Recognized as “Data Governance Visionary” by Anomalo
Earlier this month, Anomalo kicked off their “Data Governance Visionaries” campaign, where they shined a spotlight on data governance leaders and their best practices. Their first Visionary was me. Over the last few years, I have been instrumental in architecting and managing petabyte-scale data platforms for enterprises using Google Cloud, helping to drive effective use of data assets through good data quality.
I highlighted three key topics important for data governance leaders to observe when building a world-class data governance approach:
1. Good Data Quality is Essential to Strong Data Governance
“First, Tyler highlights the pain that can come from poor enterprise data governance. He points out that regardless of the advanced tools or AI models employed—be it Vertex AI, Anthropic Stack, or others—inputting poor-quality data inevitably results in suboptimal model performance. Effective governance ensures efficiency, reliability, and accuracy of data assets, maximizing the return on investment for AI applications. For data governance leaders, good data quality is essential to strong data governance.”
2. The Role of Data Quality in Helping Enterprises Win the AI Race
“Marathon runners out there know you need to equip the best gear to have the best chance at winning the race. In today’s competitive landscape, where AI adoption is accelerating, having high-quality data has become table stakes. Without it, organizations lose trust in the outputs of their data models, destroying confidence and data trust in the company’s most valuable asset. As Tyler colorfully points out, running the AI race without data quality is putting enterprises at a substantial disadvantage to their competitors.”
3. Automating Data Quality as a Means for Reducing Toil AND Increasing Time to Value
“Finally, Tyler discusses the dual perspectives of data quality through the lens of automation. Today, it’s well understood that data quality has value as a cost-saving tactic – reducing engineering hours spent, errors in data, and optimizing workflows. However, modern data governance leaders are also recognizing the opportunity created by automating data quality, including enhancing the speed of AI adoption, improving customer-facing product recommendations, and increasing the pace at which engineering teams can deploy data products. Tyler has seen modern data governance leaders benefit from both sides of the data quality coin – both saving time and money, as well as creating opportunities to accelerate their business.”
