Introduction
In a world of technological revolution, data is an important catalyst; therefore, it is important to focus on efficient ways to manage and govern data. In the light of recent events such as the collapse of international banks and the global economic crisis it is crucial for central banks of a country to issue guidelines to ensure proper management and governance of data. According to a Gartner report1 , the worldwide IT spend for organisations is expected to grow at a rate of 5.5% this year compared to the previous year’s growth rate of 0.5%. Therefore, being technologically dependent for enhanced customer service, banks must have a robust data management and governance capabilities to harness the full potential of new technologies such as generative artificial intelligence (AI) solutions, natural language generation (NLG) processes, autonomic systems, and privacyenhancing computation (PEC) mechanisms. The Reserve Bank of India (RBI) has always underlined the importance of the quality and standardisation of data across the banks, and in addition to releasing reports and guidelines on these principles, the RBI also monitors the operations of the banks internally.
Topic of the month: RBI’s guidelines on data standardisation: An overview
Recognising the need for adequate data governance (DG) and the inability of the banks to select the right data governance strategy to suit their technical landscape, the RBI along with its advisory bodies issued the Report of Committee on Data Standardisation – a set of guidelines for data governance and data quality for Indian banks.2 The guidelines outline key issues such as incomplete and inaccurate data, data duplication, limited automation and minimal controls in the international and Indian banking ecosystem, and the need for data standardisation in Indian banks to harness the potential of data by establishing robust data governance practices across banks.
Banks across the globe have started shifting their focus on establishing data management and governance capabilities in their organisations and it has become more evident in the current times when governments of various countries are mandating regulations around data management, governance, data privacy and security. The ability of organisations to govern data throughout the data’s lifecycle has become one of the key success factors. Some of the key steps which banks have taken to facilitate data governance are:
- establishing data governance teams
- identifying and rectifying data quality issues at source
- automating the report generation process
- standardising reporting formats in the local branch, region, zone and level.
However, the challenge at this stage is that while most of the banks have implemented data governance programmes which are at various degrees of implementation, there is a notable absence of data standardisation across departments and a concerning trend of low adoption.
Key obligations for banks
A robust data governance architecture can be established by following a two-phased approach across people, process, and technology drivers. A summarised overview of the activities for both the phases across the drivers has been given below:
Figure. 1 Data governance architecture framework recommended by the RBI (Source: PwC internal)
Source: PwC internal
Phase 1: Defining the strategy for data management and governance
The first phase emphasises the importance of defining data governance and management policies, processes, controls, metrics, responsible, accountable, consulted and informed (RACI) matrix, etc., across people, process, and technology drivers. Given below are some of the steps which organisations must consider for the three drivers in phase one:
- People
- Process
- Technology
People
- Define the data governance principles of the organisation highlighting the roles and responsibilities of each individual including the executive level data governance committee.
- Create KPIs and metrics to measure the performance of various data governance areas.
- Design the data ownership and stewardship model based on key parameters such as process, region and subject area.
Process
- Define enterprise-wide policies across data governance organisation structure, data quality management, metadata management, KPIs/metrics, etc.
- Design organisation-wide metadata and data quality processes which focus on maintaining the business’s technical and operational metadata, and managing data quality profiling, cleansing, monitoring, etc.
- Define key metrics to measure data quality, metadata and programme success.
Technology
- Define the rules for conducting data quality profiling and cleansing.
- Identify critical data elements from each department and select the key applications from which metadata needs to be onboarded to create a metadata repository.
- Onboard a data quality and master data management tool.
Phase 2: Operationalisation of data management and governance strategy and tools
- People
- Process
- Technology
People
- Activate the data governance office by identifying personnel for the roles defined in the target operating model.
- Establish the ownership and stewardship structure and appoint the data owners and stewards for key data elements.
- Establish the executive level committee.
- Operationalise periodic internal audit of adoption and impact of data governance policies and processes with reports published to the audit committee of board (ACB) for oversight on the implementation and impact of the policies and processes.
- Periodically monitor the KPIs and take corrective actions if required.
Process
- Implement the policies, processes and promote it throughout the organisation for increased adoption.
- Monitor the data quality and metadata metrics and highlight the key observations to the data governance committee and data owners.
Technology
- Implement data quality rules at the source by conducting data quality profiling.
- Identify data quality issues and conduct root cause analysis.
- Implement the data quality and master data management tool.
How complying with the RBI’s guidelines can benefit banks
An effectively governed and standardised data across an organisation not only eliminates duplication and redundancy of data but also contributes towards lowering the cost and effort related to issue resolution. The illustration given below provides a detailed overview of use cases to show how banks can reap benefits of the RBI’s guidelines and embark on a holistic growth curve.
Figure. 2 Potential benefits of complying with RBI guidelines
Source: PwC internal
Positive transformations which may be brought about in the banking industry due to the RBI guidelines include, but are not limited to:
- defragmented data ownership with a clear structure of governance
- strong ‘data culture’ with shift in treatment of data in day-to-day operations
- enhanced data quality with well-defined data architecture including consistent data definitions, accurate and complete data with no duplication
- low dependence on manual efforts which leads to low response time for data requests and facilitates integration with multiple systems
- effective management of high volumes of data with timely reporting.
