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Datameer Blog

3 Ways to Make Product Management More Data-Driven

By on April 21, 2016

There are lots of product management and engineering teams that use the SCRUM methodology for agile development. At Datameer, we have small, self-sufficient, independent teams across the globe that each own a certain component of a product and drive their own roadmap, which supports the overall product roadmap. Each team minimally consists of:

  • a product owner
  • a designer
  • a Quality Assurance person, and
  • ~6 developers

While tools like Jira Agile, Assembla, Axosoft and Gemini can help plan and track the individual sprints, the challenge is in release delivery, where all of these parallel tracks need to come together at the same time. These tools come with some reporting capabilities, but in general lack the more advanced insights that I as a product manager need to understand, like:

  • How do I understand overall prioritization?
  • How can I reliably estimate accurate GA dates?
  • How can we consistently feedback to customer requests?

Following are three examples of how we have made our own product management more data driven, specifically using Datameer to analyze our Jira Agile data.


Of course, product managers maintain their team backlogs, but there is nothing to guide prioritization of individual items. With hundreds, if not thousands of open requests, one can easily “get lost” finding, for example, the “five most important things.” That said, there are so many reasons why a request can be important or not, that it can quickly become too complex to oversee everything.

We enrich the data we retrieve from Jira (including our own custom fields), blending it with data from our Salesforce CRM system. This gives us a broad spectrum of attributes, which we can score individually, and then combine in a “magic formula” to calculate an overall score for each ticket. The formula has been tuned over time and now provides a reliable view of the importance of internal and external requests, which Product Owners can use to prioritize their backlogs appropriately.

The “magic formula” is built on some key criteria:

  • What’s the strategic fit of the request?
  • How many customers are asking for the same thing?
  • What is the revenue we are generating from these customers?
  • What’s the customer “health status” and happiness?
  • Is there a renewal upcoming soon? (we have a subscription model)
  • Have we committed to a due date, or to track something as an in-roadmap item already?
  • Did other internal departments (typically Sales or Services) ask for this to make their life easier?
  • How important is this relative to other requests from the customer?
  • Will it prevent support cases in the future?
  • What’s the level of effort?
  • … and more

We create a ranked lists of open requests, separated by feature requests and bugs. They come along with histograms, showing the distribution of score values. We can track the same information for closed tickets. That allows us to validate our prioritization.

score by customer

It also allows us to run analytics on a per-customer basis. This tells us which customers have requested the most “feature points” and how much we delivered for each of them. This is helpful to confirm fair resource distribution across the customer base and allows us to shift the focus to certain customers when necessary.

Release Prediction

We estimate level of effort in “story points” and engage Engineering to provide these estimates as tickets come in. With the given overall velocity (which is very much different across the individual teams), we can get a good sense of when a release will be GA. Basically, what you are doing is monitoring open vs. completed story points over time to see the trend.

Typically at the beginning, scope steadily increases. New tickets are filed as development moves forward. Later, this curves flattens. At the same time, tickets are being resolved in parallel, bringing you closer to the delivery of a final release.

If we have reason to believe we will not make a certain deadline, we immediately review the remaining work to identify areas to reduce scope.


Other actions could also involve adding more people to certain tasks, but from experience, this does not necessarily increase performance, because the “context-switching” comes at a cost.


Field enablement

Once a new version is released, Sales and Services want to reach out to certain customers to let them know that their feature requests or bug tickets have been addressed. But knowing who is affected can be a huge research or communication effort without good reporting. We track customers in our JIRA in a custom field and this way we can associate any request to the party/parties who requested it. Then, we simply create a report in Datameer and we have an easy, reliable way to report where Sales and Services can see included customer requests for every version.

customer notification

Product management is no easy task, and it only gets harder the bigger your team, and product gets. By analyzing the data you are already collecting in your agile tracking systems, you can bring a little bit of sanity, and even foresight, to an otherwise chaotic process.

Want to make your own product management efforts more data driven? Check out our free trial, and this JIRA analytics app is fully customizable, so it’s a great place to get started.

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Frank Henze

Frank Henze is vice president of Product Management at Datameer with more than a decade of experience in building enterprise software systems. Previously, Henze ran project management at 101tec, a supplier of Hadoop solutions and Nutch-based search and text classification software. He also has more than five years of experience in development of large-scale systems and search engine solutions for companies such as EMI Music, Sproose, Krugle and the German Environmental Agency.