Product managers are in a unique position with regards to data. They have access to data about how a product is being used, which is great for backward-looking evaluation of product decisions. Because product decisions have a direct impact on other teams’ metrics, that information can also be valuable as a means of helping other teams become more data-centric and achieve their own goals.
Before we discuss specific tactics for product managers, let’s take a step back and examine the organization as a whole. Regardless of the decision-making process or structure of an organization, there should always be at least one important high-level metric that a company is trying to achieve.
Data clarifies companywide focus
It’s crucial that leaders from every team have access to a consistent dataset that joins product, sales, marketing, and customer data to track progress toward that important KPI. For a lot of businesses, that one important metric is revenue. Companies at various stages of growth might choose to focus on hitting a certain number of total users, retaining a certain percentage of existing customers, or something else.
Whatever stage a company is in, leadership should be setting very public goals around this critical KPI. At Sisense, the entire company has access to what we call “The State of Sisense” and every employee can log in and view it at any time. It’s also displayed on screens around the office, so we get used to seeing the goals and making sure that our individual efforts contribute to achieving them.
A good product manager sees that big companywide goal as a frame for their own data-driven decisions. For example, if the goal is to reach a certain number of active users, then the product team should look at their product or user data through the lens of what decisions will maximize active users. Every decision should be made with respect to that larger goal and how to achieve it.
This top-level KPI alignment is also crucial to the way product managers collaborate with other teams. Rather than discussing a one-off feature request based on anecdotes, product teams should analyze the request and reframe data for these partnerships around that top-level KPI. Rather than meeting with the customer team to discuss a one-off feature request, the product team should put together a dashboard that both teams can use to more directly address that top-level goal: Which features will maximize the number of users by improving stickiness and preventing churn?
From there, a dashboard can be created to track progress and both teams can use that shared information to make future decisions. Not only is this a more effective way for a product team to prioritize projects that move the company in the right direction, it also builds trust and credibility with the internal teams.
Product data is at the heart of cross-team collaboration
The same can be done with other teams, but the process should look similar — start at the high-level KPI then determine what specific cross-team goals can contribute to that KPI. Once that has been decided, a dashboard can be created to track progress and both teams can use that shared information to make decisions. This is an easy way to narrow focus down to just the projects that move the company in the right direction.
Using data to drive decisions is also an easy way to empower a product team to make decisions about which initiatives to accept from other teams. Since all of those teams have their own priorities, product teams are often swamped with unrelated or even competing requests. It’s always hard to say no to these requests, but a team that prioritizes work based on data has hard evidence to ensure that they’re accepting only the most important projects.
There’s one more important part of this story — the data platform. Unifying cross-team initiatives around a critical metric is a better way to operate, but it only works if everyone has access to a consistent dataset. This approach breaks down if teams aren’t all looking at the same information. To do it right, all of the data from every source needs to be combined to create a single source of truth. This eliminates version problems and conflicting data-based conclusions.
For the product team, this means other collaborators have access to the product data they need and multiple teams can share dashboards that all ladder up to the critical KPI. It’s a step toward a better product and also an easy way to make sure that every necessary team joins in lock step.
To learn more about making smart product management decisions with data, download our Data-Driven Product Management guide.
Scott Castle is the VP of Strategy at Sisense. He brings over 25 years of experience in software development and product management at leading technology companies including Adobe, Electric Cloud, and FileNet. Scott is a prolific writer and speaker on all things data, appearing at events like the Gartner Enterprise Data Conference, Data Champions, and Strata Data NYC.