Data are an enterprise asset, and data governance (DG) is about establishing policies, processes, rules, standards, and controls around data to improve the quality of data and ensure data security and privacy.1
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However, organizations struggle to implement data governance and, as a result, face a large number of challenges. These challenges either prevent the DG program from gaining ground in the first place or derail the program at some point after takeoff.
According to data-governance expert John R. Talburt, “The first adopters usually had to make two, or even three, attempts before seeing any success. However, like any new paradigm, it takes time to iron out the wrinkles.”
Data governance and the car brakes
One of the challenges around data governance, and one of the key reasons behind data governance resistance and failure in organizations, is that data governance is viewed as unnecessarily restrictive. But that doesn’t have to be the case. Varun Pant, director of IT for Swati Consultancy and formerly national president for DAMA Australia, says, “Data governance is like a brake in a car. It is not there to slow you down, but enables you to go faster, more safely.”2
Brakes can be viewed as restrictive but are necessary for a safe journey, and their appropriate application can avert accidents. However, if applied when not necessary, they can slow progress.3 Like brakes in vehicles, data governance is a necessity that can empower business.
Restrictive nature of data governance
Existing laws and regulations are constantly revised and new laws and regulations created and enforced to prevent recurring economic disasters, corporate scandals, and fraud. All organizations are constrained to comply by the laws and regulations; failure to comply can be extremely costly. Data governance processes ensure that organizations manage and retain data as defined or required by the law and regulations, protecting the business from fines, penalties, and reputational damage.
Regulations such as Sarbanes-Oxley, Dodd-Frank, Basel II, and the General Data Protection Regulation (GDPR) have challenged organizations to improve their data quality and to create controls and formal accountability for data. Control is important to ensure data protection and quality. However, this can come at a cost. Privacy and security can’t be achieved without controlling or restricting accessibility, which limits what people can do with the data, thus reducing opportunities for use and productivity.4
For example, to access data that were formerly accessible, users will now have to navigate several levels of approval before gaining access. These processes and restrictions can be heavy, inflexible, and time consuming, and can adversely effect operational efficiency and productivity.
In the digital world, where the economy is driven by data, data governance is a “must have” to ensure organizations are making the best of data. However, good governance must balance security, accessibility, productivity, and enablement so processes are streamlined to minimize accessibility cycle times and ensure a less disruptive and more noninvasive data governance implementation.
A change in mindset is necessary to get buy-in from all levels in the organization. Change management will be required, as well as support from good stakeholder management, appropriate training, and effective communication at all organizational levels to ease people’s journey into new ways of working.
Conclusion
In the digital world, where the economy is driven by data, data governance is a must-have to ensure that organizations make the best of their data. However, good governance must balance security, accessibility, productivity, and enablement, as well as ensure that processes are streamlined to minimize accessibility cycle times. That way, an organization’s data governance implementation will avoid unnecessary disruption.
References
1. Mahanti, Rupa. Data Governance and Compliance: Evolving to Our Current High Stakes Environment. Springer, 2021.
2. Pant, V. “Data governance is like a brake in a car.” LinkedIn, accessed Aug. 5, 2020.
3. Mahanti, Rupa. Data Governance Success: Growing and Sustaining Data Governance. Springer, 2021.
4. Chen, A. “Breaking data myths: Highlights from Tableau CEO’s keynote at #Data17.” Centric, accessed Dec. 26, 2017.
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