Addressing the Evolving Data Privacy Landscape with Data Governance

Addressing the Evolving Data Privacy Landscape with Data Governance

In the next 5 to 10 years, most countries across the globe will implement some type of data privacy and protection regulations. As these laws become our new reality, enterprises must create efficient and scalable data governance frameworks.

Data is your organization’s most strategic asset today – which is why it should be protected at all costs.

Without comprehensive data protection in place, organizations are well-aware that their most critical data is vulnerable to cyberattacks. While industry leaders have already invested time and resources to ensure security, 90% of these decision makers still believe they are falling short of addressing cybersecurity risks.

The reason? Traditional data management solutions and the changing privacy landscape.

According to Microsoft, these traditional solutions can be overly complex and has multiple unconnected and often duplicative processes. Plus, it embodies the usual one-size-fits-all strategy in place that is not enough to meet the fast-paced digital business anymore. Using this kind of approach results in exposure to infrastructure gaps that threat actors can easily exploit.

Adding to the problem is meeting compliance regulations. Organizations need to be updated with the latest mandates and privacy requirements. While this is a positive step to ensure that consumer data is well protected, it is also creating challenges for enterprises to align their cybersecurity frameworks to evolving regulatory standards.

What enterprises need to meet these issues is an adaptable data governance approach.

Data governance itself offers a proactive, holistic approach to help enterprises embed security in the entire lifecycle of their data. It is an organization’s system of all its policies, responsibilities, and procedures to minimize risks, increase data value, meet compliance requirements, and improve internal and external data usage and communication.

But what business leaders need is not just simple data governance: they need adaptive data governance for flexible, rapid decision-making processes in data management and security. Without enabling a modern approach to data governance, Gartner asserted that 80% of organizations that seek to scale their digital business will fail by 2025.

How should organizations build an effective data governance framework?

To implement an adaptive data governance framework, key decision makers can start with the following action plans.

1. Construct a data map of all your assets. Knowing where and who has access to your data is the first step in protecting your data. Create comprehensive data classifications with clear access details. It is recommended to adopt a cyber-resilient data storage architecture for easier data scanning and classification.

2. Identify accountability and decision protocols. Now that data classifications and roles are established, assign access and permission controls for each employee, guest, partner, or vendor. Also, setting up a compliance team together with a permission management system will be a great way to monitor and report any suspicious activity.

3. Conduct regular audits of data. Threat actors are always on the prowl to identify vulnerabilities - and enterprises should double their efforts in getting ahead of them. Consistent and end-to-end cybersecurity audits can help identify existing threats and possible vulnerabilities in an organization’s data infrastructure.

4. Apply multiple governance styles. To address the demands of existing and emerging use cases, adaptive governance requires a combination of multiple approaches.

Gartner outlined four data governance styles, designed for digital business:

  • Control - Governance based on numerous data protection protocols (e.g. GPDR, CCPA, POPI)
  • Outcomes - Governance based on balancing risk and achieving business outcomes
  • Agility - Governance based on creating value-driven decisions from stakeholders
  • Autonomous - Governance based on real-time algorithms from people and systems


5. Track both structured and unstructured data. How do we ensure that data governance is ensured in both structured and unstructured data sets? Tracking data with a matrixed strategy can help capture domains and sub-domains for both data types. This way, enterprises also have records of which business sectors generate data versus simply reading, accessing, or removing data assets.

6. Delete data that you no longer need. One of the easiest ways for efficient data governance? It is simply deleting unnecessary data. Fewer data means less probability of breaches. After all, new privacy standards impose certain durations and purposes for storing data.

7. Promote data literacy across the organization. The human element in any transformative business is important. To unlock adaptive data governance, CEOs must invest in promoting data literacy among their teams. Employees must understand their role in protecting data, and how they can better use this data to innovate.


Most organizations already have data governance systems in place. But these models will need some modifications every now and then - and enterprises must be agile enough to successfully govern data and achieve better business outcomes.   


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Security. Photo by Dan Nelson: on Pexels.

Padlock. Photo by FLY:D on Unsplash. 

Developers. Photo by Desola Lanre-Ologun on Unsplash.

CXO Connect Middle East Team