Good data governance is supposed to help financial services companies control their data, improve compliance, and adapt more quickly to the near-constant changes of the financial services regulatory landscape. However, this can be a time-consuming and expensive process, especially when organizations are using legacy systems. In fact, it is estimated that businesses spend with data issues estimated to cost businesses 10-30% of their revenue handling issues relating to data quality.
Data governance that’s outdated also increases exposure to regulatory, security, and operational risks. As technology continues to progress and more institutions adopt AI and ML, poor data governance can be construed as a setback to the financial sector, one that is counted amongst the most digitally mature. From an efficiency standpoint, there is no denying the potential of AI, however, complications creep in as AI is hyper-dependent on data. Poor data governance can lead to security risks, non-compliance, increased costs, and lower productivity.
Having a continuous process in place allows for peace of mind, knowing that data is protected, usable, and accessible. With data that is consistent, trustworthy, and relevant, institutions can address issues pertaining to risk and fraud management, stay compliant, remain competitive in the ever-changing financial environment, and make better business decisions.
Financial institutions can implement very practical steps to improve their advancements toward data governance.
1. Accountability is Crucial for Good Data
Data governance should be a priority across the entire organization. Data literacy is important for effective decision-making across the organization. Institutions need to recognize that data governance is not just an IT issue, it needs to be a priority that runs throughout the organization. AI and compliance reach deeply into an organization and for that reason, all departments—not just the IT and the data team—need data literacy to be able to make informed business decisions. According to Gartner, data literacy will become an explicit and necessary driver of business value by 2023, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.
This starts with senior management buy-in. When senior leaders are onboard, employees are more apt to want to buy-in as well. By communicating the journey, sharing the importance of data governance, and helping employees to understand their responsibilities within the framework, the road to implementing a good data governance strategy by management can be an easier process.
2. Say Goodbye to Bad Data
The adoption of innovative technologies allows organizations to be more agile, increasingly adaptable to changing customer behaviors, and less susceptible to compliance risk. The path of digital transformation starts with accurate data. Improving data quality, access, and control allow for more strategic decision making in relation to business strategies. Emerging technologies such as AI and ML are data-driven, therefore, a high reliance and increasing importance is placed on good data.
3. Being a Good Steward
Data stewardship collectively ensures that data assets are accessible, usable, trusted, and safe. Data stewards must adhere to processes to uphold the integrity of the data. For these processes to be created, it’s crucial to:
- Know where data exists and is stored
- Identify access points to data and why it’s used
- Understand the parts of the business impacted by data
- Define organizational standards for data quality
Maintaining data efficiently enables institutions to consistently meet regulatory requirements.
4. Leveraging AI
Data-centric AI is continuing to revolutionize the financial sector. The next step is to go beyond discrete automation tasks to automating entire workflows and processes. Gartner recently identified hyperautomation among its top trends for 2022, predicting it could lower operational costs by 30%.
5. Resolving Issues
By detecting issues quickly, institutions can stay on track and thwart any compliance risks. Although AI and ML mitigate human error when it comes to compliance, the human factor still needs to be involved in the equation. Employees can aid by questioning and flagging dubious data. Employee involvement tends to lead to greater buy-in for the use of technology and further demonstrates the importance of data governance.
A Good Data Governance Strategy Increases Confidence
Financial institutions have undergone unprecedented digitization in the past few years. Harnessing huge volumes of data in a way that’s usable, accessible, and compliant in a relatively short amount of time has many people struggling. But implementing immediate, practical steps is the only way to transform data, adhere to data governance, and stay compliant.
Buy-in early on from senior leaders regarding the importance of data governance helps to pave the path for employee buy-in throughout the organization. Understanding data and its relevance is crucial for success. Further, data that is consistent, trustworthy, and relevant allows financial institutions to feel more confident and enhance their decision-making process. The key to staying compliant in the financial industry is to have great data governance, and it starts with taking the first step.
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