How Good Is Your Data?

Andy Jordan is President of Roffensian Consulting S.A., a Roatan, Honduras-based management consulting firm with a comprehensive project management practice. Andy always appreciates feedback and discussion on the issues raised in his articles and can be reached at [email protected]. Andy's new book Risk Management for Project Driven Organizations is now available.

 

I don’t know of many organizations that would claim that their historic project data is perfect. We all know that different areas of the business tend to use different tools, and that can cause problems with data incompatibility—both across departments and over time.

We also all recognize that a lot of historic projects have incomplete data, questionable data and flat-out incorrect data. That’s always been acknowledged, but it’s something that organizations have been prepared to accept, at least until recently.

But now, things have changed. To be optimally effective, AI tools need to consume massive amounts of historical data as part of their training, learning how organizations works so that they can provide the most appropriate guidance, and carry out tasks in the most effective way. As a result, organizations have been trying to improve the quality of their historic data…and that’s not something that’s easy to achieve.

When projects have been managed in similar ways, but using different tools, there is the potential to transform and convert data into a common data model to allow for consolidation of enterprise data. But if there has never been past consideration of how to view traditional and agile approaches in a single picture, then the data elements being maintained for those types of projects are likely to be very …

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