“Bottoms-up” data governance is the fast-lane to ROI

In this time of soaring data volumes and equally growing awareness of information’s value, the expectation of dramatic ROI on data is well-founded. That return, however, is too often elusive.?

It doesn’t have to be. In fact, it’s a movie we’ve seen before, and it’s still a great script we can follow from an earlier inflection point of technology -- the so-called “agile software movement” of the early 2000s that has inspired us in our embrace of “Agile Data Governance ”.

Traditional Data Governance Barriers?

I get it. It’s no wonder that developing a strategy to reap the rewards of data governance is a top-down proposition. The volume of raw data, from customer interactions, sales, production, supply chain management and dozens of other sources, is soaring. So are compliance regulations on data privacy and security, now enacted in 20 states and certainly coming at the federal level.?

Meanwhile, artificial intelligence, or AI, is now table stakes technology at even the smallest of companies. Of course the captains and commanders in the corner offices are barking orders for data policies, procedures, and approvals.

My impressions from many conversations with executives are well supported by the evidence, including a survey by IBM released earlier this year that found that 40% of companies keen on mastering their data assets and deploying AI to optimize the resource are not doing so amid perceived barriers of data complexity, lack of deep AI skill sets, and even ethical concerns.

There’s also a palpable fear among many executives that the “Big Tech” titans of Google, Microsoft, Amazon, Facebook, and Apple – which control more than 90% of data making it inaccessible to all but their own AI models – will rule the AI future. This is adding to the reactive mood in boardrooms. Yet, traditional data governance approaches often falter with a rigid, top-down structure. Centralized control, extensive documentation, and strict approval processes will ultimately stifle innovation. Information is often trapped within specific departments or systems.

The sheer scale and speed of data generation today requires a more agile approach. Overwhelmed leaders want to play it safe through traditional governance frameworks, but in the end their fear will lead to missed opportunities for value creation.

A Parallel Shift: Agile Software Development?

These concerns echo from my earlier days in technology in the late 1990s. At that point, many startups stumbled amid the so-called “waterfall methodology” for software development and deployment. This was a legacy of what I’ve called the “first surge” in data , an era dating to the 1970s when software was largely the domain of big banks, aerospace companies, and major manufacturers.?

They took a structured, linear approach aligned with the way they’d always done things: distinct phases of “reqs” gathering, design, development, and testing. No project began until the previous one was complete.?

This was overturned in no small measure by the movement following the principles of “Manifesto for Agile Software Development” published in 2001. It emphasized flexibility, collaboration, continuous delivery, and responsiveness to change. Widely and globally embraced, it led to the now common “sprints” by IT teams, reoriented product strategies, boosted customer satisfaction, and much more.?

The New Data Governance Paradigm

More than a generation later, a similar mind-set has taken hold in thinking about data governance, and extracting value and maximum ROI from these formidable resources. As in the past, this approach is understandable as data governance has its broad roots in the now-outdated monolithic IT systems, centralized databases, and the early use of large datasets in the heavily regulated health care sector. Another institutionalized set of “waterfall” cultures.

Among those challenging the “waterfall” approach in data governance is my co-founder Jon Loyens, our Chief Data and Strategy Officer, who envisioned and coined the new movement for Agile Data Governance. “If you try to build a data-driven culture with a top-down approach where every detail is planned far in advance, you will fail,” Jon wrote in his own manifesto on this topic back in 2020.

Data governance ROI action items for leaders?

Our vision for data.world was always about more than just creating a platform; it was about revolutionizing how the world interacts with data. As we embarked on this journey, we quickly realized that the traditional approaches to data management and governance were often at odds with our mission of fostering collaboration and democratizing data access. Through our evolution from a collaborative data platform to pioneers in Agile Data Governance, we've identified key strategies that deliver rapid ROI. Leaders looking to maximize returns on their data governance initiatives should consider the following action items:

1. Implement an iterative approach: Agile data governance emphasizes small, continuous improvements rather than large, monolithic projects. As Jon illustrated and as our clients have quickly discovered, by breaking down governance tasks into smaller, manageable increments, teams can quickly implement changes, deliver value early, and adjust based on feedback.

Action: Break down governance tasks into manageable increments, allowing teams to implement changes quickly, deliver early value, and adjust based on feedback.

2. Focus on quick wins – Agile frameworks prioritize "quick wins" that directly address pressing data management issues (like improving data quality or reducing risk) without waiting for a complete governance overhaul. Compliance issues can be resolved in days and productivity boosted immediately.

Action: Identify and tackle immediate challenges like data quality improvements or risk reduction, which can resolve compliance issues and boost productivity within days.

3. Encourage cross-functional collaboration – Agile data governance involves collaborative, cross-functional teams that include stakeholders from IT, business, and governance. This close collaboration ensures that problems are identified, prioritized, and addressed quickly, reducing the time between recognizing an issue and solving it.

Action: Establish regular cross-functional meetings to quickly identify, prioritize, and address data-related problems.

4. Leverage automation and modern tools – Agile Data Governance leverages automation and modern data tools, including our foundational technologies at data.world , the knowledge graph atop the data catalog. This is essential for AI-driven monitoring of data lineage and other governance challenges that translate to faster ROI. Action: Invest in tools like data catalogs with knowledge graph capabilities for AI-driven monitoring of data lineage and other governance challenges.

5. Ensure business alignment – Agile approaches are inherently business-driven, meaning data governance initiatives are closely aligned with the organization’s immediate business goals. Agile teams ensure that the solutions they implement deliver immediate value, which quickly translates into measurable ROI. Action: Regularly review and adjust governance priorities based on current business objectives to make sure that implemented solutions deliver immediate, measurable value.

The Agile Data Governance framework allows for rapid, iterative improvements, targeted solutions, and a faster turnaround time, driving immediate value that leads to ROI in days rather than months.

Demo our data governance platform - we would love to help you adapt.