April 17, 2026

Data analytics for oil and gas: Turning raw data into actionable insights

Before big data was a term, the oil and gas industry was collecting it. It has long worked with large quantities of data to make technical decisions, Bain & Co. notes, investing in seismic software, visualization tools and other digital technologies to figure out what lies below the surface and how to get it out. And today the sector continues to try to push the limits of big data analytics as it pulls information from robots, drones, AI-generated simulations, a growing number of sensors and SCADA systems, external sources like market prices and weather conditions, and more.

Buried within the industry’s big data are the insights that can be surfaced by basic descriptive and diagnostic analytics, such as what were a company’s production volumes last quarter. With the addition of technologies such as artificial intelligence (AI), it is possible to also learn from that data for enabling cutting-edge capabilities like automatically self-updating demand forecasts as new data comes in from supply-side, demand-side, commercial and other domains. Capturing insights requires managing the 3 Vs of big data: volume, velocity and variety.

Going by the idea that advanced analytics for big data may equate to anything that uses AI, statistical modeling, machine learning, real-time data integration, natural language processing (NLP), optimization or various combinations of these to generate forward-looking insights, many oil and gas companies are leveraging the technology. More than 22,000 oil and gas assets are adopting advanced analytics platforms across upstream, midstream and downstream operations, according to 360 Research Reports. Yet an EY report notes that only 23% of oil and gas companies say advanced analytics lives up to expectations.

That’s because turning advanced analytics for oil and gas — as the sector sees data generation growing by leaps and bounds thanks to increasingly smart equipment and always-connected infrastructure — into a value-driving asset is a lot more complicated than you might think. Leveraging big data in oil and gas for advanced analytics requires integrating data sources, implementing big-data-compatible storage and building processing pipelines. But that’s only the beginning. Adopting the technology also requires selecting the right analytics, visualization and reporting capabilities, as well as managing security and governance.

Building an advanced analytics solution for a new world of big data and new expectations — such as agentic and autonomous AI decisions — comes with unique challenges, which require domain- and technology-specific expertise to overcome. That’s why many companies consider hiring oil and gas IT services experts — they don’t have such talent in-house.

Why big data analytics matters for the oil and gas industry

Leveraging big data analytics in oil and gas has the potential to strengthen competitive edge through use cases such as predictive maintenance and real-time asset performance monitoring. The technology can also help oil and gas companies tackle other long-term industry challenges, such as climate-driven mandates, supply chain unpredictability and cybersecurity threats.

That matters now that the days of competing on new hydraulic technology are likely behind us. Productivity gains are stagnating, while sector margins are narrowing as operational costs rise. Consider this: in the U.S., new well oil production rose by less than 2% YoY in 2025, while costs are expected to increase by 2% to 5%.

On top of that, investors expect strong capital discipline alongside returns. So, maximizing asset performance becomes paramount for oil and gas executives.

In brief

  • Big data analytics helps oil and gas companies improve operational efficiency, enhance environmental management, optimize drilling and exploration, and more.
  • Implementing big data analytics requires attention to data collection, storage, processing, analytics, visualization, reporting, security and governance.
  • Adopting big data analytics may require overcoming challenges such as disparate data formats, data silos, privacy, security and data quality management.

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What makes big data analytics tick: Core components

1. Data collection and storage

Common sources of big data in oil and gas include:

  • IoT sensors that measure asset performance and condition or capture production data (fluid pressure, temperature, composition, etc.)
  • SCADA systems
  • Drilling tools with logging and measurement capabilities
  • Seismic and narrow-azimuth towed streaming (NATS) data
  • External sources: market prices, weather data

This data (whether structured, unstructured or semi-structured) has to reside somewhere, too. Traditional databases won’t hold it; they’re not made to handle its volumes, variety and velocity. That’s why big data solutions use data lakes or warehouses to capture and preserve that data.

2. Processing and analytical techniques

Captured data can’t be analyzed as-is. Mismatched formats, corrupted values and incomplete entries will taint the output. So, it has to be cleaned, standardized and organized first. The Upstream Oil & Gas Data Management Trends: Challenges and Potential Solutions report discusses primary data types including seismic, wells, drilling and completion, reservoir and production volumes, and noted as challenges data silos, quality and consistency, standardization, volume and complexity, accessibility and security, and data governance. It proposes solutions around advanced data integration platforms, cloud computing, analytics, AI and comprehensive data governance frameworks.

3. Visualization and reporting

No one wants to dig through code or tables to understand the results of big data analytics. This is why this software component is so important: it makes those insights truly user-friendly.

Data visualization tools, for example, can power dashboards for monitoring production data in real time. Reporting tools, in turn, take the analytics output and generate reports using the established format.

