Data governance creates trust and a culture based on data. It focuses on providing value to data consumers rather than controlling the flow of knowledge. Based on that, it supports the highest deployment parameters to achieve maximum performance. Progressive implementation allows users to develop data governance programs at their own pace based on available technologies.
Let's look at each section of the progressive framework. The tragedy is that data governance has the power to be much more: a way to maximize the value of data assets. And the DAMA Data Management Dictionary describes data governance as “the exercise of authority, control, and shared decision-making (planning, oversight, and compliance) over the management of data assets. However, the team members creating the AI model are data scientists with little or no business knowledge.
When data is of high quality, standardized, and certified, it can be used to make better business decisions. Like a project plan for successful data governance, a roadmap provides tangible objectives for an achievable data governance strategy. Setting up a data governance program may seem like a daunting task, but we've compiled some tips to help you get yours up and running. The goal of active data governance is to formalize responsibility for data, so that people are actively involved in ensuring that the definition, production, and use of data conform to the rules and provide a path to appropriate decision-making that directly benefits the organization, rather than forcing data users to assume rigid roles.
The non-invasive approach adds value by introducing a formal layer of data governance, also known as active data governance. Data governance involves establishing internal standards and data policies that apply to how data is collected, stored, processed, and deleted. The Committee meets to discuss pain points and requirements, and recommends data governance policies for implementation. The problem with this is that across the organization, different teams will have different relationships with data.
New data methodologies, such as data operations, the data mesh, and the data structure, are revolutionizing the data industry. Financial institutions are heavily regulated and must comply with a litany of rules, including how data is managed. Ultimately, organizations that collect personally identifiable information or other sensitive data must comply with various industry regulations and have policies that define how the data is collected, who can access it, how they can access it, and much more. Centralized data governance lacks team input and, therefore, data is unlikely to see mass adoption.
Financial institutions that implement strong data governance programs will achieve results that go beyond simple regulatory compliance: they lay the foundation for business growth and expansion.
