“Data is the manifestation of a business. It represents its customers, employees and suppliers; its activities and transactions; its outcomes and results.” – Z. Panian
When handled correctly, data can be an organization’s most important resource. This leads many businesses to set rules that dictate how their data will be used, developed and managed. Of course, rules alone are not enough to ensure that data and information are aligned with the needs of the business; people, processes and technology are needed to enforce them. This comprehensive approach is called data governance.
In her book, Data Governance Simplified: Creating and Measuring Trusted Data for Businesses, Holly Starling compares data governance implementations to building a puzzle with access to the original picture. Indeed, it is possible to build the same puzzle without the reference, but the picture illustrates the groupings of different colors and shapes, provides context for each puzzle piece, and provides a clear overview of what to reconstruct.
One could argue that Starling’s puzzle analogy is inaccurate when applied to data, because obtaining the desirable “original picture” is not as easy as peeking at a puzzle’s cardboard box. Data is often scattered across multiple different business systems. However, addressing latent organizational, process, and technical issues concerning the data can improve its quality. Proper data governance enables companies to have a better picture that helps understand not only their own needs, but also those of their customers or clients.
Each data governance implementation varies from business to business, depending on operating models, systems, external regulations and corporate governance policies. In theory, companies could try a data governance initiative without an underlying technology infrastructure. Some might resort to spreadsheets and Word documents, but most will very quickly find out that this manual approach is devastatingly limited. Leading data governance implementations rely on a technology platform to ensure that employees can find the data they need, understand it, and trust it to make better decisions. An example of this is Collibra DGC, which allows management in a systematic, repeatable and scalable way.
In the article Some Practical Experiences in Data Governance, Zeljko Panian mentions that the most common business drivers of data governance initiatives are growing revenue, lowering costs and ensuring compliance. If these goals align to your business, data governance can provide the original picture for your puzzle.
Panian, Ž. (2010, January). Some Practical Experiences in Data Governance. In International Conference on Knowledge Management 2010 (ICKM 2010).
Starling, H. (2015). Data Governance Simplified: Creating and Measuring Trusted Data for Businesses. CreateSpace Independent Publishing Platform.