Data Alone Isn't Enough to Make Development Decisions

Using data for tracking your product development process is only one piece of the bigger picture. There are other steps to take to make wise development decisions.
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If you’re already tracking your product development process using data, that’s fantastic. But it’s still only one piece of the bigger picture.

Companies managing complex systems can no longer afford to let critical decisions — with millions of dollars in balance — left up to instinct. A gut feeling might help you make a strategic change, for instance, but it won’t adequately convey how that decision impacts all the other dependencies both up and downstream.

Data and analytics are increasingly becoming more integrated in all forms of product development for good reason. Gauging development progress and identifying risk has a direct correlation on cost, schedule, and quality. Tracking these areas with analytics from the start of a project will help you correct issues before they spiral into huge expenses in the form of recalls, returns, and all the negative impacts that come from such problems.

1.) Provide Meaningful Context

As authors Lou Wheatcraft and Michael Ryan describe in the whitepaper, “Integrated Data as a Foundation of Systems Engineering,” data on its own isn’t very valuable at all. A set of numbers detailing the number of requirements by “done” status isn’t going to be beneficial if you can’t compare it with something else for context.

When you have the ability to measure your current requirement status flow – how requirements transition through different states in your process over time — against a couple previous projects, that information suddenly puts a lot of other things into perspective: Are you behind? Ahead of schedule? On track? Then you can start drilling down into the reasons behind your progress (or lack thereof).

Further broadening your scope – measuring cycle times across, say, 10 development cycles – will give a much better idea of your performance. And with this context for the information, you can then expand on the data and further mine it for gold.

2.) Recognize Trends and Reporting Status

The more information you begin aggregating and analyzing — whether through manual inputs or machine learning — you can begin understanding patterns, correlations, and causations.

For instance, if you’re consistently seeing spikes in activity towards the end of a project, rather than at the beginning, you may have a problem with late-stage thrash, which usually leads to costly rework.

Armed with detailed information, you can use this knowledge to communicate more effectively across the organization, better defend and justify decisions, and correct problems sooner. Your status updates to stakeholders will suddenly take on a greater amount of weight when there’s cold data backing them up.

You’ll also be able to stay on top of project process a lot better and make sure whatever is being developed is meeting the appropriate needs and expectations of the stakeholders.

3.) Leverage Your Team's Expertise

There’s still one more layer of data analysis that can make you more effective as a systems engineer: your team.

Applying all the processes mentioned here will enable you to get a stronger grip on your development. Relying on your team’s collective expertise, however, is the thing that can take it a step further.

By sharing the real-time data, trends, and insights with your engineering team, you can fuse the present with the past by leveraging their years of experience in new and profound ways.

Each person on your team has different experiences to lean on, and their collective expertise will help guide your process remarkable directions.

Maybe you’re missing something that’s causing a nagging bottleneck that one of your veteran engineers can instantly recognize and solve. Conversely, it’s possible your first-year QA team member has a workaround to expedite testing hang-ups. You won’t know until you begin sharing information with your development team and getting their feedback.

Much like numbers need comparisons to provide value, statistics alone are just one part of the equation. In order to make full use of the learnings, you need the shared wisdom of your team to maximize the effectiveness. Anything less isn’t enough.

Robin Calhoun is a Senior Product Manager at Jama Software

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