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Analytics-powered performance - opportunities for oil and gas companies

This Accenture study examines data integration, digital technology and the power of analytics in the oil and gas sector.


Oil and gas companies are struggling with data integration. Is analytics the answer? What steps can companies take to turn data into insight and action? This report provides insights from a survey of energy companies and provides a roadmap on how to create an analytically powered organization.

Energy is an industry dominated by engineers and scientists, working in disciplines that embrace facts and figures to reach conclusions and make informed decisions. Yet a recent Accenture survey of analytics practitioners indicates that only one in five energy-industry respondents report using an integrated, organization wide approach to analytics.

With the rise of big data and a wide range of new technologies, analytics have reached the tipping point.

This report explores the barriers blocking companies from generating improved outcomes from analytics and provides case studies of how organizations have leveraged analytics for tangible gains. It also offers a road map to gauge maturity on the journey to becoming a business where analytics drive competitive essence.


Read the report.


Energy companies have been measuring and monitoring production for years, using sensors and machine-to-machine communications. Many tools to boost performance, however, were not designed for analytical optimization. In addition, ad-hoc adoption has led to bolt-on tools and standalone analytics, which have not been integrated with the overall enterprise architecture.

To better understand progress to date, Accenture surveyed 35 analytics practitioners in oil and gas companies as part of a global research effort interviewing 600 executives in total across multiple industries. The survey addressed questions such as:

  • What are the key challenges with analyzing data?

  • How committed is senior leadership to adoption of analytics?

  • How widely are analytics used across the organization?

  • What are the overall levels of satisfaction with outcomes from analytics?

Key Findings

Summary of key challenges with analytics in oil and gas companies:

  • Poor data quality and lack of integration

  • Data collected is oftentimes not relevant to the business

  • No single, holistic data strategy, resulting in fragmented use of analytics

  • C-suite does not lead by example to mandate insight-based decisions

  • Patchy ownership of data across processes

  • Limited visibility of data across the breadth of processes


Four major technology developments are creating a tipping point, with new possibilities to enable faster, smarter business decisions:

  1. Convergence of information technology (IT) and operations technology (OT)
    Data from machines is generated, captured and integrated into IT systems for analysis, often in real time.

  2. Mobility and technology consumerization
    Mobility and user-friendly IT provide remote access to data, affording improved visibility and faster response.

  3. Disruptive architectures, including cloud computing
    Cloud provides cost-effective options to access large-scale computing and storage capability, as well as tools, all essential for analytics. Also, "on demand"is reshaping the IT operations model with the option for analytics-as-a-service.

  4. Big data analytics and in-memory computing
    The application of advanced analytic techniques to very large data sets is aided by in-memory databases that greatly reduce query time.


Three recommendations for becoming analytically-powered:

  1. Visualize the value and design for analytics outcomes

    • Ask the right questions

    • Develop a value-led road map

    • Identify gaps and work diligently to close them

    • Help the business visualize the value at speed

  1. Adopt an end-to-end process view, integrating enterprise and operations analytics

    • Define an organization-wide analytics strategy … and communicate

    • Leverage existing investments while embracing big data platforms

    • Close the loop defining the right metrics, across functions for insights that lead to action plans

  1. Promote a cultural shift to an analytically astute, insight-driven enterprise

    • Ensure leadership leads by example

    • Select the right analytics operating model

    • Get the most from the talent the company employs

    • Attract, develop and retain analytics talent

    • Foster new behaviors with a change enablement program


Contact our authors to discuss your Analytics Journey:

Jeffrey Miers
Managing Director, Energy—Analytics, North America

Kevin Donaldson
Managing Director, Energy—Analytics, Europe, Africa and Latin America

Senthil Ramani
Managing Director, Analytics—Technology, Asia Pacific

Richard Holsman
Managing Director, Energy Technology Lead