How to create a (necessary) framework for data governance
If data is the ‘new oil’, then data governance is the refinement process transforming data from crude oil to petrol you can actually put in your engine.
John C. Tanner15 Nov 2019
How to create a (necessary) framework for data governance
If data is the ‘new oil’, then data governance is the refinement process necessary, to transform data from crude oil to petrol you can actually put in your engine. And doing that requires a framework – ideally, one designed around the principles underlying the EU’s GDPR law.
That was the key message from Sunny Nirala, Data Quality Management, Celcom, who talked about the importance of data governance in the digital era and offered some pro tips on how to go about managing data governance effectively at Digital Transformation Asia in Kuala Lumpur last week.
Strictly speaking, Nirala explained, data governance is nothing new – it’s been around for as long as there has been data to collect.
The key word is “organized” – data governance in any era requires keeping track of what data you have, how it's organized, how accurate it is, and what it represents. The main difference with digital data – apart from the technological aspect – is that there is so much more of it.
That’s why data governance in the digital age requires an overarching framework that focuses on the strategic framework and policies required to implement governance procedures and policies across the organization, Nirala said.
That overarching framework will guide other specific areas of data governance, such as:
The framework also has to take into account the different classifications of data you may have (public, private and restricted data), and the various roles in the data governance chain (owners, stewards, custodians and citizens).
A data governance framework also has to be designed to help organizations comply with data protection and privacy laws such as the EU’s General Digital Protection Regulation (GDPR) that went into effect in May 2018.
In fact, said Nirala, because GDPR is the strictest data privacy law to comply with at the moment, it’s a good idea to design your data governance strategy based on the seven key principles underlying GDPR:
Another key motivator for GDPR-level data governance is growing consumer awareness of the practice of data collection and its implications. And those consumers are also your customers.
“You need to place yourselves into the shoes of your customers, and think about how would you like your information to be freely available to anyone for them to do whatever they wish with it,” he said.
That was the key message from Sunny Nirala, Data Quality Management, Celcom, who talked about the importance of data governance in the digital era and offered some pro tips on how to go about managing data governance effectively at Digital Transformation Asia in Kuala Lumpur last week.
Strictly speaking, Nirala explained, data governance is nothing new – it’s been around for as long as there has been data to collect.
“The purpose of data governance has always been to protect it and to serve it to others in an organized way. That’s been true even back in the days when data was collected on paper,” he said.
The key word is “organized” – data governance in any era requires keeping track of what data you have, how it's organized, how accurate it is, and what it represents. The main difference with digital data – apart from the technological aspect – is that there is so much more of it.
That’s why data governance in the digital age requires an overarching framework that focuses on the strategic framework and policies required to implement governance procedures and policies across the organization, Nirala said.
That overarching framework will guide other specific areas of data governance, such as:
- Master data management (ensuring a single source of truth)
- Metadata management (capturing data about the data, as it were)
- Data lineage (tracking where the data originated)
- Data quality (which requires its own framework)
- Data forensics (the ability to deep-dive in to root-cause analysis)
- Data certification (for quality control of data)
- Data discovery (crunching different datasets to discover new relationships between them).
The framework also has to take into account the different classifications of data you may have (public, private and restricted data), and the various roles in the data governance chain (owners, stewards, custodians and citizens).
A data governance framework also has to be designed to help organizations comply with data protection and privacy laws such as the EU’s General Digital Protection Regulation (GDPR) that went into effect in May 2018.
In fact, said Nirala, because GDPR is the strictest data privacy law to comply with at the moment, it’s a good idea to design your data governance strategy based on the seven key principles underlying GDPR:
- Lawfulness, fairness and transparency
- Purpose limitation
- Data minimization
- Accuracy
- Storage limitation
- Integrity and confidentiality
- Accountability
“That should be the basis for your approach,” he said, not least because data protection and privacy laws around the world are popping up or being revised in the wake of GDPR.
“GDPR is setting the global benchmark for everyone else to follow,” Nirala said.
Another key motivator for GDPR-level data governance is growing consumer awareness of the practice of data collection and its implications. And those consumers are also your customers.
“You need to place yourselves into the shoes of your customers, and think about how would you like your information to be freely available to anyone for them to do whatever they wish with it,” he said.
