As organizations feverishly plan the next year, it presents a vital opportunity for data teams to shape and drive this process analytically. It is one of their key jobs-to-be-done.

But, what does this look like? The image below provides a summary.

Let's consider a base financial model that outlines the desired direction for the business. The metrics of interest at this level are usually the highest-level outputs such as revenue and costs.

1) Breakdown Outputs: The first area where a data team can help is in breaking down these outputs into more granular and operational input pieces.

How should we assess the contributions from various cohorts of users or accounts? From existing or new product lines? From new features? From different markets? By increasing supply? By driving engagement? By improving application performance? Or upgrading the operations?

Data teams as one of very few teams with a holistic view of the business, can translate these top-line KPIs into targets for specific teams.

2) Resolve Conflicts: A second role for data teams is identifying and resolving conflicts.

It is tempting to want all metrics to move up and to the right - but in reality, metrics are often in conflict. For instance, if you focus on driving traffic, you may see a drop in conversion rates. If you want to drive higher revenue per account, expect higher churn. If you want to improve margins, new acquisition efforts may slow down.

Balancing these metric equations is vital for establishing metric goals, as failing to do so can demotivate even high performing teams who will struggle to connect their work to overall progress.

3) Inform Trade-offs: Data teams can help in making informed trade-offs. Drawing upon their experience of what’s worked, they can shape strategy discussions. A consequence of this is focus - deciding what to worry about, and what to de-prioritize can be liberating for operational teams.

All these pieces of work are ultimately accomplished with a significant amount of data and code. Apart from spreadsheets or notebooks, which are both do-whatever-you-please tools, there aren’t many options for analytics or business teams.

The flip side of having open-ended flexibility is that these operations are expensive - requiring experts to hand-craft queries, retrieve data, build models, and execute calculations.

In practice, due to these productivity constraints, the planning process usually does not end up as analytically rigorous as desired. Worse yet, it can be half-baked where executives believe they are thorough, but the numbers are backed by false precision.

All said, it is worth noting we are just getting started. Data teams are playing a greater role in shaping how organizations debate strategy, allocate capital, make bets, create plans, establish tactics, and set and monitor metric goals.

I’m excited to see this elevate the visibility and ROI of data teams.