Every data request must have a clear path to the goal of the organization

Why understanding the organization’s goal is the key to aligning all data work.

Hello everyone. Yes I’m writing again!

I thought the break would be longer, but it turns out I was putting undue pressure on myself for my writing to be perfectly aligned with a singular idea.

What I enjoy instead is exploring ideas related to data engineering, analytics, decision making and business.

I’ve read a lot of books and articles on those topics, learned some useful things and would love to share them.

So you’ll get a slice of that in your inbox. I’ll keep them short and to the point, so you can read it in a few minutes and still get nuggets of insight.

Here we go!

Every organization has a goal. Once understood everything becomes clear. Data work is properly aligned and you’re no longer swimming in unanswered requests and overdue tickets.

More often than not, if you ask stakeholders what the goal is, you’ll get different answers:

• A marketing executive might say “ROAS” or “CAC.”

• A sales executive might say “Close Rate” or “Deal Flow”

• A VP of finance might say “ROI”

• The CEO might say something like “Growth”

It’s really easy to get caught up in these local-optima metrics and lose sight of the overall goal. This lack of clarity makes it difficult to see the impact of your work. Worse, it can get very discouraging.

Usually the goal of any for-profit company is to make money. All the other metrics, like the ones we saw above, support that goal and can be thought of as necessary conditions.

Nonprofit organizations also have goals. A hospital for example has a goal like health outcomes through patient care and research.

The goal should be expressed as a single metric and come from the top.

Until you get the executive board to agree on the goal, you’re doing yourself a disservice. In many cases the goal should come from them and be disseminated through the company.

Every request must have a clear path to the goal

As data professionals we help stakeholders make evidence-based decisions. These decisions are mainly about improving the organization’s system.

Improving a system means to make it achieve more of the goal, achieve the goal faster, better (e.g. higher quality), cheaper or simpler. Therefore whenever you get a request for data—whether it’s a pipeline, a metric or a report—pause and ask:

“How does this impact the goal?”

They should at least have a rough idea or a causal mental model. Here’s an example of a mental model for increasing ROAS (return on ad spend):

Move Lever => Increase ROAS => Reduce CAC => Increase CAC/LTV => Increase profitability

Seasoned readers of my newsletter will immediately see this as a branch of the company’s metrics tree.

I would highly recommend you do this for every request. Start with their request, figure out what outcomes they’re looking for, figure out what actions they are considering, then map out the casual chain.

Now you can suggest different actions or data research projects. This, by the way, is how you discover actionable insights. Doing data spelunking without a clear understanding of the goal and a rough idea of the causal model is a waste of time.

When you map out the entire mental model explicitly, you’ll not only help them get clarity, but also make your work seem more structured and make yourself seem smarter. People will come to you just to clarify their thoughts.

The simple diagram I showed is an example of a CDD (Causal Design Diagram) which is the key element of the field of Decision Intelligence. I’ll write up a more detailed post on CDDs later on but for now, the above should suffice.

Now your work suddenly feels strategic. It has real impact. Isn’t that what you got into data to begin with?