Digitizing Energy. Analytics-Powered Performance. Opportunities for oil and gas companies to improve business outcomes

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1 Digitizing Energy Analytics-Powered Performance Opportunities for oil and gas companies to improve business outcomes

2 Contents Introduction 3 Section 1: The current analytics landscape in 5 oil and gas 1.1 Survey reveals barriers to better business outcomes Insights into the analytics tipping point 11 Section 2: Seizing the opportunities for 16 analytics-powered performance 2.1 Upstream operations Downstream operations Corporate functions 24 Section 3: Achieving better business outcomes 28 from analytics in oil and gas 3.1 Accenture analytics capability maturity model Recommendations for becoming analytically-powered 31 Visualize the value and design for analytics outcomes 32 Adopt an end-to-end process view, integrating enterprise 33 and operations analytics Promote a cultural shift to an analytically astute, insight-driven 35 enterprise Conclusion 37 2

3 Introduction 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, organizationwide approach to analytics. 1 One of the industry s greatest challenges is data integration (perceived as a greater barrier in oil and gas than in other industries surveyed), with additional challenges including functional silos and cultural resistance to change all of which inhibit an integrated approach to end-toend processes. With the rise of big data and a wide range of new technologies, analytics have reached the tipping point. More than three-quarters of survey respondents in oil and gas indicate their senior leadership is highly or totally committed to factbased decision making. But will companies be able to turn this commitment into a healthy return on investment? This report explores the barriers blocking companies from generating improved outcomes from analytics, whether in upstream or downstream sectors, and in corporate functions. It considers areas where companies should consider focusing near-term investments, 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. Oil and gas companies that want to use analytics to power their businesses will start by visualizing the value and designing for improved outcomes. Analytics can be used to improve exploration and drilling, especially related to unconventionals. Downstream businesses can apply analytics for greater insights into logistics and supply chain, marketing and trading to better manage operations end to end, from the demand side to commercial channels. The leaders will promote a cultural shift, fostering an analytically astute, insightdriven enterprise that relies on data-driven decision making. Thinking outside the box, they will integrate new technologies digital, mobile, cloud and analytics not only to improve current activities, but also to design dramatically improved workflows aligned with high performance. High performance hinges on the ability to gain insights from datainsights that enable organizations to make better decisions at the right time. Action Insights Analytics 3

4 Cost effective outcomes Analytics 4

5 Section 1 The current analytics landscape in oil and gas 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. There is skepticism about analytics, which is understandable. Consider the digital oil field, a concept that emerged some 15 years ago. Digital oil field is a suite of interactive and complementary technologies for operators, business partners and service companies to combine data and knowledge management, using enhanced analytical tools to develop more efficient business processes and make timely decisions. The promise of the digital oil field has, however, not been fully delivered, with hurdles ranging from a lack of sponsorship across functions, to weak workflow integration and data ownership, to technology limitations. Additionally, the quantity and variety of data vary greatly with asset age. Older facilities lack instrumentation, thereby limiting advanced analytics. Conversely, thanks to the sensors and automation in newer facilities, torrents of data are being produced, but few companies have worked out how to turn growing amounts of data into insights yielding improved profitability. 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. 2 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? 5

6 1.1 Survey reveals barriers to better business outcomes Survey responses show that energy companies have started to mobilize to embrace some of the practices necessary to achieve greater return on investment with analytics: recruiting the right talent, investing in tools and software, and evolving mindsets toward factbased decision making. Most energy companies surveyed are demonstrating commitment to analytics in multiple ways. Survey findings, however, reveal that much more needs to be done to realize improved, analytics-driven outcomes. From the survey, and also from working with energy companies throughout the world, Accenture sees an integration gap, or missing middle, as the key barrier to better business outcomes from analytics in energy. Numerous signs of the missing middle are evident from survey responses relating to lack of integration of data and systems, functions and workflow, and corporate strategies and culture. Figure 1. Summary of key challenges with analytics in oil and gas companies. Poor data quality and lack of integration C-suite does not lead by example to mandate insight-based decisions Data collected is oftentimes not relevant to the business Patchy ownership of data across processes No single, holistic data strategy, resulting in fragmented use of analytics Limited visibility of data across the breadth of processes The missing middle in energy companies In many companies, a breach the missing middle is evident in multiple dimensions: between the data available and disparate systems used; from the lack of end-to-end integration across processes or workflows; and between corporate strategies and analytics efforts at functional and departmental levels. With this gap, energy companies struggle for a complete and timely assessment of the impact of operational decisions on corporate performance. Likewise, corporate entities are unable to factor in day-to-day field operations in their objective setting and planning decisions. Source: Accenture Analytics Adoption Study, March

