Data Governance practices need to mature. Data Governance Trends in 2019 reports that dissatisfaction with the quality of business data continues in 2019, despite a growing understanding of Data Governance’s value.
Harvard Business Review reports 92 percent of executives say their Big Data and AI investments are accelerating, and 88 percent talk about a greater urgency to invest in Big Data and AI. In order for AI and machine learning to be successful, Data Governance must also be a success. Data Governance remains elusive to the 87 percent of businesses which, according to Gartner, have lower levels of Business Intelligence.
Recent news has also suggested a need to improve Data Governance processes. Data breaches continue to affect customers and the impacts are quite broad, as an organization’s customers (including banks, universities, and pharmaceutical companies) must continually take stock and change their user names and passwords. Effective Data Governance is a fundamental component of data security processes.
Data Governance has to drive improvements in business outcomes. “Implementing Data Governance poorly, with little connection or impact on business operations will just waste resources,” says Anthony Algmin, Principal at Algmin Data Leadership.
To mature, Data Governance needs to be business-led and a continuous process, as Donna Burbank and Nigel Turner emphasize. They recommend, as a first step, creating a Data Strategy, bringing together organization and people, processes and workflows, Data Management and measures, and culture and communication. Then creating and choosing a Data Governance Framework. Most importantly, periodically testing that Data Governance Framework.
To truly be confident in Data Governance structures, organizations need to do the critical testing before a breach or some other unexpected event occurs. It is this notion—implementing some testing—that is missing in much current Data Governance literature. Thinking like a software tester provides an alternative way of learning good Data Governance fundamentals.
Before Testing, Define Data Governance Requirements
Prior to offering feedback on any software developed, a great tester will ask for the product’s requirements to know what is expected and to clarify important ambiguities. Likewise, how does an organization know it has good Data Governance without understanding the agreed-upon specifications and its ultimate end? First, it helps to define the what Data Governance is supposed to do. DATAVERSITY® defines Data Governance as:
“A collection of practices and processes which help to ensure the formal management of data assets within an organization. Data Governance often includes other concepts such as Data Stewardship, Data Quality, and others to help an enterprise gain better control over its data assets.”
How Data Governance is implemented depends on business demands specifically leading to a Data Governance solution in the first place. This means breaking down the data vision and strategy into sub-goals and their components, such as a series of use cases. Nigel Turner and Donna Burbank give the following use case examples: