scientists are tinkerers at heart. Some love thinking about coding tools, some about modelling techniques, some about efficient implementations… But I’ve yet to see a data scientist in his or her natural state who loves thinking about delivering value for the company. It’s just not something we are bred for. And that, if left unchecked, can lead to unintentional, but very real problems.
They say 87% of data science projects never make it to production. And while that infamous quote has (thankfully) been tried and found wanting, it still touches a nerve: an unsettling proportion of data science projects do never make it to the stage where they actually become useful to the wider organisation.
Now, this can have a lot of reasons as to why, but the one I’ve seen most frequently in the wild is this: data science teams are laser-focused on technical excellence, but they lose sight of the value their work is adding. They don’t course-correct based on business context and find out too late that their (often genuinely good) technical solution does not generate any value for the stakeholders.
And so the vicious cycle begins:
Data scientists don’t understand what they’ve done wrong and failure to adopt their product demotivates and disengages them
Business in turn blames the data scientists for not being focused on delivering useful solutions
This attitude disheartens both parties and so puts the projects that come after at the heightened risk of failing
Because of this, some (or most) of the following projects fail
A cynical attitude spreads and people from both sides of the fence start to believe that no amount of honest effort they put into data science projects will lead to success, and that there is something wrong with their counterparties
With this, the cycle is complete and you’ve arrived at the unhappy station of self-fulfilling prophecy: projects fail because everyone thinks they will.
Breaking the Spell
Before discussing how culture of being value-focused can help here, let’s get some housekeeping out of the way. The projects should solve real business problems, project teams should include heavy business competence, business sponsors should be fully engaged in the project, and all that. If these are not taken care of, no amount of orienting data scientists towards value will help.
But once these fundamentals are in place, you need to make sure that your data scientists get excited by not only clever technical solutions - sure, that’s a must, but they don’t need your help there - but by building actually useful solutions.
It’s not easy - data scientists become data scientists because they mostly get excited by building cool stuff. Their whole training further reinforces that: all the university courses, bootcamps and Kaggle competitions preach that once you have the perfect model, it’s all smooth sailing from there. The discussion about importance of actually delivering value through data science (even if sometimes through really dumb models, or even just well thought out business rules) is nowhere to be found. And the end result might be not unlike that Sherlock Holmes joke - you build something that is very cool, and very useless.
So, how do you get your data scientists excited about the value they can create through their work? There’s no one recipe, but I would recommend tapping into one of the strongest intrinsic motivations any of us have: to be doing work that affects others positively.
Unless your data scientists are complete sociopaths (in which case delivering value should be the least of your worries), in the long-run they care that their work makes lives of their colleagues and clients better. So make sure you answer that question loud and clear and as often as you can. Show your data scientists how their work benefits others, show the positive response from the people their work is affecting, let them taste the intoxicating feeling of doing meaningful work, and they will naturally start to concentrate on how to do that more. They will concentrate on delivering value for their stakeholders.
Yes, but How?
So how do you actually do that? It’s obviously a never-done process and I haven’t personally tried out all the ideas listed here, but here’s some food for thought.
Keep Value Top-of-Mind
To build a culture of value, you have to keep it front and centre. Beginning of any well-run project is formulating the business problem and very explicitly sizing the opportunity. And the biggest mistake at this point is to not involve your data scientists every step of the way because “they’re more focused on the technical side of it” or that “we want to save their time”. In my experience, the initial hint of hesitance data scientists exhibit on those meetings is quickly replaced with enthusiasm and once they see first-hand how business thinks about a problem in terms of value, they get motivated to help as much as they can.
Another way to keep value on everyone’s mind is to develop an executive dashboard which tracks value of all projects through time and use every excuse to bring it up in any remotely relevant conversation with your stakeholders. Sure, they’ll stop inviting you to parties pretty soon, but can you really put a price on creating a value-first culture?..
Create spaces for celebrating success
Once you’ve cemented the culture of thinking about value at every step of the project, it’s time to reward your champions. Celebrate success at every point, and use every opportunity to show how your data scientists help their coworkers and clients through their work.
It could be all sorts of things from a simple recognition of good work on team retro to organising a huge get-together where business and data science come together to celebrate achievements.
Whatever the format, be sure to do several things:
Make a point of talking about the good work yourself - praise from a leader means a lot
Be a megaphone for your team’s good work with your management and find ways to have them personally thank the team
Organise meetings where senior business stakeholders will detail how the work your team is doing is helping them in their day-to-day
If you can do so, symbolic titles can also be a very profound way of recognising work: I’ve heard of an European telco that gives out “legend” titles to the most productive (as measured by delivered value) data scientists during their annual get-togethers with business. Can you imagine what would it do to your motivation if your stakeholders called you a legend for your work?.. Exactly.
Connect your work to a higher purpose
Lastly, some of the most powerful motivation can come from the most overlooked of places: talking to your data scientists about the company mission and impact.
See, the problem is that data scientists usually don’t get much exposure to what the company is doing outside the narrow project scope that they are working on. I myself have continuously bumped into data scientists not knowing the most basic facts about the products or major processes of the companies they work for.
This is not data scientist-specific: all business support functions (ahem, developers) face the same problem. This is why great companies like Shopify put all their new employees through a rigorous onboarding which helps them learn about the company mission and products. They even have their developers build real online stores!
So, it could be a good idea to put together some content that shows your data scientists several things very clearly:
Your company’s products
History, mission and values
How their work contributes to the mission
This might seem a bit out of JD for a data science leader, but if the result can move people to tears and let them feel connected to their work, then surely that’s worth a try.
How do you approach the challenge of connecting work with value in your data science teams? Share your experiences in the comments. Your insights might just save a fellow leader from reinventing the wheel - or at least give them a better blueprint for it.