How a Premortem Reveals Data Modernization Project Pitfalls

How a Premortem Reveals Data Modernization Project Pitfalls

By questioning assumptions early, leaders can spot workflow dependencies, adoption barriers and ownership gaps that a technology-first plan could miss.

Enterprises pursue data modernization projects to improve analytics that support decision-making and to create a foundation for AI.

However, those modernization efforts can focus on platforms and architecture at the expense of data governance, change management and user workflows. As a result, unanticipated risks can surface as distorted metrics, disrupted processes and declining trust from frustrated users.

Data leaders can use a premortem exercise to reveal those risks before major decisions become too difficult to undo. Described by psychologist Gary Klein in his 2003 book "The Power of Intuition," this risk-assessment technique asks participants to imagine a project has failed and then work backward to identify risks that might otherwise go unnoticed. Applied to data initiatives, the exercise broadens discussions beyond technology.

How to conduct a premortem

A premortem does not replace a risk assessment. Rather, by assuming the worst has already happened, it can make participants more willing to challenge assumptions and raise concerns.

"The Power of Intuition" describes a premortem as typically involving eight to 12 project team members. In it, participants give reasons why they think the failure happened, create a consolidated list of their concerns, devise ways to avoid the anticipated problems and periodically review the list.

Comparing related planning exercises

Some businesses embed premortem-like approaches within broader project approaches under a different name.

Balazs Fejes, CEO and president at IT consultancy EPAM Systems, said his company's change management process involves business users to uncover requirements that centralized IT departments might overlook.

"We need to work backwards from the client -- from the business users -- and really understand the requirements," Fejes said.

Data modernization or platform changes usually start with IT or data teams, Fejes said. But IT or data managers might not understand how business users actually work with the data sets and platforms they provide, including dependencies on any vibe-coded tools or other shadow IT systems, used to connect to corporate data.

EPAM uses tabletop exercises to explore how planned changes to data sets, platforms or architectures would affect users. For example, what would happen if users no longer had access to a particular data set?

The exercises identify connected systems, processes and dependencies between data sets and data products, Fejes said. Unlike a premortem, their main purpose is data usage discovery and requirements gathering.  

Excerpt originally published in TechTarget. Read the full article here.

Learn more about how EPAM helps clients build the foundations for AI-ready enterprise data here.