A few weeks ago, Thomas C. Redman posted Demand the (Right) Right Data on the Harvard Business Review blog, about how managers should set the bar higher, in terms of data.
Why are managers so tolerant of poor quality data? One important reason, it seems to me, is that most managers simply don't know that they can expect better! They've dealt with bad data their entire careers and come to accept that checking and rechecking the "facts," fixing errors, and accommodating the uncertainties that using data one doesn't fully trust are the manager's lot in life.
Although Redman suggests that managers should demand higher quality data, I immediately thought about how to check the quality of SLIM-DataManager databases using the Validate function and SLIM-Metrics.
If you're using SLIM-DataManager to create your own historical database, you can use the Validation feature to help you demand the (right) right data. The Validation feature in SLIM-DataManager analyzes the projects in your database, highlights suspect projects, and offers a brief explanation tool tip. Simply go to File|Maintenance|Validate to run this feature and wait for SLIM-DataManager to analyze your database. If SLIM-DataManager detects anomalies, it will highlight that project in blue. If you hover over that project, a tooltip will explain what is wrong with that project data and what you need to take a second look at.