Behavior modification


The challenge of data/information management practitioners attempting to initiate an analytics program intended to benefit a target audience is that after the analysis is completed, there is no control over how the results are used, or if those results are used at all.

In my last post, we considered modeling the business processes to be enhanced with analytic results and to determine where in the process the behaviors of informed decision-makers were not aligned with expectations. Because the success of the analytics process is bound with improvements in the business results, the goal is to achieve the desired results by ensuring that all parties, especially on the business side, are using the actionable insights in the optimal way.

It may be possible to coerce or encourage those individuals to employ the analytical results, but a good approach is to associate incentives with modified behavior. Reflecting again on the sales prospect analysis example I have used over the previous two posts, one approach might be to compare the difference between those sales people who have adopted the use of analytical prospect scoring and those that have not.

Some key metrics might include the response rate, the closure rate, the volume of goods sold and the aggregate commissions paid. This last measure is quite important as it highlights the reward for adopting the use of analytics: increased compensation.

The motivation of a financial incentive is often enough to warrant at least interest in understanding the level of effort necessary to change behavior. If the amount of effort is seen by the data consumer to be small in comparison to the potential benefit, it is worth modifying his or her behavior. While that desire is necessary, it is not sufficient. Next time we will discuss management’s role in transitioning behavioral tactics.


About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at

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