7 Integration of data and systems The energy industry has some way to go to possess the data quality necessary for sophisticated analytics. That is to say, to secure insight through analytics using real-time data across multiple assets and spanning a range of activities, including operations and back-office functions. As indicated by responses to survey question 23 [Q23], data integration is the greatest challenge relative to data quality and the ability to analyze data (see Figure 2). Sixty-three percent of energy respondents (significantly higher than 50 percent for the other industries overall) perceive data integration as a barrier. Responses to additional data-related survey questions indicate that more than onehalf of energy respondents cite format, completeness and accessibility as issues. Accenture attributes problems such as these to a lack of data governance, which results in disparate systems and redundancy. One-half of respondents identified their current technological resources and systems as one of the top two inhibitors to the wider use of analytics. [Q21] The technology hurdle is, however, not likely due to lack of investment; less than one-half of the respondents cited software and applications investments as a challenge [Q23]. Energy companies have relatively formal structures for data related to enterprise departments, such as finance, largely because of the enterprise resource planning (ERP) backbone defining the related activities. In general, however, the data collected is lacking not only in quality, but there is also a disconnect between what is collected and its relevance to the business; only 40 percent see their data as relevant [Q3]. And yet, nearly 70 percent of respondents [Q5] indicate that unique, proprietary data is of value to differentiate their companies, a slightly higher percentage than the overall level across all industries. There is a data disconnect between what is collected and its relevance to the business; only 40 percent of respondents see their data as relevant. Figure 2. Please select what are your key challenges when it comes to analyzing data? Data integration Outcome from data 50% 58% 60% 63% Data collection 48% 54% Data interpretation and approach Identifying insights from data (e.g., industry data models) Software and applications investment (e.g., virtualization) 42% 43% 49% 49% 49% 49% Talent acquisition 34% 47% Global Energy Base: Total Energy Respondents (n=35) Global Total (n=600) Source: Accenture Analytics Adoption Study, March

8 Functions and workflow Not surprisingly in this asset-intensive industry, three-quarters of energy respondents confirm that their use of analytics is primarily focused on generating insights and setting targets for quicker, more effective decision making (77 percent), as well as improving efficiency and productivity (74 percent) (see Figure 3). In addition, nearly two-thirds of respondents continue to manage analytics by specific function or department, which inhibits an integrated approach for end-to-end workflows. Accenture sees this finding as further evidence of the missing middle. Accenture experience shows that the missing middle persists, in large part, due to a lack of data ownership across the breadth of processes. While the CIO and back-office activities at headquarters might have worked together to help achieve corporate goals related to improved data management, reporting and analytics, the efforts in the field, in particular, have remained ad-hoc and siloed. Attempts to bridge gaps and improve integration are often limited because of the complexity and lack of prioritization. Additionally, there has been a lack of focus on analytics related to the core activity of production. Only one out of two of the executives interviewed said their organizations were using sophisticated analytics related to their operations, compared to more than two-thirds of respondents using analytics in finance and customer management [Q1a]. 3 Rather than taking an organizationwide approach, 60 percent of respondents are managing software and systems at a departmental level. Figure 3. In which, if any, of the following business functions (areas) are you currently setting targets based on analytics? (Q14b) Quicker/more effective decision making Improved efficiency/productivity Market performance/sales 62% 61% 59% 63% 74% 77% Competitive performance/market share 47% 54% New concept/product development Cost savings 40% 40% 48% 48% Global Energy Base: Total Energy Respondents (n=35) Global Total (n=600) Source: Accenture Analytics Adoption Study, March

