The 7 Deadly Sins of Data Mining and How To Avoid Them

3

Our M2010 Data Mining Conference keynote speaker, Dick De Veaux from Williams College just finished his entertaining and informative presentation. He thoughtfully noted that our location (Las Vegas) is very appropriate for the subject of his presentation.

Are you guilty of any of these data mining sins? Luckily, Dick also presented the seven virtues of data mining to help absolve us of our sinful ways.

Seven Deadly Sins of Data Mining
1. Not asking the right questions.
2. Not fully understanding the problem.
3. Underestimating data preparation.
4. Ignoring what's not there.
5. Falling in love with your models.
6. Going it alone.
7. Using bad data.

Seven Virtues of Data Mining
1. Define the problem.
2. Prepare the data, use domain knowledge.
3. Be open to new methods and models. Keep the toolbox open.
4. Be aware of missing data, create dummy variables.
5. Work in teams.
6. Ensure data quality.
7. Use models, not just associations.

Share

About Author

Michele Reister

Marketing Specialist

Michele Reister has worked in the Education Division at SAS since 2004. During that time she has played many roles including marketing training courses, developing product bundles, managing conferences and overseeing the division’s discount programs. Currently, she is responsible for the division’s social media strategy. Michele holds a BS in Management and Information Technology from Daniel Webster College and an MBA from University of North Carolina at Chapel Hill. Michele is a perpetual student herself and is constantly looking for better ways to serve SAS’ user population. When she’s not expanding her knowledge of marketing, Michele enjoys group fitness classes, cooking, volunteering, reading and chasing after her two children.

Back to Top