The Protection of Personal Information Act (POPIA) may have catalysed the recent focus on data governance, but organisations benefit from effective data governance in a myriad of ways beyond compliance.
By focusing on the people within the data environment, organisations can implement more effective data governance strategies and optimise the value of their data.
Many organisations view data governance exercises grudgingly, and often, only within the context of compliance with legislation such as POPIA. For them, a data governance framework may end up as little more than a guide which is not applied in practice. For many others, the value of effective data governance is recognised, but execution becomes a challenge.
Experts in the field are often called in to assist when organisations are working towards compliance, have a particular data governance business use case, or are addressing a burning data issue. Few embark on a comprehensive data governance exercise simply because it will position them to harness more business value out of their data.
Large local banks, for example, started out with compliance-driven initiatives such as FICA or KYC, but quickly realised they were achieving better insights from their data as a result, and were able to get value from it.
Start with the framework
To achieve optimal data governance, organisations should start with a data governance framework. This encompasses the principles of best practice data management and usage, and oversight thereof in the form of policies, rules and definitions that will apply to the broader organisation and break the silos of ad hoc and non-standard practices being employed.
Once these principles are in place, data can be leveraged in a governed manner, which will ultimately lead to better value realisation in data-driven strategies. The data governance framework articulates the execution of these policies and the use of data management competencies such as metadata management, data quality, data security and data integration.
People are one of (if not the most) essential pillars of a successful data governance framework.
It should be noted that a framework is akin to a living entity, which is not static and adapts and changes in line with operational and business requirements. Frameworks evolve over time and according to circumstances – it is not necessary to encompass the processes, components and stakeholders of the entire organisation all at once.
Businesses can start small, and develop frameworks to address specific use cases and mature these frameworks as it progresses.
Technology is part of data management competencies and is thus the enabler for the execution of a successful data governance framework. Without it, the execution of the framework often becomes a manual or resource-intensive exercise that can cause a data governance programme to fail.
Pay attention to the people
What is often overlooked, however, is that people are one of (if not the most) essential pillars of a successful data governance framework. They drive and define the principles of the framework and are ultimately responsible for successfully executing the framework and data strategy.
In many cases, it will require a cultural shift among people in the organisation to assist in understanding the importance of data and the need to leverage it in a governed manner to benefit the business.
However, once this hurdle is passed, the task of assigning roles, developing skills, garnering sponsors and aligning the organisation will become easier.
A key aspect with data governance is the definition and assignment of roles and responsibilities around the management and control of data. Whether this is a domain owner, data owner or data steward, it becomes critical to define where the responsibility lies with decisions around key data elements.
But the data stewards’ responsibilities must be clearly defined. People within the organisation need to understand whether they are responsible for a particular set of data or a data domain, and exactly what their responsibilities entail, whether this is managing the quality or security of data, or how it is processed.
Should they be responsible for the quality of data, for example, it should be clear which data they are responsible for, what level of quality is required, and what the measures for success are. These measures might include metrics on completeness, duplicates, accuracy and consistency of data.
Data quality technology can help enforce this responsibility by measuring the effectiveness of the role through key performance indicators. This is where the data management lifecycle and its capabilities also play a vital role in ensuring the framework does not only remain a concept in a manual or handbook, but gets implemented.
Contributors such as business and IT subject matter experts are also crucial, as they provide the intellectual property of the data and processes that will be governed by the framework.
They have context into the data, how and where it is used, the criticality of certain data to business and the importance of governing it appropriately. They will also understand the broader impact of applying certain data governance policies to the business.
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