Data Quality Is Your Bottleneck: Fix The Foundation Before Scaling AI

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 platforms either on-premises or via SaaS. Clean, trusted data is a prerequisite for accelerating analytics and enabling more advanced digital capabilities.

👤 About the Guest

Shravan Gunda is the CEO of Kaarvi, an enterprise data platform focused on data quality, governance, transformation, and observability. He previously worked in senior technology roles supporting national oil companies and has extensive experience managing large-scale industrial datasets across upstream environments.

🌐 Website: Kaarvi.ai

💼 Shravan Gunda on LinkedIn

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The views expressed in this podcast are my own and do not constitute professional advice.