9 Corporate strategies and culture Most oil and gas companies are running global portfolios, complex and interconnected operations across which they take steps to confirm a company-wide analytics strategy. While 60 percent of the respondents say data is currently lacking in relevance, the good news is that 71 percent of energy respondents (significantly more than the global sample, 62 percent) confirmed that the C-suite is demonstrating a commitment to a data strategy by establishing exactly what data needs to be collected, shared and how (see Figure 4). The findings show strong leadership support for fact-based decision making: 77 percent are highly or totally committed. The commitment is reinforced by the appointment of a chief data officer at 60 percent of energy companies surveyed, of which 40 percent had recruited the role within the past 12 months [Q10a/b]. Nevertheless, part of the missing middle is the need not only to collect and analyze the data, but also to enable the workforce to act on the insight. Analytics should not, after all, be intended primarily for a data officer and his or her corporate team. Only 40 percent of respondents confirmed that becoming more analytical in decision-making styles and methods across the enterprise was a longterm goal. Indeed, only 29 percent of middle managers get all of the training needed to use analytics effectively in day-to-day decision making [Q21]. Only one in five energy companies attests to routine use of analytics as part of an integrated, enterprisewide approach ingrained into the fabric of the company. Only 29 percent of middle managers get all of the training needed to use analytics effectively in day-to-day decision making. Figure 4. How has the C-suite demonstrated a commitment to a data strategy for your firm? (Q12) Establishing what data needs to be shared and how, and what needs to be collected and how 62% 71% By asking teams to evaluate data, to not only describe what is happening 60% within the company, but also to predict trends 63% Defining a data strategy plan, with a clear owner, that has been communicated across the organization 49% 51% Identifying the company s data inventory and how it is being catalogued Ensuring a truly integrated data strategy with suppliers, service providers and other partners with the focus on data: specifying which data each party can access, who owns what, how it is to be used and shared Making a commitment to source top data talent to address a data-skills gap; determining where the gaps are greatest and giving ownership to the right teams to fill them Putting someone in charge of reviewing the concept of data exchanges through which data can be shared internally and maybe even bought and sold with external partners Giving ownership to others to map out ways to ratchet up expectations about how to better analyze data and how to better report on it 34% 49% 50% 49% 50% 46% 49% 50% 40% 50% Global Energy Base: Total Energy Respondents (n=35) Global Total (n=600) Source: Accenture Analytics Adoption Study, March

10 As long as analytics management, systems, data and talent are managed at functional levels, the missing middle is destined to remain [Q7] (see Figure 5). Bridging the gaps will require the full integration of corporate and financial objectives with the operational and enterprise analytics driven from a field level. As additional input to this report, Accenture commissioned a separate crowd-sourcing survey, conducted by 10EQS, of energy executives with analytics responsibilities and experience. This survey inquired about pain points, opportunities and barriers to analytics adoption. Some of the quotations from executives (who remain anonymous as part of the 10EQS survey methodology) are highlighted in this report. Figure 5. When thinking about your company s use of analytics, please state if used broadly across the organization, in specific functions or not at all. Energy results Analytics management 37% 52% 11% Analytical software/systems 34% 60% 6% Corporate data 31% 60% 9% Analysts/analytical talent 31% 58% 11% Across the organization Specific function areas of the company Not at all Base: Total Energy Respondents (n=35) Global Total (n=600) Source: Accenture Analytics Adoption Study, March

11 1.2 Insights into the analytics tipping point The macro trends affecting the industry are widely known: growing energy demand, rising capital costs, global competition, increasing regulation and compliance. To address these issues, oil and gas executives are focused on boosting production and return on investment, while managing safety and risk. At the same time, a range of technologybased, digital developments, are coming into play, with oil and gas companies now much more actively assessing how they might significantly improve production and meet their safety and risk goals. For more than a decade, the vision for these four technologies has been shared, but only recently has the technology attained the maturity to underpin a genuine transformation (see Figure 6). These technology trends are gaining momentum concurrently, which amplifies their impact and ability to transform. Digital, mobile, cloud and analytics technologies are converging, producing the potential for dramatically improved business outcomes. Each of these digital trends is (by definition) related to data: how it is generated, captured, integrated, managed, analyzed and stored. In this context, oil and gas companies will digitize processes through these technologies, and will start to compete on data management and analytical proficiency as has been the case in other industries. This development potentially will change how energy leaders run operations, manage people and meet customer needs. Figure 6. 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. 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. 2. Mobility and technology consumerization Mobility and user-friendly IT provide remote access to data, affording improved visibility and faster response. 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. Source: Accenture analysis,

