Omri Kohl, Co-Founder and CEO of Pyramid Analytics, points to that as a key evolution in data governance. Organizations are still trying to catch up to their increased reliance on AI-driven analytics. Pyramid Analytics offers a unified analytics platform that empowers teams to connect, combine, and control data from hundreds of sources, faster and easier than ever before. This evolution is important as more and more businesses are looking for agility and consistency in their data operations.
Kohl points to the need for governance policies and procedures to live where the data does. In practice, organizations have a lot they can do to greatly increase their capacity to enforce unified data practices. This is essential for ensuring trustworthiness of AI deliverables. Join us at organizations that have adopted this new paradigm, employees are empowered to unlock the full potential of their data assets.
The Three Pillars of Modern Data Governance
Kohl outlines three essential pillars for a new era of data governance: unified governance, automation, and closer relationships between governance and data. Together, these pillars create a streamlined framework that weaves governance into the day-to-day operations of data management, improving efficiency and data reliability.
Unified governance creates an opportunity for a more cohesive approach, where all teams play by the same rules. In this governance model, governance responsibilities are moved out to the edge, closer to where data is being created and used. Kohl notes that “the old model of central governance is unsustainable,” as it fails to accommodate the speed at which businesses operate today.
Automation is at the heart of this change. It allows data-driven enterprises to automatically discover, curate and organize data, validate the quality, detect anomalies and enforce policies—all without human intervention. Kohl remarks, “Instead of relying on manual effort, governance becomes part of the workflow.” This type of integration is key to keeping teams productive and doing their best work without compromising quality standards.
The Role of Data Stewards and Business Teams
The rapidly changing climate has forced data stewards to prioritize their work differently. According to Kohl, they’re not worrying as much about validating individual datasets. Rather, their task should be to enable multidisciplinary teams through clear goals and standards that allow for nuanced and thoughtful decision-making.
“Business teams now play a central role because they understand how the organization actually operates,” Kohl explains. This collaborative approach prevents governance from becoming an IT-only mandate and turns it into a joint effort among business units and departments. And just as important, it lets business teams infuse context into AI systems, which is critical to achieving the best possible outcomes.
As a consequence of COVID, Kohl notes that starting sometime in 2020, companies have seen a huge growth in governance functions across their businesses. “It’s no longer something IT can do alone,” he states. This move counters a tendency within organizations toward fragmentation, a major barrier to innovation that tends to bog down the pace of progress.
Embracing Automation and AI for Effective Governance
Now that AI is transforming our data landscape daily, organizations need to update their data governance frameworks to match. With an eye toward the future, Kohl makes the point that automation is playing a bigger role in improving governance processes. Equipped with AI, organizations are better positioned for greater agility and confidence as they deploy AI into production environments.
AI systems depend on high-quality data that is consistently defined and fully understood. Beyond that, that data has to be rich with the relevant business context, he continues. Through embedding AI into their governance strategies, organizations can help make sure they are living up to the strict expectations of advanced analytics.
Kohl believes that when data preparation, modeling, and reporting occur within a unified system, organizations can maintain consistent policies more effectively. “When your data prep lives in one system, your modeling in another, and your reporting somewhere else, it becomes impossible to maintain consistent policies,” he warns.
