Over the last few years I’ve gotten to ask innumerable data leaders what’s top of mind for them. I’ve heard a lot of things – warehouse migrations, tool selection, team hiring – and it’s all important.
But there’s one overriding, dominant thing I hear more than anything else: understanding and communicating the ROI of their data team.
The one thing you can’t measure
This topic comes up a lot around budgeting cycles. Data teams want to hire more people, or acquire new tools, or justify their massive warehouse spend, and a well-intentioned person from finance comes along asking if you can put a number to the ROI of all of this stuff.
Attempting to measure this quantitatively is alluring, especially for data people — our whole job is quantifying things! It’s easy to get pulled down a path of model assessments, impact scores, and insight-value attribution.
Measuring the impact of most data work is just really, really hard. Calculating a crisp “return” on an analysis project or model is difficult, and it’s even harder for infrastructure investments: how do you quantify the impact of a better schema, or a more reliable pipeline? An improvement in data quality can be objectively beneficial, but also quickly taken for granted. So much of data is glue work – and trying to quantify it is sticky.
But people try! Oh they try, with baroque “insight log” spreadsheets trying to add up the value of the support they provided. There are limited cases where this might work (for instance, if your data team is providing assets that directly enable a customer-facing product). But in most cases, it is not only really tedious to track and tally, it just winds up being measurement theater, and ultimately underwhelming and unconvincing.
So… how do you measure data team ROI? You don’t.
ROI as NPS
The truth is that if you’re trying to quantify your impact by yourself, you have already lost. Instead, the best way to tell the ROI story is for other people to tell it.
“Net promoter score” is a simple concept – basically asking customers “how likely are you to recommend this product/service to someone else?” It concisely captures a lot of dimensions around user satisfaction and perceived value into one metric.
I’ve come to believe that this is the right way to think about understanding the value of a data team — through the lens of the “customers” aka the stakeholders and consumers of insights. Ultimately it comes down to what they’ll say — do they love what your team is doing, and would they recommend it to others? If your data team is truly providing value, the leaders of other functions should be lining up to sing your song. In fact, they should be the ones advocating for more data headcount!
And if they’re not — what does that say? Are you really providing value? If they feel they could get along without you… maybe they should?
Service and agency
One challenging part about this is that it requires fully embracing that your value as a data team is really transitive — you’re there to support the success of other teams. I’ve found that many data leaders don’t want to be told or hear that they’re a “service” organization. It feels “back office,” like you only exist to support other functions.
Well, I’m sorry to say — you do. That’s the gig, and deeply internalizing that is the road to fulfillment and value. The fundamental role of the data team is to provide the data, tools, and analysis to stakeholders so they can make better decisions.
Driving ROI through this lens requires a level of agency – including the squishy, human work of convincing other people to give a damn about the work you’re doing. Call this politics, call it persuasion, call it projection – whatever.
But you need to realize that the “ROI” of your insights will effectively round to zero unless they inspire action.
Toward a bright, impactful future
It’s as exciting a time as ever in analytics, with data lakes, AI, and integration providing so many new opportunities.
But gaining the budget and support needed to take advantage of all of this is an uphill battle without advocacy from functional stakeholders. Data teams need to make sure their impact is obvious and clear to their stakeholder first and foremost – and not pretend that a spreadsheet can tell the story.
Done right, it means the next time a data leader is asked to justify ROI, they won’t have to. They can sit back, and let others tell the story for them.