12 Figure 7. A detailed look at technology trends contributing to the analytics tipping point in oil and gas companies Convergence of information technology and operations technology (IT/OT) Trend Related terms Relevance to oil and gas The gap (to date characterized by different systems, standards and manufacturers) between operations and information technologies can be bridged, enabling the integration of data from sensors and devices, with control systems (SCADA), middleware (e.g., manufacturing execution systems) and back-end IT systems (e.g., ERP). Data from machines can now be captured and integrated into IT systems for analysis frequently in real time. Machine-to-machine (M2M) communication; industrial internet; sensors and meters. Directly relevant to asset-intensive industries, especially where assets are geographically distributed. 2. Mobility and the consumerization of IT Trend Advances in wireless technology mean that consistent, wireless connections in industrial landscapes can be achieved. The landscape of intrinsically safe mobility is about to change with the introduction of I-Safe tablet devices and new options for Wi-Fi connectivity. Technology innovation in mobility is further extending uses (e.g., drones, intelligentvision eyewear). Related terms Relevance to oil and gas Also, as a result of consumer-oriented, hand-held technologies, oil and gas workers in enterprise functions and operations want highly intuitive applications facilitating the collection, use, treatment and sharing of data related to their work. Investment in mobility platforms inevitably will lead to wider access to real-time data and analysis. Bring your own device; radio frequency identification (RFID); track and trace; embedded mobility (M2M); mobile as the new financial wallet. Highly relevant to industries with geographically dispersed, or with field-located assets and workforces, to provide access to performance data. Also relevant to support mass customer interactions. 12

13 What about social? Nearly 80 percent of US smartphone users check social media within 15 minutes of waking up. As consumer technologies come to the workplace, social networking is reshaping how companies interact with customers and third parties. However, the impact in oil and gas is not yet as significant as with trends such as cloud, mobility and big data. Energy companies are likely to consider opportunities with social networking and analytics related to seamless collaboration between employees and customers, as well as potentially even devices. In downstream, for example, the monitoring of social media postings can enable the evaluation of market sentiment, including potential opportunities, brand health and competitor information. Likewise, in upstream, companies may monitor postings to assess positive and negative sentiment related to their environmental or their health and safety record. 3. Disruptive architectures, including cloud computing Trend Relevant terms Relevance to oil and gas The on-demand dynamic is gaining momentum in the oil and gas industry. This development affects the IT operations model in data center operations and enables flexible sourcing. Most companies are moving to capture efficiencies from procuring server and data storage capacity as a service. Also, off-premise models such as software as a service and analytics as a service enable companies to outsource certain technologyrelated activities, while focusing on what they do best. Cloud computing: private, public, hybrid, community; software-as-a-service (SaaS); platform-as-a-service (PaaS); infrastructure-as-a-service (IaaS). Increasingly relevant to oil and gas companies as a means to harness the computing power required to manage massive data volumes, at reasonable cost. 13

14 4. Big data analytics and in-memory computing Trend Big data describes the exponential growth, availability and use of information, which is diverse in type and not necessarily structured. Data volumes have multiplied in all industries, including oil and gas. Big data analytics is the application of advanced analytic techniques to very large data sets. It is not longer necessary to centralize big data because data representation architecture is being decoupled from applications. The convergence of multiple technologies has led to the tipping point for analytics. Consequently, people in oil and gas businesses are on the cusp of using tools to make faster, smarter business decisions. Relevant terms Relevance to oil and gas As critical data volumes increase, the speed of computing becomes vital. Open-source tools make it possible to make sense of huge amounts of data, automating the integration of traditionally structured and unstructured data. Advances in extract-transfer-load (ETL) tools ease the process of gathering and integrating information from disparate sources for statistical analysis and modeling. The openness of Internet protocols further supports organization-wide analytics. Most importantly, compared to traditional disk storage, in-memory data greatly reduces query time, providing for faster and more predictable performance, reducing processing time from hours to seconds. Data management; business intelligence; analytics; in-memory database. Opportunities for companies generating large volumes of complex data from multiple sources (from instrumented machinery to global positioning signals, text messages, social network postings and Internet searches). The insight gained from analytics applied to big data enables oil and gas companies to improve core operations and business functions. In the future, energy companies that are leaders in analytics-heavy activities could start to sell their capabilities to peers on a pay-per-use basis. Source: Accenture Analytics,

