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POPIA drives data governance best practice to top priority

With the fast-approaching POPIA deadline, there is growing urgency to implement data governance best practices to control and monitor data management.
Veemal Kalanjee
By Veemal Kalanjee, MD of Infoflow.
Johannesburg, 12 Apr 2021

Data governance has become a key priority in recent years, as organisations look to maximise the value of their data goldmines and align with global regulations and best practice.

With the deadline for compliance with the Protection of Personal Information Act (POPIA) in just a few months, there is growing urgency to implement data governance best practices to control and monitor data management by people, processes and technologies, and to ensure all data is fit for purpose and secured, yet accessible to the right people and processes at the right time.

South African organisations are at various levels of data governance maturity, since the need for data governance in an organisation is largely driven by specific business and operational pain points and/or data management initiatives, such as compliance and regulatory evidencing, digital transformation, or access to reliable data.

The impact and implementation state of these requirements directly dictates where an organisation would find itself in the data governance journey, and in South Africa specifically, we have found clients at various stages in this journey.

Some organisations are at the early stages with no framework for data governance, but due to impending regulations such as POPIA, have been forced to start putting in controls for the governance of their data to meet requirements for the regulation.

Others are more mature and have identified data as a strategic asset and have gone as far as having a defined data governance framework together with the key enabling processes and technologies in place to assist with the execution of the capabilities within the framework.

The cloud effect

Cloud computing has introduced a paradigm shift in the way companies process their data. However, the principles of data governance have remained largely the same.

What have evolved are the underlying data management capabilities of assessing, curating, cleansing, securing and accessing data. These technologies have had to adapt from being largely on-premises to becoming cloud-native.

Cloud computing has introduced a paradigm shift in the way companies process their data.

Data management now needs to be practised on data sources and targets within the cloud versus within the firewalls of an enterprise. This in itself has introduced key focal points for data sovereignty, data gravity and, of course, data governance.

For organisations with a well-established governance practice, the transition to cloud is somewhat easier from a pure governance perspective as the principles of the governance framework still apply within the cloud.

Artificial intelligence (AI) and machine learning (ML) may promise to turn masses of data into business insights that can shape strategy. However, AI and ML are dependent on good quality, trusted data. Without any of the principles of data governance and broader data management in place, trusted data becomes difficult to attain.

Tips for better data governance:

  • Effective data governance results in quality, trusted data – an area in which many organisations still fall short.
  • Best practice data governance can start small and be implemented in manageable iterations, with organisations identifying the easy initiatives or pain points which will deliver quick value with minimal effort (aka the “low-hanging fruit”).
  • Business and executive sponsorship is key to ensuring the data governance programme retains momentum and does not lose steam. Data owners across the organisations also need to buy in for successful governance. It is useful to get programme champions aboard from the outset of any data initiative to define data governance and the business case for data governance best practice.
  • Selection of enabling technology to automate and support the data governance and data management journey must be ensured.
  • Gartner’s recommendations for more effective data governance include mapping the data assets to a specific business purpose, and investing in metadata management solutions to manage this lineage.
  • It should also be noted that data governance is not a finite project – it is an ongoing practice or formal business function that should be instituted in the organisation.

Wherever an organisation is in terms of data governance maturity, and whatever the drivers for improving data governance, now is the time to set the data foundations in place and start embedding a culture of data governance and responsibility throughout the organisation.

This not only helps improve business through access to quality data for decision-making, it also helps to prove compliance and reduce organisational risk.


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