Digital Oil And Gas

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