- Data has become the lifeblood of the banking industry. With the exponential growth of data, ensuring its proper management, security, and utilisation is crucial for banks. This is where data governance comes into play.
- Data governance for the banking industry refers to the framework and processes that govern the management, privacy, and integrity of data within the banking sector.
- Data governance is a continuous process, especially in organisations like financial institutions, where datasets are complex and highly regulated.
Data governance is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal standards and policies that also control data usage. Effective data governance makes sure that data is reliable, consistent, and not abused. It’s becoming more and more important as businesses deal with tightening data privacy laws and depend more and more on data analytics to streamline operations and inform strategic business decisions.
Data governance in banking is the management, control, and access of financial data. It involves establishing processes, policies, and controls to ensure the accuracy, integrity, and security of banking data throughout its lifecycle. In this article, we will explore how data governance provides value in banking and implementing a data governance strategy in banking.
4 ways data governance provides value in banking
1. Regulatory compliance
It is a requirement for banks to keep all the data they have secure, based on a variety of federal and state compliance regulations. Regulatory requirements continue to pressure the banking industry to get data governance under control as the consequences of data violations become more costly.
With the right data governance plan in place, banks always know exactly what data they have access to. They also always know where data is located, which ensures they can enforce the right controls — even during complicated projects like cloud migrations.
2. Cost cutting
Manual data management is tedious, inefficient and expensive. The responsibility of manual data management is often placed at the doorstep of IT teams, which means financial institutions frequently foot the costs of maintaining active IT teams.
3. Market insights
The financial sector is now characterised by relentless competition between institutions and saturation for newcomers. As a result, market insights have become a necessity for a competitive advantage. Through data analysis initiatives, banks can confidently approach their data and derive actionable insights.
4. Data-driven culture
Data-driven models are increasingly transforming how organisations handle business goals and objectives. A data-driven culture is proving to be of great benefit to organisations, as it intuitively improves approaches to cost-cutting, innovation and customer insights. Data governance supports and encourages a data-driven culture so banks can more effectively run their operations and make customer-experience-focused decisions.
Also read: What is internet governance?
Implementing a data governance strategy in banking: A step-by-step guide
Implementing a data governance strategy requires a systematic approach. Here’s a step-by-step guide outlining the key actions to take when implementing a data governance strategy in a banking organisation:
Establish clear goals and objectives: This might include ensuring regulatory compliance, enhancing customer service, or driving operational efficiency. For example, a mid-sized bank might set an objective to reduce data processing errors by 20% in the next financial quarter as a means to increase operational efficiency.
Conduct a data inventory assessment: Before establishing a data governance strategy, it’s crucial to know what data you have, where it’s stored, who uses it, and how it’s used. For example, in a regional bank, the data team might carry out an inventory of all databases and data sets used in its operations, noting down specifics of data usage, storage, and management.
Identify data domains, domain owners, and consumers: A crucial step in data governance involves categorising your data into domains (e.g., customer data, transaction data), appointing domain owners, and identifying consumers for each domain. For example, in a bank, the domain owner for customer data could be the Head of Customer Services, and the consumers might be the marketing, sales, and customer service teams.
Define data governance roles and responsibilities: This includes identifying data owners, stewards, custodians, and users. For example, the Chief Data Officer (CDO) might be responsible for overseeing the overall data governance initiative, while data stewards ensure data quality, accuracy, and compliance within their respective domains.
Also read: What is open banking? A short guide
Develop a data governance framework: This involves creating a structure that dictates how data will be handled, who is responsible for different data domains, and the processes involved in maintaining and protecting data. Read more → How to simplify data governance
Implement data governance tools and technologies: Adopting the right tools and technologies, such as data catalogues, data lineage tools, data quality tools, and data protection tools, is vital for the success of your data governance strategy. These tools aid in data discovery, data quality management, data protection, and overall governance.
Define metrics to measure data governance framework adoption and effectiveness: Defining clear metrics will help you measure the effectiveness of your data governance strategy and framework. These metrics could include data quality scores, data usage, compliance metrics, and business outcomes tied to data initiatives.
Develop a training and continuous education program: Data governance is not a one-off project, but a long-term program that requires continuous learning and adaptation for its success.
Monitor and measure progress: Regularly monitor your data governance metrics to track progress and make necessary adjustments. This could involve regular audits of data quality, compliance checks, and monitoring of data usage and impact. For example, the bank’s data governance team could conduct periodic assessments to identify areas for improvement, such as enhancing data quality controls or updating data security measures, and implement necessary changes to drive continuous improvement.
Foster a data-driven culture: This involves ensuring that employees across the organisation understand the value of data, have access to data-driven insights, and are encouraged to make decisions based on data. It requires effective communication, training, and support from top leadership to embed a data-driven mindset within the organisation.
Data governance in banking plays a pivotal role in everything from regulatory compliance to enhanced value from data. The implementation of data governance requires clear goals, a solid framework, a data-driven culture, and continuous learning and adaptability.
