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Recently I attended a seminar on a commercial reporting and data sharing product. In the sales material and discussion, the phrase “Single Version of the Truth” was used several times. But what does it mean?
“In computerized business management, svot, or Single Version of the Truth, is a technical concept describing the data warehousing ideal of having either a single centralised database, or at least a distributed synchronised database, which stores all of an organisation's data in a consistent and non-redundant form.” - Wikipedia
The concept is attractive to decision makers who collect and analyze information from multiple departments or teams. Here's why:
“Since the dawn of MIS (Management Information Systems), the most important objective has been to create a single version of the truth. That is, a single set of reports and definitions for all business terms, to make sure every manager has the same understanding.”
Sounds simple, doesn’t it? Sales pitches for svot imply that if distributed data sources were linked into a single master repository, the problem of unambiguous, consistent reporting and analysis would be solved. Yet reports are often based on different data using different definitions, different collection processes, and different reporting criteria.
I frequently heard W. E. Deming say, “Follow a process, get a number. Change the process, get a different number.” He was talking about sampling, but the observation is equally applicable to any type of data collection. Data base and reporting packages do not guarantee standardized data collection processes. Furthermore, it is not enough to standardize data collection and data storage (to link and synchronize an organization’s disparate data stores), because data extraction, analysis and reporting processes can still introduce variation in the output.
For example, two reports may look different (and encourage conflicting decisions) because they:
This Computerworld article makes several key points, including: Truly valuable data must be mined; and Truth changes with time. The article reinforces the need for good data analysis.
It is dangerous to make decisions on output without understanding the data used to generate the output. Decision makers must take the time to understand what they are looking at. Analysts must take the time to make things clear and define their terms.
It is all too easy to create complex reports. It is much more difficult to create clear and concise reports.
Paul Below has over 25 years’ experience in measurement technology, statistical analysis, estimating, forecasting, Lean Six Sigma, and data mining. He serves as a consultant for QSM, providing clients with statistical analysis of operational performance leading to process improvement and predictability.
Mr. Below is a Certified Software Quality Analyst, a past Certified Function Point Specialist, and a Six Sigma Black Belt. He has been a course developer and instructor for Estimating, Lean Six Sigma, Metrics Analysis, Function Point Analysis, as well as statistical analysis in the Masters of Software Engineering program at Seattle University. He has presented papers at numerous conferences. He has one US patent and two patents pending.