Demand the (Right) Right Data with SLIM-DataManager

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.

Losses Loom Larger Than Gains

Anyone who has gambled (and lost) knows the sting of losing.  In 1979, Daniel Kahneman and Amos Tversky, pioneers in the field of behavioral economics, theorized that losses loom larger than gains; essentially, a person who loses $100 loses more satisfaction that what is gained by someone who wins $100. Behavioral economics weaves psychology and economics together to map the irrational man, the foil of economics' rational man. 

How can I leverage this theory for software development?

According to the QSM IT Software Almanac (2006), worst in class projects took 5.6 times as long to complete and used roughly 15 times as much effort with a median team size of 17, and were less likely to track defects. 

One way you can leverage your worst in class projects would be to use them as history files in SLIM-Estimate, which would adjust PI, defect tuning, etc., to match how you have developed software in the past. Don Beckett recently discussed how to tune effort for best in class analysis and design.

Another way to leverage your worst in class projects would be to build a "project graveyard," that is, a database of your organization's worst projects, and load it into SLIM-Metrics. In SLIM-Metrics, you can analyze duration, peak staff, average staff, and defects to view your own organization's weaknesses. Depending on how well documented your SLIM-DataManager database is, you could analyze some of the custom metrics that ship with SLIM-Metrics, such as reviewing who the project was built for (customer metric) and complexity.

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