Gartner, like so many other organizations, has had to postpone several of its scheduled Data and Analytics Summits to later this year due to the novel COVID-19 pandemic. Thankfully, the analyst firm was able to hold its Sydney Summit earlier this year without issue, and I was lucky enough to attend to hear Gartner Vice President Saul Judah discuss the importance of data governance. While this subject isn’t as sexy as artificial intelligence (AI), cloud, augmented analytics, or IoT, data governance remains integral to an organization’s ability to become truly become data-driven and deliver on all the promises of these hot trends.
The problem, according to Gartner predictions, is “only 20% of organizations investing in information governance will succeed in scaling governance for digital business through 2022.” Why? Judah proposed that many organizations are limiting their view of data governance to compliance initiatives, which are, for the most part, tactical and externally motivated. He advised that data governance needs to be part of a dynamic and flexible data strategy that stretches across the enterprise, is tied to outcomes, and connected to value.
Only 20% of organizations investing in information governance will succeed in scaling governance for digital business through 2022, according to Gartner.
Why Is Data Governance So Important?
A solid data management implementation is all about managing data throughout its life cycle. It is founded on technologies that enable an organization to integrate, validate, and store data. It is about technologies that ensure the integrity of data through cleansing and remediation, and it’s also about the technologies that deliver insights from analytics, AI, and machine learning. When any of these integral technologies are missing, projects fail.
If data management is all about data logistics, then data governance deals with the data strategy that feeds the logistics. Data governance is about the rules, processes, and standards that guide organizations towards trusted data. As organizations look to increase efficiency, drive transformation, and improve the customer experience, better data equals increased business value.
Of course, a data governance strategy is in some ways not about the data at all; it is about the people and processes responsible for maintaining and using the data. It is about policies, responsibilities, and roles. And it is about institutionalizing data standards that ensure the accessibility, usability, reliability, and quality required to support the organization.
But the bottom line is this: only when a solid data governance strategy has been framed can we then work backward into the implementation and the data logistics, i.e., the data usage, data access, and mastered data. Of course, the most important part, as we work backward, is having data management tools that can quickly, flexibly, and iteratively ingest the strategy.
To truly achieve digital transformation and become data-driven, we need to start with a comprehensive and well thought out data governance strategy – one that can easily roll up into our data management implementations and enable us to become data driven.