Anybody that has studied English grammar, whether as their native tongue or as a foreign language, has at some stage been dumbfounded by one of the many exceptions to the basic rules as defined in grammar and spelling. This article discusses how data governance can learn lessons from the complexities of English grammar.

An entertaining article by the Financial Times’ Michael Skapinker, discusses how the English language is constantly adapting to changing cultural circumstances. This means that grammar and spelling that was at one time acceptable, become unacceptable.

Eventually, these original uses of language may become common again – by definition changing the rules of language again.

Data governance lessons from English grammar

Any data-intensive project, and any data governance program, need to absorb two critical lessons from this experience.

1.) Data rules must be fit for purpose

The grammar and spelling rules for data need to be defined to ensure that data is fit for purpose.

Data rules and definitions (called metadata) may include a business glossary of business terms, definitions of calculated fields, key business indicators (data quality rules) and attribute definitions, to name a few.

 Ensuring common use and reducing ambiguity among the data users helps to ensure that data is fit for reuse and is, therefore, a critical success factor for enterprise projects such as data governance and master data management.

2.) These rules must be living!

Modern business drivers require the ability to adapt quickly and continuously to changing business circumstances – whether driven by new legislation, by mergers or acquisitions or simply by competing for a new client segment or adapting to a competitor’s new product launch.

Metadata that is buried in passive documentation is likely to be ignored by business users.

Active metadata, on the other hand, is exposed and used by the business to measure, validate and track data compliance to business needs.

New business requirements can be documented, for example in the enterprise data quality suite, which supports the implementation of these rules across various enterprise systems.

Like English spelling and grammar, last year’s rules may no longer be relevant or correct. In fact, when coming to business terms, part of the complexity arises from the fact that these are based on natural language and actual use, which, like the language, may evolve over time.

Metadata that is being actively used by systems and business users is far more likely to be kept current, and, in staying current maintain its value.