By David Irecki
Robust Data Governance
Imagine if you will, a ‘junk’ closet stashed to the brim with all manner of ‘must-haves’ that you supposedly couldn’t do without.
The purported essential nature of these items is a testament to the difficulty many of us face in trying to declutter.
We agonise over trying to organise and decide what can be kept, thrown out, or even donated.
In many ways, this is analogous to data governance. Banking executives often lament that they’ve got data all over the place, but don’t know where it is.
This is followed by complaints about how safeguarding data privacy feels like a losing battle, as the data they possess can’t be trusted. This really is a microcosm of why sound data governance matters.
Typically, data governance covers unearthing data, classifying it, setting appropriate guidelines as well as standards, and enforcing those standards with rules. Ultimately, this is to manage and govern data risk but, for most organisations, this feels like an endless task.
All too often, individual lines of business will adopt their own data processes and standards over those created through a broader governance initiative.
This invariably results in hampered data quality. According to a report by Bank Negara Malaysia, a financial institution should assess and enhance the resilience of its cloud services by adopting measures such as redundancy, geographical diversity, hybrid cloud setups, backup providers, and a multi-cloud strategy to ensure continuous and reliable service availability in extreme scenarios.
The Malaysian government has tabled the Omnibus Act in June 2023, allowing data sharing among all government agencies via the Malaysian Main Database (PADU).
Economic Affairs Minister emphasised that the Act is essential, ensuring both the safety and legality of data sharing through PADU, a streamlined and integrated data gathering system compared to existing ones, thereby serving as a continuous database mandated by law.
Data governance for changing times
To understand why data governance is considered critical for banks, we need to get acquainted with the underlying challenges facing financial services organisations as they modernise.
Rolling out new cloud applications or Internet of Things (IoT) devices into an environment where legacy on-premise systems are already in place means more data silos and data sets to manage.
Invariably, this results in data volumes, variety, and velocity increasing much too quickly for banks.
This gives rise to IT complexity – driven by technical debt or the reliance on systems that are cobbled together and one-off connections. Not only that, it also raises the spectre of ‘shadow IT’, as employees look for workarounds to friction in executing tasks.
This can create difficulties for banks trying to identify and manage their data assets in a consistent, enterprise-wide way that is aligned with business strategy.
Ultimately, barely controlled data leads to errant financial reporting, data privacy breaches, and non-compliance with consumer data regulations. Failing to counter these risks can lead to fines, hurt brand image, and trigger lost sales.
Arguably more importantly, the financial sector is entrusted with the personally identifiable information (PII) of consumers, suppliers, and employees – failing to live up to this duty would erode confidence, which is a cornerstone of finance.
Indeed, there is an interesting trend emerging vis-a-vis trust in the financial sector.
The growing number of hyperscalers offers a landing zone for businesses looking to reach Malaysia’s almost 27 million online customers, says Forrester’s The State of Cloud in Malaysia in 2023 research.
Despite earlier cloud experiments by Malaysian businesses, interest in migration is growing due to the availability of domestic public cloud capacity.
Boosting outcomes with a holistic approach
Overcoming these risks rests on having the right approach towards privacy protection, while delivering consistent, timely, and accurate information that can be accessed at a moment’s notice by business analysts.
A holistic data governance framework should directly address the following:
- Regulatory compliance and risk management – Because banks are exposed to significant risks, the financial services sector is tightly regulated. By improving the quality and reliability of data, a comprehensive data governance framework aids adherence to regulatory mandates and minimises compliance risks.
- Operational efficiency – The rise of disruptors in financial services in recent years can be attributed to the ability to harness structured and unstructured data from a wide range of internal and external sources. Upstarts such as fintechs have upended the traditional landscape, and can now deliver diverse financial services faster than their legacy counterparts. To keep pace with these new entrants, what is needed is a clear strategy to boost operational efficiency by eliminating data redundancies, reducing errors, ensuring data consistency, and orchestrating efficient data processing – in other words the hallmarks of a data governance framework.
- Customer experience and satisfaction – By ensuring high-quality, actionable data, a robust data governance policy allows banks to provide up-to-date, personalised services that enhance the customer experience. This, in turn, improves customer satisfaction and loyalty. Data governance also helps banks manage customer consent and preferences in compliance with regulations, such as the European Union General Data Protection Regulation (GDPR) by offering mechanisms to capture, store, and update customer consent preferences.
- Innovation and competitive advantage – When data can be reliably counted upon to be of high quality, there exists a solid foundation to identify new opportunities, analyse trends, develop innovative solutions, and gain a competitive edge. Armed with the ability to undertake advanced analytics, banks can drive innovation, as well as identify upsell and cross-sell opportunities.
Implementing a data governance strategy requires a systematic approach. Financial services organisations can enhance data governance strategies via AI-powered automation and a low-code, cloud-native platform that enables full configuration so that data can be delivered securely when requested.
These capabilities also empower financial services organisations to mask sensitive data via roles-based permissions, preventing them from being viewed by unauthorised users.
Through clear goals, a solid framework, and the power of automation driven by a low code, cloud-native and unified platform, banks can position themselves to capitalise on the opportunities that emerge on the back of the digital economy.
By being on top of data as it proliferates, banks can compete at ever-faster speeds, unlocking the flexibility and innovation to thrive.
This article is written by David Irecki, Director of Solution Consulting, APJ, Boomi. Boomi is an integrated platform software provider.
