Don’t Sweat the Small Stuff, Except in Data Quality

April was a busy month. I was the project manager on a new web application, nearly completed my first German web site (also as project manager) and released the book “Data Governance Imperative.” All this real work has taken me away from something I truly love – blogging.

I did want to share something that affected my project this month, however. Data issues can come in the smallest of places and can have a huge effect on your time line.

For the web project I completed this month, the goal was to replace a custom-coded application with a similar application built within a content management system. We had to migrate log in data of users of the application, all with various access levels, to the new system.

During go live, we were on a tight deadline to migrate the data, do final testing of the new application, and seamlessly switch everyone over. That all had to happen on the weekend. No one would be the wiser come Monday morning. If you’ve ever done an enterprise application upgrade, you may have followed a similar plan.

We had done our profiling and knew that there were no data issues. However, when the migration actually took place, lo and behold – the old system allowed # as a…

I did want to share something that affected my project this month, however. Data issues can come in the smallest of places and can have a huge effect on your time line.

We had done our profiling and knew that there were no data issues. However, when the migration actually took place, lo and behold – the old system allowed # as a character in the username and password while the new system didn’t. It forced us to stop the migration and write a rule to handle the issue. Even with this simple issue, the time line came close to missing its Monday morning deadline.

Should we have spotted that issue? Yes, in hindsight we could have better understood the system restrictions on the username and password and set up a custom business rule in the data profiler to test it. We might have even forced the users to change the # before the switch while they were still using the old application.

The experience reminds me that data quality is not just about making the data right, it’s about making the data fit for business purpose – fit for the target application. When data is correct for one legacy application, it can be unfit for others. It reminds me that you can plan and test all you want, but you have to be ready for hiccups during the go live phase of the project. The tools, like profiling, are there to help you limit the damage. We were lucky in that this database was relatively small and reload was relatively simple once we figured it all out. For bigger projects, more complete staging of the project – making dry run before the go live phase would have been more effective.