4. Security and governance

Big data solutions can be vulnerable to fake data generation attacks and data poisoning alongside the more traditional cybersecurity threats. In the oil and gas sector, fake data generation could lead AI models to predict that no maintenance is needed on equipment that actually is on the verge of catastrophic failure that can cause fires, for example. Securing them requires a comprehensive security strategy that’s built on:

  • Zero-trust architecture
  • Granular access controls
  • End-to-end encryption
  • Automated monitoring and incident detection
  • DevSecOps best practices

A data governance framework, in turn, ensures data quality, security and availability across the enterprise. It defines data governance goals, roles and responsibilities, data standards and policies, auditing procedures and governance tools.

How big data can benefit the oil and gas industry

Depending on the use cases and underlying business goals, big data analytics can:

  • Reduce production costs by as much as $3 per barrel, according to BCG, with more precise exploration and drilling, enhanced production and predictive maintenance
  • Optimize operations (most notably drilling and production operations), which BCG says could unlock $275 billion in value
  • Prevent as many as 140 hours of unnecessary downtime with predictive maintenance and operating conditions monitoring and forecasting, according to Deloitte
  • Optimize maintenance schedules to reduce costs and failures by as much as 40%, according to the same report
  • Enhance environmental management with environmental impact monitoring, optimization, forecasting and modeling for new sites
  • Improve exploration success rates and increase asset yields with real-time drilling parameter adjustments
  • Enhance decision-making with real-time production and asset performance data

Implementing big data analytics isn’t always simple

Like any technology, adopting big data analytics effectively requires identifying the right use cases, ensuring workers can use the new technologies and coordinating across departments. Unfortunately, this is exactly where oil and gas companies struggle in new technology adoption, according to EY; 85%, 84% and 82% of respondents, respectively, classified these as major or minor technology adoption challenges.

A solid data foundation is the key to success in big data adoption. Building it, however, is easier said than done. Among emerging technology adopters, EY reports that 96% of oil and gas executives say turning complex data stores into useful information is a key challenge in their crosshairs. Moreover, according to the same report, 98% also focus on tackling data governance and compliance challenges.

Adopters also have to brace themselves for dealing with hurdles including integrating data from multiple sources with inconsistent, proprietary or inaccessible formats; breaking existing data silos;  managing large dataset sizes and volumes (petabytes at a time for a single seismic survey); ensuring low-latency real-time processing, including in remote areas with poor connectivity; mitigating cybersecurity, privacy and compliance risks; and securing the talent required to implement and maintain big data solutions.

5 ingredients for a successful big data analytics initiative

The tough reality is that big data advanced analytics implementation can go wrong in so many ways. Large volumes of data can lead to high latency and performance issues. Inconsistent formats or corrupted data can undermine output accuracy. Poorly selected use cases can lead to underwhelming ROI.

To avoid these and other worst-case scenarios:

  • Analyze your data maturity. Get the lay of the land: evaluate the existing data sources, data quality, collection and integration processes and analytics solutions. Create or review your data strategy, as well. According to the Journal of Business, Communication & Technology, in Exploring the Adoption of Big Data Analytics in the Oil and Gas Industry: A Case Study, the biggest technological issue is data quality, as companies operating in the oil and gas sector deal with the data that comes from various sources in various formats. “This leads to data that is dispersed across multiple databases but may lack a standardized and organized format, which is detrimental to data consolidation and quality,” it reports.  
  • Keep a close eye on data quality. Robust data quality management wards off errors, inaccuracies and inconsistencies in the output. Define a strategy for it and implement comprehensive data cleansing, validation and standardization.
  • Build a compelling business case. Start with the tangible pain points, objectives and use cases; not the desired technology. In oil and gas, these are very much related to reducing risk and preventing catastrophic failure; optimizing high-cost, long-life assets; and making better decisions under uncertainty. Then, verify big data analytics is the right choice for the selected use cases and conduct a feasibility and ROI assessment.
  • Invest in change management. The conventional wisdom states that 70% of success hinges on it. So, ensure cross-departmental collaboration, revamp processes to maximize gains from big data analytics and provide training and upskilling for end users.
  • Align infrastructure with big data requirements. Big data can span whole petabytes, which would strain the capabilities of many cloud providers. Besides, in oil and gas, data may come from locations with limited bandwidth. Consider using edge and distributed computing to overcome these challenges.

Big data options

There are two main ways to prepare big data for analytics:

  • Batch processing. Data is broken down into batch jobs that run according to a predefined schedule (e.g., every 5 minutes). This approach works only if this latency is acceptable.
  • Stream processing. It’s used for real-time insights, with latency spanning milliseconds to seconds. This approach uses real-time messages to filter and prepare data for analysis.

