Data Quality Is Your Bottleneck: Fix The Foundation Before Scaling AI
About this episode
The episode discusses the importance of data quality in oil and gas companies and how to improve it for better analytics and decision-making.
Oil and gas companies generate enormous volumes of operational, geological, and production data . Despite this abundance, much of that data remains fragmented , inconsistent , and difficult to trust . Teams often spend a significant portion of their time preparing datasets rather than analyzing them. The result is delayed decision-making, inflated costs, and reduced operational agility. The core complication lies in data quality , data governance , and data readiness . Duplicate records, null values, drift, and structural inconsistencies make it difficult to move quickly from raw data to actionable insight. Asset teams frequently work semi- independently , each rebuilding transformation processes from scratch. Without reliable data foundations, scaling analytics, automation, or advanced modelling becomes difficult and costly. In this episode, I'm in conversation with Shravan Gunda , CEO of Kaarvi , to discuss how a structured approach to data ingestion , anomaly detection , ETL transformation , and data lineage can reduce time-to-insight from weeks to hours. He outlines how upstream teams can standardize workflows , support governance requirements such as SOC 2 , and deploy…
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