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The key ingredients to creating a data governance model for Philippine banks

Adrian M. Reodique | Dec. 14, 2016
ASG’s Praveen Kumar shares why financial institutions need to create a business glossary, build data lineage, and assign a data owner.

Data plays an important role in enabling the financial and banking industry to comply with regulations. Hence, it is imperative to have a proper data governance to ensure the accuracy and quality of data.

However, most banks in the Philippines are still in the initial stages of having a data governance model, said Praveen Kumar, General Manager of ASG Technologies in Asia Pacific. "[Banks are still] in the preliminary stage because most of them have just deployed a core banking solution or are in the process of deploying one. Data is very decentralised and uncontrolled [while] the regulators are just tightening the laws slowly but steadily. That's why data governance has not been a very big topic so far," Kumar explained in an interview with BankIT Asia.

He noted that developing a data governance model involves three processes: creating a business glossary, building data lineage as part of metadata, and assigning a data owner.

Creating a business glossary

Since the definition of some words varies from one department to another, Kumar underscored the importance of specifically defining each data element.  For example, the loan department in a bank may define 'risk' as the capability of a borrower to pay his/her loan. However, 'risk' in the bank's investment department may refer to the extent in which the person is willing to invest, Kumar said.

As such, Kumar said it is important to have a business glossary. "In the bank, you need [something like] a Wikipedia because there are so many terms and you have to remember those terms in the business glossary. Once you have that, you will then have the capability to understand which are your key elements and the definitions of each of those elements," he added.

After defining each data element, banks must then verify the source of the information. Kumar mentioned the KYC process (Know Your Customer), as an example, which involves the collection and verification of personal information of the customers. "[KYC allows] regulators to know whether every [customer] who's opening or transacting in that country is a genuine person or not. Generally, regulators [require banks to] have as much details [of the customer] as possible to prevent fraud and prevent money laundering activities that could happen," he explained.

Kumar added that data sometimes evolve as it travels along different applications in the organisation. This where data lineage comes in.

Data lineage is defined as the lifecycle of data that includes its origin and where it moves over time. According to ASG, data lifecycle illustrates the various forks along the way where data may be transformed or where 'bad data' may be propagated. These forks represent likely decisions or governance points where data can be checked, validated, authorised, approved, and where there may be a transition in data ownership and responsibility. "When data is stored in many different applications, that data is either photocopied or scanned from somewhere, or is derived from another source. But do you know if that data has been transformed, how it was transformed, if the transformation done in purpose [or] if there a process around it?" Kumar questioned.

 

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