15 Aug 2007
Why would an executive care? There are two main reasons why every business and technology executive should consider the quality of data modelling to be core to their success.
The first is that information is a valuable economic asset (as argued in MIKE2.0 in the article the Economic Value of Information). Customer data, performance data, analytical information all combine to be an asset that is often worth multiples of billions of dollars. If a company had billions of dollars worth of gold, I’d expect business executives to want to review and understand how such a valuable asset was housed! Given that the data model is usually the main home for the information asset, the same should also be true. The data model cannot be delegated to junior technical staff!
Increasingly there is another reason for elevating the data model. Legacy information is becoming an obstacle to business transformation. As the price of storage dropped during the 1990s, new systems began also storing ancillary data about the parties involved in each transaction and substantially more context for the event. Context could, for example, include the whole sales relationship tracking leading up to a transaction, or the staff contract changes that led to a salary change. With the context as part of the legal record, there are operational, regulatory and strategic reasons requiring that any new or transforming business function do nothing to corrupt the existing detail. The data model is the only tool we have to map new business requirements to old data.
Given the complexity of data modelling, it’s not surprising that executives have shied away from speaking to technologists about the detail of individual models. A discussion on normalization principles would be enough to put most decision makers off!
In the Small Worlds Data Transformation Measure article MIKE2.0 introduces a set of simple metrics to indicate whether on average the data models of an organization or doing a good job of managing the information asset. Using the principle that information makes most sense in the context of the enterprise, it measures the level of connectivity and the degree of separation on average across a subset or all of the data models housing the information assets.