Improving Data Quality is Hard Work – Guest Author James Funk

By Richard Wang

Recently the World Trade Organization heard testimony from the European Union that the Boeing Company “receives lavish government subsidies that give it an unfair competitive advantage over rival Airbus SAS.” It also heard testimony from the United States complaining about aid granted to Airbus. Each side has accused the other of supplying inaccurate data. The commission says the U.S. “grossly inflates the numbers” when it comes to Airbus support, while the U.S. argues that the EU unfairly counts as government grants $10.4 billion Boeing got from NASA for research services. The heated exchanges center on the understanding of what constitutes government aid to a private organization.

In another series of articles the task of reconciling global accounting standards was discussed. In the world of Accounting there are two standards for reporting company results. These are the Generally Accepted Accounting Principals (GAAP) and the International Financial Reporting Standards (IFRS). The Securities and Exchange Commission (SEC) took its first step toward embracing international accounting standards for all companies that file financial reports in the U.S.

The SEC voted unanimously to propose allowing companies based outside the U.S. to file financial results using International Financial Reporting Standards, or IFRS, as set by the International Accounting Standards Board — without reconciling the figures to U.S. Generally Accepted Accounting Principles, or GAAP, and highlighting the differences, as is now required.

The proposal is a “significant next step” toward one globally accepted accounting standard and having all companies speak the same financial language, SEC Chairman Christopher Cox said.

The decision is the latest by the agency to adapt to the increasingly global securities market and to address concerns that U.S. financial markets are losing their edge to London and Hong Kong. The move also raises concerns about ensuring American investors receive accurate and consistent information. Differences between the two accounting systems could make it difficult for investors to compare companies, even firms in the same industry. Under U.S. GAAP, research and development costs, for example, are generally expensed when they occur. Under the international standards, once a project gets to the development stage, costs are spread out over time. The upshot is that a company could show different operating income and net profit depending on which system they use.

One may ask what does this have to do with information quality within my organization. In most companies the sources for information are many. Each source collects the information based upon the meanings and understandings that it has about the terms that it uses. One may recall a series of advertisements that were printed by a leading vendor in the field. In one, three people approach the president of the organization with the results for a particular company operation. The numbers are different and the ad asks what should be done. Think about how difficult it is to get different portions of an organization to change the information it collects locally to meet the needs of the greater organization. There will be complaints that such efforts will penalize the profits of that part of the organization if they are made to change. Each source will state that their definition of the term is the correct definition.  In almost every situation the differences impact management or financial reporting more than they impact actual organization operations. No matter what one does, it will take a long time for successful change to occur.

When trying to minimize or eliminate such confusion over the meaning of the data within an organization, it is necessary to understand the underlying meaning and calculations that exist for the data. In some cases the differences will be difficult or impossible to eliminate because external definitions and regulations, such as government standards, drive how the data is collected or calculated. For companies that operate in many international locations it may be impossible to change the local definitions. In such situations it will be necessary to develop additional reporting procedures to allow for these legitimate local variations.  One solution is to allow for local reporting and to add an organization reporting that makes the necessary adjustments to the local numbers. In this case it is important that the new organizational numbers are only available to those who need to see them. Even then it is important that people understand that there are different numbers used and that they are correct for the context in which they are used. Care must be taken that people understand the importance of using numbers in their correct context. Care should be taken when labeling data in reports to ensure that the correct context is understood. Additional remarks should be added to reports when misunderstandings are likely because of the existence of the different contexts.

You can spend a lot of time trying to improve the accuracy of the data used within your organization. When dealing with management and financial reporting data, you should make sure that you are not just dealing with data representing different contexts within the organization.

What difficult data issues are you dealing with in your organization? Let me know and we can discuss potential solutions and practices for improving data quality within your organization. I can be reached at jimfunk@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 rwang@mit.edu, http://mitiq.mit.edu