Measuring Success in DataOps, Data Governance, and Data Security: What Works
In 2006, Clive Humby, a British mathematician, famously declared that “data is the new oil.” But just like oil, data in its raw form is not useful until it’s refined, processed, and strategically distributed. Fast forward nearly two decades, and this analogy has shaped the core practices around DataOps, data governance, and data security, three essential pillars for managing data in today’s business world. These functions not only help integrate and manage data, but they also ensure its compliance, accuracy, usability, and protection against threats.
As AI-driven digital transformation becomes the norm, especially with the rise of generative AI products, business leaders increasingly recognize the need for robust data practices. AI governance has become a vital safeguard, but it’s equally important to measure the effectiveness of data operations, governance, and security. Despite the heavy investments in these areas, many organizations struggle to define clear metrics to gauge whether these initiatives are truly adding value and reducing risks.
When asked about how to measure the success of these practices, several industry leaders weighed in. There’s a growing concern that businesses are investing heavily in AI without a clear understanding of whether it’s delivering measurable business outcomes. So, what metrics should organizations use to assess the value of their data operations, governance, and security strategies?
One of the key insights shared by experts is the need to align metrics with tangible business outcomes. “To truly demonstrate business value, CIOs must focus on KPIs that tie directly to organizational goals, rather than relying on traditional IT metrics,” says Yakir Golan, CEO of Kovrr. For instance, instead of reporting on IT ticket resolution rates, businesses should highlight cost savings from automation or a reduction in forecasted risk exposure. For example, illustrating how a $2 million reduction in risk can be achieved through better data management is far more compelling to executives than technical efficiency alone.
Data effectiveness metrics also play a crucial role. Srujan Akula, CEO of The Modern Data Company, suggests calculating data ROI, which is akin to marketing attribution. By evaluating the costs of data processing and storage against the business value they generate, companies can measure the time-to-insight and better understand how efficiently data is being leveraged to drive business decisions. These kinds of metrics allow business leaders to see the direct link between their data investments and the overall impact on the company’s bottom line.