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Data governance

10 design principles to boost data governance adoption and success

In recent years, the volume of data used within organisations has increased dramatically. However, ungoverned data is messy, lacks rules and inhibits productivity. Hence, being able to properly manage these growing volumes of data is rapidly becoming vital to harness data for business value. This increased recognition is driving governments and companies of all sizes to invest in data and its governance. Companies that are not will be or are significantly putting their value creation at risk. Think of it as purchasing a fancy new car: would you buy one without knowing how to drive it?

The essence of data governance revolves around specifying a cross-functional framework for managing data as a strategic enterprise asset. In doing so, data governance specifies decision rights, accountabilities and processes related to data assets with the objective of ensuring the quality, consistency, usability, security, privacy, and availability of the data. Good data governance helps to ensure that those who need to use data can find it, understand it and trust it, which fuels data-driven decision-making across your organisation.

Today, there is a widespread recognition that in order to successfully run a data-driven organisation it is vital to:

  • Establish standards, policies, and procedures for the usage, development, and management of data
  • Design the right organizational structure
  • Implement the technology infrastructure to support data governance

Yet, the successful adoption of data governance remains a problem for most companies. Many organisations have conducted a proof of concept or successfully implemented the plethora of functionalities provided by data governance platforms. Proof of concepts manage to eke out small financial gains of the organisation and excitement often grows as functionalities are being materialised. But then months or years pass without bringing the big wins organisations expected.

Companies substantially struggle to scale from proving the functionalities of data governance tools to companywide leverage. These challenges are prominent across industries and irrespective of the tools, systems or models used. Why are many data governance programmes running out of steam, losing funding, and ultimately falling by the wayside?

In this article, we discuss 10 design principles to maximise proper adoption of your data governance programme. These principles have been inspired through our joint work with clients worldwide. In all of this, there is one absolute truth: automatic adoption of data governance programmes doesn’t exist (yet).

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