If you're running an internal enterprise data science or analytics team, and you can't get the time of your stakeholders and business partners "to help you help them," there's probably a reason - or two - or three. These are some of the cold hard facts that maybe they won't tell you--but maybe they are feeling these. The good news is, there are ways out of these, but it may require playing a longer game. Short-term tactics won't work.
So, when you need their input, help, or time to help you understand how best to help them with decision support applications, dashboards, predictive models, and the like—here are some reasons why they may not be available, give you vague requirements, push you to a subordinate, etc.
This might hurt a little, but that's not the intent—so I hope this comes across as productive, useful feedback:
- They don't associate your work or team with delivering value as defined in their eyes.
Note that I am not talking about being on time with deliverables or commitments or dashboards or models or whatever. I'm talking about the downstream value/outcomes associated with these outputs.
- They know that your work represents the truth in cold hard facts—and the truth is threatening.
The facts—in certain environments—can be a threat. They can require us to say, "hey, that didn't work-our assumptions have been off...for a long time." Not everyone is ready for that.
- They believe that you, being the data wizard you are, should be telling them what they should be focusing on, looking at, exploring, believing—and that they aren't integral to the solution. That's especially true for you data scientist types who may be perceived as wizards with "magical" capabilities.
- They believe that their delivery of requirements in the form of solutions is sufficient (even if their requirements do not represent a clear problem space to you). In the book, The Mom Test, about doing product research with customers, the author makes an interesting point: the customer/user/stakeholders owns the problem. You own the solution. Your stakeholder may say "I need a better dashboard with best-in-class visualizations powered by AI," and sometimes, that may be true, but that's not a problem. It's a solution masquerading as a problem.
- They don't understand the cost in time, dollars, or technology that building the wrong solution imposes on them (or you). They may feel that you can just "redo it" if it's not right, which may be true—the question is whether the hard costs, or the opportunity costs, matter—and can be quantified in a way that the risk to their goals is quantified and clear.
- Your past solutions are too hard to use, not useful, or not trustworthy (possibly because you fell into the trap of giving them what they asked for instead of what they needed).
- They (perhaps rightfully so) believe you should know the business (or their business) well enough such that they shouldn't need to be involved at the level you're asking for.
- Your team is perceived as too slow to provide useful insights on the timeline they are operating under. It may be that their timeframe is unrealistic, but helping them to understand the tradeoffs of speed vs. accuracy, and value, is (I would argue) part of your job—particularly as a data product manager.
- All they hear about from your team are things like snowflakes, pipelines, fabrics, cloud, meshes, products, and random forests when their lingo may be "sales, CPM, reach, attrition, CLV, etc." You don't want them saying,"WTF are they doing over there?" but if all that data s@#$ is all they hear about, that's likely what they're thinking.
- You don't make them feel like you're on their side. Whether it's because of your lingo, your approach, how you make them feel, or something else, you're as much of a threat as you are an asset. "I'm going to get a million questions about this and that if we bring in the data science team and right now, I just need X."
What did I leave out?
Is there a #1 here that resonates with you most? Send it to me here.
Photo by Wesley Tingey on Unsplash.