Big data solutions can use four types of analytics to produce insights:

  • Descriptive analytics involves analyzing what happened using historical data
  • Diagnostic analytics answers why something happened
  • Predictive analytics forecasts what will happen based on past and real-time data
  • Prescriptive analytics identifies what should be done next based on predictive insights

5 big data applications in oil and gas to know

Big data can serve a variety of purposes, from identifying the highest-ROI drilling spots to optimizing drilling and completion strategies.

1. Data-driven optimization of drilling and production operations

Deciding where and how to drill is the million-dollar question in oil and gas. Thanks to big data analytics and solutions, like LLM-powered data exploration tools from DXC in partnership with AWS, seismic and geological information can unveil the highest-potential drilling spots and optimize well placement. Big data in the oil and gas industry can also aid in predicting geological challenges and performing continuous seismic analysis.

When it comes to production, big data analytics can track asset performance, yield rates and equipment conditions in real time. These insights can power predictive maintenance, identify the best operating conditions, and dynamically adjust drilling parameters.

2. Improving recovery and reservoir performance

Every reservoir is unique, so a cookie-cutter approach doesn’t always work. The wealth of data — well logs, seismic surveys, downhole sensor data — can help identify the most effective recovery plan.

For example, downhole sensors can collect temperature, acoustic and pressure data. Big data analytics can reveal shifts in reservoir dynamics in real time, enabling workers to quickly adapt to them and maximize reservoir performance.

3. Optimizing supply chain and logistics operations

As the world is heading toward oil and liquified natural gas oversupply, big data and advanced analytics in the oil and gas industry can help forecast demand and adjust production and prices dynamically. Moreover, big data can help promote sustainability in supply chain decisions, which matters for 71% of energy executives, according to EY's Future of Energy survey.

Big data analytics can also improve visibility into logistics operations, optimize inventory management, automate route planning and forecast material and equipment needs.

4. Cybersecurity

Oil and gas companies are prime targets for cybercriminals. In May 2025, Cybernews reported that over half of the world’s top oil and gas companies suffered a data breach within the previous 30 days.

Big data has several applications in cybersecurity: most notably, as Cybernews cites, it can detect threats in real time and respond to them immediately.

5. Environmental and safety management

In this domain, big data use cases in the oil and gas industry include automated leak detection, impact monitoring and compliance reporting. The historical environmental data can also enhance risk assessment for new sites, making it more comprehensive and precise.

The wealth of on-site data, in turn, can reveal when equipment failure becomes likely and alert workers about it. Preventing these failures reduces safety risks. Other on-site data can help identify risky behaviors or collect data for compliance reporting.

How oil and gas companies are already using big data analytics

BP has had big data in its sights for a decade now. In 2017, more than 99% of its oil and gas wells were already equipped with sensors. The sensors provide data on the condition of the infrastructure, as well as production performance, all in real time. Since then, BP also turned to big data to curb carbon emissions by optimizing process heat and detecting methane leaks.

What’s next for big data analytics in oil and gas?

AI is poised to be the next frontier of innovation in big data analytics. As the technology powering predictive and prescriptive analytics, it’ll continue to drive process optimization, asset performance and integrated operations. The industry’s investment into the technology is expected to almost quadruple between 2025 and 2029 in the United States, according to Deloitte’s 2026 report:

As for innovations in this field, explainable AI and agentic AI are likely to dominate the conversation:

  • Explainable AI seeks to resolve the black-box problem to improve reliability and trust in AI systems. Explainable AI techniques include prediction accuracy, traceability and decision understanding.
  • Agentic AI, in turn, is an AI system that can achieve certain goals with limited supervision. It is more autonomous and adaptable than traditional AI systems. In oil and gas, agentic AI could potentially perform tasks like seismic data analysis and emissions monitoring.

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More tech on track for the energy sector

While AI seems to be the next big thing for oil and gas digital transformation, it’s not the only technology worth mentioning here.

For one, big data analytics also powers digital twins, which are digital replicas of real-world systems, assets and locations. Digital twins can model real-world scenarios with outstanding accuracy, enhancing planning and decision-making.

Digital twins can be used to explore drilling sites and power predictive maintenance for equipment, for example.

Finally, there is one thing we can be certain of: the volumes of generated data will continue climbing. Leveraging it for real-time insights and strategic decisions alike is what will separate winners from losers in the oil and gas sector in the coming years.

Frequently asked questions about DXC Energy: Oil and Gas

Big data itself is characterized by its high velocity, volumes and variety. Big data analytics involves finding patterns and connections in large volumes of raw data.

 

Together with analytics, big data enables predictive maintenance, determines the highest-value drilling locations, monitors carbon emissions and reservoir dynamics and more.

Big data analytics is used in exploration, drilling, production, recovery, reservoir management, asset management and environmental management.