15 Digital technology trends such as social, mobile, and cloud, are creating the tipping point for greater analytics adoption. None of these digital trends, including analytics, are a solution in of themselves to a business issue or opportunity. Nevertheless, when applied to meet a business objective, they can help increase efficiency and engagement by optimizing processes, thereby enabling new ways of interacting with all the actors of the energy industry (customers, suppliers, employees, partners, regulators...). Digital technologies will impact all parts of the industry value chain, particularly core process areas in upstream and downstream. Figure 8. Impact of digital technologies (mobility, analytics, cloud, social) on key oil and gas processes. Accenture process model for energy Planning & Enterprise Management Enterprise Strategy & Performance Management Enterprise Risk Management Portfolio Management Stakeholder Relationship Mgmt Business & Competitive Intelligence Upstream Downstream Exploration & Appraisal Channel, Network & Offer Management Drilling & Completion Marketing Operations (B2B, B2C/Retail) Field Production & Asset Operation Plant Production & Asset Operation Development / Capital Projects Environment, Health, Safety (EH&S) Integrity Management Decommissioning Operational Business Operation Support Research & Development Hydrocarbon Accounting, Planning & Forecasting Integrated Business Planning Process Engineering & Automation Integrated Activity Planning Management of Change Production Forecasting & Planning Laboratory, Chemical & Quality Mgmt Construction & Project, Facilities Engineering Hydrocarbon Accounting Commercial Management Contract & Nomination Management Trading & Risk Management Joint Venture Management Product Supply Chain Management Storage & Metering Bulk Movement & Transportation Terminal Operations Transportation & Distribution Non-Hydrocarbon Supply Chain Management Supply Chain Strategy Requirements Planning Sourcing & Procurement Warehousing & Logistics Requisition to Pay Support Services Finance & Accounts Human Resources & Knowledge Mgmt Information Technology Data & Information Management Physical Infrastructure Regulatory Compliance Business Process Lifecycle Mgmt 1 Business Value of Impact Social Mobility Analytics Cloud High Medium Low None Source: Accenture analysis,

16 Section 2 Seizing the opportunities for analytics-powered performance With the emergence of the analytics tipping point, where should oil and gas executives focus investment? Companies perceive some of the likely benefits, but which areas are likely to provide the greatest returns? In today s environment, where instant response is expected, oil and gas companies need advanced systems that enable them to do the things they already do but faster and with enhanced flexibility. Furthermore, advanced analytics solutions are needed to manage the volume of data (structured and unstructured), in upstream exploration and production (E&P), downstream and in corporate functions. Issue Step 1 16

17 Step 3 Step 4 Outcome Step 2 17

18 2.1 Upstream operations Oil and gas companies have learned how to predict the performance of conventional oil and gas wells. But unconventional wells perform unpredictably. Extracting more oil and gas from reserves remains a universal concern, and the efficient drilling of hundreds of unconventional wells is vital for profitability. Analytics for drilling Complex algorithms and immense computing power are helping to interpret some of the largest data sets ever assembled, thereby aiding exploration of fields locked in shale formations and beneath the ocean floor. Powerful tools enable analysis of multiple data sets, and interpretive software and visualization tools make the information accessible to a wider internal audience. Seismic data can be gigabytes in size, but this information is not what is typically thought of as big data, which tends to be transaction-based and can include unstructured data, such as social media postings. Unstructured data is less critical in E&P. Analytics provide E&P companies with a better understanding of what is happening in reservoirs, modeling the way fluids move through rock formations and for optimal well placement. Analyticsbased approaches might help to extract another three to five percent, meaning extra cash flow, which is important in high-priced commodity environments. A largely untapped area for energy companies is analytics across assets, particularly in unconventional fields. Companies are starting to look at all the wells a company has drilled and, in addition to applying first principles, incorporating statistical data for insight into which wells are similar to the ones about to be drilled. 5 Analytics-powered approaches could help companies identify likely upsets in drilling and production before they occur. One day of a shutdown could mean avoiding an estimated loss of $1 million per day in total costs. A largely untapped area is analytics across assets, particularly in unconventional fields. Figure 9. Industry and corporate pain points where analytics can potentially drive better business outcomes. Upstream operations Downstream operations Corporate operations Forecast and deliver production commitments 18 Efficiently deliver unconventional plays Enforce rational and appropriate working standards Manage equipment supply chain Execute capital projects to time, budget and specified scope. Source: Accenture Analytics, March Optimize end-to-end the integrated value chain from plant to pump Configure the supply chain to enable cost reduction in the manufacture of specialty lubricants Measure and manage market risk at commercial and logistics levels Enforce rational and appropriate working standards. Optimize cash flow to effectively meet planned capital expenditure commitments Enable or manage contingent labor Measure and manage market risk at a commercial level Enforce rational and appropriate working standards Execute capital projects to time, budget and specified scope.