The technological advancement in services like unified payments interface (UPI), payment services and e-wallets leads to complex banking systems and multiple data sources resulting in numerous versions of the information created across the systems. All these issues impact business aspects like capital management and capital ratios, asset quality monitoring, funds and liquidity management ultimately impacting effective risk management. Therefore, there is a strong need for streamlining processes/procedures and standardising metadata to mitigate the risks.
The way forward
Given below are a few areas where banks should focus on for incorporating effective data governance in their operations:
a. Data governance, organisation structure, roles and responsibilities: Banks should design the operating model of data governance along with the data governance structure for the people and define the ownership and stewardship roles. They should also develop the responsibility assignment RACI matrix to outline the responsibilities as well as the interaction and escalation matrix. Furthermore, banks should also define the committees for data governance, including terms of reference, frequency of meetings, and representation of members.
b. Data governance policies and processes: Robust policies, processes and frameworks in areas such as data catalogue, metadata, data quality and master data management increases efficiency and enhances data trust. Data quality frameworks ensure improved data quality and define the KPIs of the processes. Banks should discuss and promote the policies and processes with the stakeholders to ensure greater adoption and implementation of these policies.
c. Enterprise metadata management: Robust metadata management in banks may provide valuable insights and context for understanding and utilising data assets effectively. To ensure an effective metadata management process, banks must start with identifying critical data elements and defining the business glossary for these elements. This will ensure standardisation of business terms across departments. Metadata should be harvested from the source system and lineage should be established to facilitate the recording of the origin, transformation and movement of data across its lifecycle. Data dictionaries should be created to establish a link between the business and the technical metadata. To ensure that quality metadata is captured, banks should establish an effective data quality programme.
d. Effective data quality at source systems: By focusing on data quality at the sourcing stage, organisations aim to improve the overall reliability, integrity, and usefulness of their data assets. Banks must prioritise data quality from the very beginning by focusing on customer data which serves as a foundational element for numerous banking activities and processes, including customer relationship management, risk assessment, regulatory compliance, and personalised services. By collecting quality customer data, banks can improve their operational efficiency, enhance customer experience and gain a competitive edge over their competitors. Once high-quality customer data is maintained, the next step should be to maintain the quality of the data for distributors, employees, vendors transactions, etc. To ensure that quality data is collected and maintained consistently it is necessary to develop a comprehensive framework for collecting data, conducting quality checks, and analysing the issues which have been identified to determine and eliminate their root causes.
e. Maintaining single source of truth: To ensure consistent and accurate data across various systems banks must focus on master data management. Master data management establishes a single, authoritative source of critical data elements, such as customer data, accounts information and service data. Effective management of master data ensures accurate regulatory reporting facilitating regulatory compliance, better customer insights generation by integrating the customer data, efficient product service innovation leveraging reliable and consistent foundation of product data, increased operational efficiency with streamlined data management process, and reduced data duplication and data inconsistencies.
Conclusion
Effective data management has become paramount for banks in the current digital banking landscape. Implementing robust data management practices has also become crucial to ensure regulatory compliance, protect themselves against cyber threats, unlocking the full potential of their data assets, delivering innovative financial services to their customers, and leveraging data for competitive advantage. Banks need to comply with the guidelines released by the RBI and implement them for a seamless data governance process which will benefit them as well as their customers.
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3. Banks and financial institutions embrace the metaverse to enhance regulatory compliance
Banks and financial institutions have launched compliance pilot programmes in the metaverse to enhance regulatory compliance, overcome the hurdles of fragmented data systems and poor interoperability, and conduct real-time monitoring. They will run their pilot programmes for the next 18-24 months for small business units (e.g. a retail banking division) within their organisations.
4. Suo moto settlement of claims for the victims of the Odisha rail tragedy
The IRDAI has requested insurance companies to settle the claims of the Odisha tragedy victims suo moto. Suo moto refers to the proactive settlement of claims of the victims without waiting for the request from the victims or their families. The last time insurers had taken suo motu settlements was after the flash floods in Himachal Pradesh in 2022.
5. HDFC Mutual Fund introduces a defence-focused mutual fund
HDFC Asset Management Company Ltd has unveiled a mutual fund focused on the defence sector. At least 80% of the fund’s net assets will be allocated to investments in businesses in defence and related industries. Aerospace explosives, shipbuilding, defence and allied services are included under the funds which are focused on defense and allied stocks.
Acknowledgements: This newsletter has been researched and authored by Abhinaba Bhattacharjee, Arpita Shrivastava, Aniket Borse, Bhavika Tahiliani, Garima Yadav, Harshit Singh, Krunal Sampat, Nimish Nama, Prakash Suman, Raghav Sharma, Riddhi Ruparelia, Sneha Baliga and Sourav Mukherjee.
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