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Governance

Governance
Published: 07 February 2018

The data journey starts with governance and accomplishes trust. To do this, data must be developed, tested and feedback noted. To implement data governance, a roadmap must be created and followed, and a team appointed to ensure ongoing governance. This team must employ comprehensive governance strategies to ensure data is properly collected, managed and used.

Because data is a dynamic and evolving asset, governance is required throughout the lifecycle of the data. The governance roadmap must include:

  • Quality assessment: The quality of the data collected from its various sources determines the ultimate value of that data to your organisation. Ongoing assessment and feedback is vital for continual improvement of data quality over time.
  • Data protection during exploration and analysis: Access control is key in ensuring no data is changed without signoff from the governance team. While data scientists require broader access to data, larger groups - such as your clients - need restricted access managed by the governance team.
  • Setting access control at the data level enhances the data’s security journey.
  • Tracking and auditing: the data journey must include the governance agent's understanding of how the data has been modelled to ensures insights are correct and that errors can be traced and fixed. For auditing purposes, governance requires that systems are in place to document who touched what, when, why and how.

Data governance is the oversight aspect of enterprise information management and comprises people, processes and technology. While the data governance team should understand the business value of the data, the data management team should be familiar with the intricacies of the data and should make policy recommendations regarding the data. The best data governance is that which guides the appropriate use of data which in turn leads to better insights, information and innovation.