19 Conducting statistical analysis on data within a company is relatively straightforward, although the analytics processes might be complex. An unexplored opportunity for companies is to enlarge the sample of data with information from other companies operating in the same location. Analysis enabled by new visualization tools could enable scientists to see formerly unseen patterns and perhaps improve production yields by several percentage points. Sources of data are limited not only to the drilling process, but also to nontechnical information, for example, how long it takes to move a rig, or which rig is more effective under which conditions. Analytics can enable monitoring of operations across a basin, rather than from the more limited perspective of isolated wells, thereby reducing downtime and increasing speed to production. Greater insight from analytics also could lead to more efficient batch drilling. Case in point Building the foundation for a high-performing analytics company This company is a privately-held oil and gas company in North America. As total production volume declined from roughly 250,000 barrels of oil a day in the 1980s to approximately 130,000 barrels a day, advanced analytics were seen as a way to bend the production curve. The company possessed enormous amounts of data both real time and historical but tools and systems were spreadsheetbased and the results localized. Company leaders sought new ways to use analytics to drive greater benefits. The company has developed an analytics program to build a foundation for an analyticallyastute company. They are implementing SAS s Predictive Asset Maintenance and Visual Analytics platforms, and using the SAS tools on two test cases reducing subsurface impairment by optimizing water injection and increasing well production by optimizing rod pump set points. In addition to the technical mandate of integrating SAS tools and demonstrating their value to the company, this project will build enterprise-wide features of an analytic operating model defining the company s analytics vision, proposing a way to organize analytics talent, detailing business process implications, and developing a talent development and acquisition plan. The company is adopting a broader, more holistic approach to analytics that moves beyond limited point solutions to innovative ways to resolve business issues, organize talent and enhance processes. Improving information sharing and collaboration across companies E&P projects typically involve operators who hold onto their data rather than share it. It has been scientifically demonstrated, however, that greater pooling of data can yield benefits for multiple parties. The success of statistical analysis depends on the size and quality of available data. With more diverse data sources, the industry could develop a clearer picture of ways to improve drilling in unconventional fields. Companies have an immediate opportunity to learn more from their own internal data on E&P projects. Combining this data with similar information on wells drilled by other firms nearby (or in similar geological formations) would very likely yield greater insights, leading to improved outcomes. The greater sharing of information would likely result in insights fueling continuous E&P improvement. While pooling of drilling data might seem anti-competitive in some circles, an argument could be made that improved practices are in the public interest as well as the industry as a whole, given continuing worldwide demand for energy. A less controversial application of the cross-company collaborative approach might involve pooling energy industry data to gain insight into ways to improve health, safety, security and environmental practices. 19

20 With rising capital costs related to E&P, the use of digital technology and advanced analytics is likely to increase. Interest is growing in using analytics to anticipate risk, be that from a preventive maintenance vantage point, or from health, safety and environmental perspectives. Analytics for predictive asset maintenance The convergence of operations technology (OT) and IT is improving maintenance, enabling remote monitoring of equipment and planned (rather than unexpected) shutdowns. Indecision about the reliability of a drilling rig, for example, has a significant impact on cash flow. In addition to using innovative technology tools, companies need to close the loop with associated processes. Asset-maintenance insights need to be communicated to multiple parties and workflows need to be integrated. Equipment and spare parts will be more readily available, for example, through greater sharing of information in near real time. Analytics related to asset maintenance also affect other processes, as the Taleris case study illustrates (see sidebar), influencing outcomes not only for capital assets, but also for scheduling and human resource utilization. Case in point: Taleris Recovering faster from unplanned events, airlines get a lift from big data analytics Companies in other asset-intensive industries already are achieving substantial results from advanced analytics. In the airline industry, for example, protracted delays undermine profitability, and analytics-powered solutions enable companies to recover faster and improve maintenance to avoid equipment breakdowns. New technologies, for example, enable scanning of data from tip to tail on parts, components and systems. As Norm Baker, president and CEO of Taleris, a GE-Accenture joint venture, explains, Significant benefits can be realized through predictive analytics technologies, which leverage an aircraft s data within the context of the operations so one can address an issue before it occurs. Etihad Airways, the national airline of the United Arab Emirates, is working with Taleris as part of a proactive approach to manage its fleet of Airbus and Boeing aircraft. The technology analyzes data from multiple sensors on components and systems, and warns of imminent problems. According to Werner Rothenbaecher, Etihad s senior vice president/technical, With Taleris prognostics, we will be able to predict future faults and take proactive measures, which result in less unscheduled disruption to our global operations. Analytics-powered solutions are providing insights about: Planning operations. By evaluating millions of schedule combinations in minutes, optimizers help reduce crew costs by up to five percent, thereby boosting aircraft utilization. Recovery. To minimize losses, a decisionsupport service quickly generates costeffective solutions to rapidly recover aircraft, crew and passenger schedules. Operations management. Up-to-theminute situational awareness enables operators to make faster business decisions aligned with improved business outcomes. 20