Harnessing Data & Technology: The Missing Link to Reboot Nigeria’s Oil & Gas Performance

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Nigeria’s oil and gas sector remains central to its economy—accounting for about 85% of export earnings and roughly 30% of government revenue—but growth has lagged due to systemic inefficiencies. According to a recent analysis, one of the major gaps is the lack of robust data infrastructure: while Nigeria has introduced reforms like the Petroleum Industry Act (PIA) 2021 and welcomed the commissioning of the Dangote Refinery, these advances will only pay off if matched by the adoption of modern IT-(Information Technology) and OT-(Operational Technology) systems. Such convergence allows companies to gather operational data in real time (through sensors, monitoring of equipment), enabling predictive maintenance, risk management, and overall productivity enhancement.

The article argues that Nigeria must invest in digital infrastructure—data centres, analytics platforms, AI/ML tools—to unlock informed decision making. By tracking and mining facts and performance metrics (such as downtime, production yield, equipment conditions), firms can anticipate failures, reduce waste, and attract more investment. This is especially important given the country’s vast natural gas reserves and the global push for cleaner energy. With stronger data systems, Nigeria can better align with both domestic goals and international expectations for transparency, environmental responsibility, and operational excellence.

Why It Matters

  • – Efficiency & Competitiveness: Without reliable data, oil & gas operations suffer delays, waste, and higher costs. Digitisation helps optimize processes.
  • – Policy & Investment Attraction: Investors and regulators increasingly demand transparency and measurable metrics; poor data can deter investment or lead to unfavorable terms.
  • – Environmental & Energy Transition Imperatives: As the world seeks cleaner energy, the ability to monitor emissions, manage gas infrastructure, and reduce environmental impact hinges on data quality.

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