Information Quality Means More than Accuracy – Guest Author James Funk
By Richard Wang
In last month’s post we talked about one of the foundations required for high information quality, accurate data. Many people think that is the sole prerequisite for achieving high information quality. Several people have written books discussing what you can do to increase the accuracy of the data that is used within your organization. These authors have identified practical and effective actions that can be used to increase data accuracy. But, as we have indicated before, this is typically not enough to guarantee highly accurate information within today’s complex and geographically dispersed organizations.
Let’s take the example of an organization that has multiple divisions each with its own information processing capabilities and also has a decentralized management structure. In this situation the general manager of the division is responsible for all business activity and for the business results that are then communicated to corporate headquarters. Each division is responsible for gathering and providing the information needed by the business participants to fulfill there designated tasks.
The people in each division are very happy because the information that they can access is deemed to be very accurate and meets their individual needs. They are very satisfied and, if asked, would indicate that they have access to very high quality information. In fact, they would be correct in making that statement.
Now corporate headquarters wants to implement a new benchmark that will be gathered for all divisions. As an example let’s say that the benchmark is gross sales or gross revenue per customer/client. Each division is gathering the required data and each division can easily provide the information to headquarters. When the information is put into a data warehouse and analyzed at headquarters, the corporate analysts are surprised. The results show that some of the divisions do not do as well on the new benchmark as expected. The analysts begin to question the accuracy of the data that has been sent. As stated before, each division is sure that the data it has provided is accurate. They will state that any inaccuracies have been the result of actions at corporate headquarters. The resulting discussions can be timely and exasperating.
There are several reasons that could explain the differences. If the divisions are located in different countries, there can be different legal definitions as to what constitutes gross sales or gross revenue. If corporate headquarters has not specified what they mean by the term, it is likely that each division is sending the data as used to prepare the financial statements using the rules and regulations in place in their country. These differences can be eliminated by the corporate analysts specifically identifying what the term “gross revenue” means and also identifying that they understand that the new value may not be consistent with the value reported in the financial statements of the division. This means clear communication to both senior corporate executives and division management that the values may be different but the need to have a consistent base for calculating the new benchmark requires the change in value. Do not underestimate the amount of time and effort that is needed to explain the differences. Also be prepared to constantly receive questions about the accuracy of the new values from new senior executives and division managers. Also make sure that any changes to the gross sales figure are agreed to by division management. If corporate analysts change the numbers unilaterally, then the resulting values will not match the values contained in the financial statements. Both senior corporate executives and division managers will likely hold the opinion that the new values are not accurate. In this case, senior executives will tend to favor the numbers in the financial statements since they are the numbers with which they are the most familiar and comfortable. It will also help if the name of the new value is changed so it is not confused with the existing term that is used in the financial statements. It takes a lot of effort to familiarize people with the new term but not as much as would be needed to explain the differences in the multiple uses of the same term.
How divisions identify who is a customer or client may also result in inconsistencies in the new benchmark. I will leave it to the reader to think through how this could occur in their situation and the actions that could be taken to eliminate these differences.
This has been a quick start to our discussion that information quality depends on more than the accuracy of the data and that the context in which the data is captured and used is very important. Data that is accurate in one context may not be accurate in another. We will continue this discussion in the next few issues.
As a quick aside, I direct you to the July 23rdedition of the Wall Street Journal. There was a column about a new business methodology they term “management by data”. It states that the premise behind the methodology is that successful enterprises collect more information and analyze it better. Stanford business professor Robert Sutton is quoted that prior management techniques are based on “faith, fear, superstition and mindless imitation. However he also understands that running a complex organization cannot be reduced to a spreadsheet and that even detailed statistical analysis has limitations.
The article and relevant books by Professor Sutton and Thomas Davenport highlight the new methodology. If it gains some traction within the business community, information quality or the lack of it will take on increasing importance.
We look forward to our continuing conversations about information quality and wish you success in your information quality journey. If you have questions about what we have discussed or want more clarity about what we have said, contact us at firstname.lastname@example.org@mit.edu, or visit http://mitiq.mit.edu.
About the Author
Richard Y. Wang is Director of MIT Information Quality (MITIQ) Program at the Massachusetts Institute of Technology. He also holds an appointment as University Professor of Information Quality, University of Arkansas at Little Rock. Before heading the MITIQ program Dr. Wang served as a professor at MIT for a decade. He also served on the faculty of the University of Arizona and Boston University. Dr. Wang received a Ph.D. in InformationTechnology from MIT. Wang has put the term Information Quality on the intellectual map with myriad publications. In 1996, Prof. Wang organized the premier International Conference on Information Quality, which he has served as the general conference chair and currently serves as Chairman of the Board. Wang’s books on information quality include Quality Information and Knowledge (Prentice Hall, 1999), Data Quality (Kluwer Academic, 2001), Introduction to Information Quality (MITIQ Publications, 2005), and Journey to Data Quality (MIT Press, 2006). Prof. Wang has been instrumental in the establishment of the Master of Science in Information Quality degree program at the University of Arkansas at Little Rock (25 students enrolled in the first offering in September 2005), the Stuart Madnick IQ Best Paper Award for the International Conference on Information Quality (the first award was made in 2006), the comprehensive IQ Ph.D. dissertations website, and the Donald Ballou & Harry Pazer IQ Ph.D. Dissertation Award. Wang’s current research focuses on extending information quality to enterprise issues such as architecture, governance, and data sharing. Additionally, he heads a U.S. Government project on Leadership in Enterprise Architecture Deployment (LEAD). The MITIQ program offers certificate programs and executive courses on information quality. Dr. Wang is the recipient of the 2005 DAMA International Academic Achievement Award (previous recipients of this award include Ted Codd for the Relational Data model, Peter Chen for the Entity Relationship model, and Bill Inman for data warehouse contributions to the data management field). He has given numerous speeches in the public and private sectors internationally, including a thought-leader presentation to some 25 CIO’s at a gathering of the Advanced Practices Council of the Society of Information Management (SIM APC) in 2007. Dr. Wang can be reached at email@example.com, http://mitiq.mit.edu