How IT teams can benefit from predictive modeling
Every business, from the simplest to the most sophisticated, relies on data. Even a hot-dog vendor runs the numbers to ensure he’s turning a profit and determine his peak hours or most popular toppings. More sophisticated businesses use predictive analytics to drive highly strategic digital marketing campaigns.
But does it feel like there are still unexplored opportunities for applying data analytics to the business of IT? Information Technology and Services teams can be so busy helping the whole organization collect, manage and leverage data that they don’t have the resources to analyze their own processes. How often do you really look beyond standard project-reporting and service-level-agreement (SLA) metrics to find the deeper lessons about your delivery capabilities?
Likely not enough. Because when IT is treated as a support function, rather than a strategic function, having even a moment for improvement is unheard of. Other departments don’t want to foot the bill, and IT can’t be its own client. Convincing executive management that a small investment now will ensure big savings later can be a serious challenge.
But smart companies with high-performing IT organizations (the Googles, the Amazons, the Teslas of the world) invest in the function. And more and more, that means turning to analytics.
Numbers are neutral
Your ability to deliver is embedded in your stats. No matter the company size or what the project, you will achieve greater success sooner if you understand your team’s strengths, challenges, and overall ability to deliver. But you need to look at the data. Data doesn’t care that a particular stakeholder said you can shave three weeks off the timeline, or whether there’s a bonus tied to line item #4. Numbers are neutral.
Which is why examining the patterns in your team’s operational data will enable more worthwhile conversations about mitigating risk and planning for the unexpected. You already have the data – it’s just a matter of turning it into predictive actionable intelligence.
For example, how many times have well-intentioned project members said they expected the pace of a project to accelerate as the deadline nears? And how many times has that actually happened? The assumption is that because team members will learn on the project, they’ll be able to work quicker the further along it is. But before teams let this assumption dictate timelines, they need to determine if it’s supported in their own project data.
Consider custom software development. Data about defect find-and-fix rates can be used to predict when the software should be of reasonably sufficient quality to move into production. It’s fairly straightforward to construct a model that shows whether the team is fixing problems at least as quickly as they’re being found. This type of analysis provides a good gut check for your go-live date and allows you to take action early if you see your timeline is at risk. Simply put, knowing more about your abilities enables you to be more responsive.
Let us help
At cShell Consulting, we have hands-on experience developing and deploying similar models for a variety of large-scale projects. Let us show you how to turn your current and historical project data into predictive intelligence that’ll keep your plans on track. Learn more about the expertise we can bring to your organization.