This document provides dos and don'ts for data mining based on experiences from various practitioners. It lists important steps like clearly defining objectives, simplifying solutions, preparing data, using multiple techniques, and checking models. It warns against underestimating preparation, overfitting models, and collecting excessive unhelpful data. Practitioners emphasize the importance of domain knowledge, transparency, and creating models that are understandable to stakeholders.

1. The Do’s and Don’ts of
DATA MINING
Based on real-world experiences
Published By
A
publicatio
n of

2. Data mining has come a long way over the past 300 years…

3. Over time, data
practitioners
have had their
fair share of

4. Change
the way you do things, or KEEP doing what works!

5. The Do’s of
✓
Data Mining

6. …According to Scott Terry
President
Rapid Progress Marketing and Modeling, LLC
www.RPMSquared.com

7. Do Create a
Clearly-Defined,
Measurable
Objective for
Every Project

8. Do Simplify The Solution
To Increase
Your Chances
of Success

9. …According to Gregory
Piatetsky-Shapiro
Editor
www.kdnuggets.com
@kdnuggets

10. DO ASK QUESTIONS.
Understanding the problem
and asking the right question
is more important than using
an advanced algorithm.

11. …According to Jim Kenyon
Director of IT Services
Optimization Group
www.optimizationgroup.com

12. Do Plan For Data To Be Messy
While data is available for mining projects in everincreasing amounts, it is the rare occasion when it
will arrive in a tidy, mining-ready format. More
typically, it will show up in multiple spreadsheets that
vary in format and granularity. These varied formats
frequently require hours (and hours) of ETL (Extract,
Transform, Load) time.

13. Do use more than 1
technique/algorithm.

14. Do cross-check data coming out
of the ETL process with the
original values, and with
project stakeholders.

15. …According to Falk
Huettmann
Wildlife Ecologist
His work is explicit in space and time, and looks
closely at the global effects of the economy.

16. DO BE INFORMED
Stay fluent on the latest data
mining concepts and
approaches, as well as data
mining history.

17. The Don’ts of
✗
Data Mining

18. …According to Scott Terry
President
Rapid Progress Marketing and Modeling, LLC
www.RPMSquared.com

19. DO NOT EVER…
I MEAN EVER
UNDERESTIMATE THE POWER
OF GOOD DATA PREPARATION

20. Do Not Ascribe Them
Mystical Powers and
Wrongly Think
“It’s All About the
Algorithms”

21. …According to Dean
Abbott
Founder & President
Abbott Analytics/Abbott Consulting
www.abbottanalytics.com
@deanabb

22. DON’T USE THE DEFAULT
MODEL ACCURACY METRIC

23. …According to Gregory
Piatetsky-Shapiro
Editor
www.kdnuggets.com
@kdnuggets

24. Don’t Overfit
With Big Data, it is easy to find
patterns even in random data. Use
appropriate tests such as
randomization tests to avoid finding
false patterns in test data, which will
not hold later on.

25. …According to Jim Kenyon
Director of IT Services
Optimization Group
www.optimizationgroup.com

26. Do not just collect a pile of data and “toss it
into the big data mining engine” to see
what comes out.
Domain knowledge is an important cross-check
on the variables being used. Extraneous data
can reduce model accuracy.

27. Do not underestimate the power of a simpler-tounderstand solution that is slightly less accurate.
A model a client cannot grasp is one
that will not be trusted as much as
one that “makes sense.”

28. …According to Falk
Huettmann
Wildlife Ecologist
His work is explicit in space and time, and looks
closely at the global effects of the economy.

29. Don’t forget to document
all modeling steps and
underlying data

30. Do not blindly
trust
assumptions made to satisfy
frequency statistics, as well as
p-values and AIC

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