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Knowledge Integrity:
Information Insurance?

  Column published in DM Review Magazine
September 2004 Issue
  By David Loshin

In our information quality practice, the most significant driving force (and one that is well-received by the clients) is in transforming an organization from being a reactive information quality (IQ) environment to being a proactive one. The difference is intuitive: a reactive organization addresses IQ issues as they arise, while a proactive organization attempts to eliminate problems before they occur.

The strategy for developing the proactive environment involves, among other things, three aspects:

  1. Understanding the kinds of IQ problems that may exist within the organization, how those problems are identified and how to clearly determine when a violation occurs.
  2. Determining the impacts and costs incurred when each problem erupts.
  3. Being able to clearly define rules that can be used for ongoing monitoring of information to catch problems at the earliest point in the process.

The main idea is that if we know what kinds of problems might happen and we have a general idea of the costs for reacting to each problem, then by catching the problem at its earliest occurrence, we can reduce the overall impact and, consequently, the overall cost. Of course, it would be great if we could eliminate problems before they occur, especially if there are many major impacts associated with those problems. Yet there are two major challenges that must be addressed before progress can be made on the "proactive" front: individual intransigence and the need for budget justification.

The issue of intransigence is not meant to imply any value judgment on any individual within the organization. Rather, one's natural inclination regarding problem solving requires the problem to exist before one can solve it. However, in the proactive approach, the goal is to eliminate problems before they appear, which is a contradiction of sorts. In other words, we are seeking to eliminate problems that don't yet exist.

Instead, we expect that we should be able to anticipate those problems and institute monitoring to flag and identify the expected problem. Yet, while we continue to build a reservoir of problems to be identified and eliminated, we still monitor all the previously defined problems. If our predictions are accurate and our monitoring is well designed, the problems effectively disappear before they ever manifest themselves.

The second challenge is the need to establish a business case for IQ improvement before an investment is made. This means evaluating the costs associated with poor information quality (i.e., how much does each problem cost you?) and justifying the expense by showing that the investment will result in cost elimination or cost avoidance. We just said that if our approach is the proactive approach, we end up eliminating problems before they appear - does that mean that we are trying to justify the expense of building an IQ program based on the costs associated with problems that will never crop up?

This argument sounds silly, of course, but it is not too much different from some real conversations I have had with clients about how to "sell" IQ within the organization. The result is frequently a standstill - one cannot justify the expense without knowing the costs, and the costs are not necessarily knowable unless the problems to be prevented actually occur.

The reality, however, is that one might justify the expense based on knowledge of how much the problems have cost in other environments. We often deal with this kind of model without giving it a second thought.

Example 1: There is some commitment and expense associated with brushing your teeth twice each day - a time commitment and a cost commitment (toothbrushes and toothpaste). We do it because it is a proactive measure to prevent cavities, which can be addressed, but not without different kinds of commitments (dentist appointments and fees). Clearly, the cumulative costs of tooth brushing outweigh those of tooth decay and extraction.

Example 2: Every month, we pay a premium to a health insurance company for a policy that covers the charges associated with healthcare. Even though most months we don't need to go to the doctor, we can feel comfortable that the expenses will be covered when medical care is needed. However, if we consider the amount we pay for insurance when we are relatively healthy people, we might find that we pay a lot of money even if we are not using any healthcare services. We do it because there are some problems that might occur that could incur significant costs in the absence of insurance.

Health insurance is a very good example because the model of healthy individuals resembles that of healthy information, although the documented body of knowledge about health problems far outpaces that of documented IQ problems. As that knowledge base grows, it will be time to think about justifying IQ using an "insurance model" instead of a cost avoidance model. If you have firsthand experience with evaluating the costs of poor information quality, I would love to hear from you. I will attempt to summarize and post your experiences in upcoming columns.


For more information on related topics visit the following related portals...
Data Quality.

David Loshin is the president of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. Loshin is the author of Enterprise Knowledge Management - The Data Quality Approach (Morgan Kaufmann, 2001) and Business Intelligence - The Savvy Manager's Guide and is a frequent speaker on maximizing the value of information. Loshin may be reached at loshin@knowledge-integrity.com.

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