From predictive asset maintenance to personalized customer service, data analytics have redefined how energy companies operate, produce and deliver power—yet many energy companies aren’t reaping all the benefits that analytics and the IoT have to offer. This white paper explores the current state of analytics, IoT and BI in the energy industry; common roadblocks; and opportunities to bridge the analytics gap.
Modern analytics and business intelligence change how companies think, operate, retain customers, and produce and deliver power. But many organizations have yet to take full advantage of powerful and proven business insight tools and practices.
Energy and utility companies no longer need to make decisions without the benefit of real-time data and advanced analytics. They draw on many kinds of important data related to critical aspects of their business such as customers, transactions, the performance of industrial assets and facilities, and the outcomes of service operations. Technology providers serve the energy market with solutions for artificial intelligence, machine learning and predictive analytics. Leading energy companies are beginning to use predictive analytics to become more productive and improve the customer experience. However, newer and potentially valuable technologies that could complement analytics are widely considered experimental, and many opportunities for business insight do not see action. If energy companies lag in reaping the benefits of modern analytics, how can they catch up?
This whitepaper explores the nexus of business intelligence, predictive analytics and internet of things (IoT) adoption in the energy and utilities industries. It offers a high-level overview of the energy sector in 2019 and beyond, discusses the current state of analytics and BI in the energy vertical, and notes challenges companies face in working with analytics. It also identifies promising approaches to achieve results from analytics and IoT for energy companies.
Section 1: Evolution and Disruption in the Energy Sector
Section 2: Analytics in the Energy Sector is Business-Critical and Becoming More So
Section 3: Current Analytical Practices in the Energy Industry
Section 4: What is Holding Energy Industry Analytics Back?
Section 5: Roadmaps for Implementing Energy Analytics
Section 6: What You Can Do Now
For 2019 and beyond, analysts anticipate more changes and disruptive events in the energy sector than the industry has experienced in recent years. Intense competition, technological evolution, and changes in customers’ attitudes and preferences are gaining momentum, while regulatory measures are adjusting at a slower pace.
Electric utilities deal with seasonal increases in demand driven by extreme summer heat, but over recent years, electricity consumption growth has been slower despite the growing popularity of electric vehicles. However, accelerated electricity consumption in mid- and late2018 shows that trend may be changing. Electric utilities will need to manage change to meet this new demand, which includes satisfying consumers who expect more sustainable sourcing and carbon footprint reduction1.
Oil and gas producers continue to face strong demand worldwide, but find it logistically challenging to transport oil or gas from North America to the Asian and Pacific Rim countries that need it. Overall, considering both conventional and nontraditional ways of accessing resources, supplies are plentiful, so oil prices are shrinking. However, production in existing oil fields is declining by close to four percent every year so, despite current availability, oil and gas companies are augmenting their capital expenditures in oil and gas exploration to tap into new sources. Industry-wide, these investments are expected to increase by six percent every year between now and 20252.
However, that may not be enough to counteract a decades-long decline in new, conventional oil and gas discoveries. Many companies practice conservative finance and resource management, for example, by delaying non-emergency spending on industrial asset maintenance. But this isn’t always a sustainable approach and could eventually incur greater costs than what was temporarily saved. At the same time, the loss of critical skills and the need for new talent in the industry are exacerbated by demographic changes and earlier workforce reductions to save costs.
Especially in the highly developed countries of North America and Europe, electric vehicles are experiencing rapid, increasing adoption that could amount to 150 percent worldwide during the next three years3. Rising customer demand is leading to more planning, budgeting and building of charging stations along the freeways and highways of public infrastructures.
Regulators and utilities are rethinking how power is distributed, consumed and monetized. Some U.S. states and Canadian provinces are creating their own environmental regulations, which could complicate the regulatory landscape and require greater agility in decision-making and planning by up-, mid-, and downstream energy companies.
Many energy enterprises embrace new technologies and renewable sources to increase their competitive advantage and satisfy consumers, often transforming their organizational structures and creating new business models4. For example, 85 percent of the utilities in the Forbes Global 2000 will, by 2019, have established new business entities to accelerate their innovation and business transformation. Worldwide, solar is the most rapidly growing, “new” distributed energy; it necessitates new deployments and large expansions of distributed energy management systems.
Cloud adoption in the industry has been slower than elsewhere, but is accelerating as companies incorporate IoT data streams into their operations and take advantage of economical, scalable cloud storage and analytics resources in the cloud5. Asset and grid monitoring and management, enhanced by cloud-based business intelligence, are particularly high-value use cases for energy companies to continue migrating to the cloud and taking advantage of this new technology.
Blockchain is another technology that holds great promise for the industry. In the U.K., industry observers expect that established energy companies as well as new, non-traditional competitors will use blockchain to streamline energy distribution6. Analysts expect that 50 percent of the gas and electric utilities exploring blockchain today will move to production use of the technology in areas like peer-to-peer energy exchange7. With relatively wide adoption across a variety of use cases, the IoT continues to improve data-driven insights and decision making as energy companies manage their explorative and extraction assets, production machinery, refining facilities, delivery systems, fleets and demand strategies8.
The trends mentioned may impact upstream, midstream and downstream energy companies differently, but they do have one thing in common: The need for business analytics to provide more insights to organizations to make strategic decisions and manage operations for the most desirable outcomes. Companies in all three industry sectors generate, manage and use large masses of data, although the data types and the specific business requirements that necessitate advanced analytics are specific to each segment.
The business activities of energy companies often include critical elements that are too complex for individuals or teams to manage effectively without the decision-making and planning assistance that analytics can offer:
As margins shrink and industry changes augment existing risks, energy companies need to find better ways to assess the returns from their investments in energy exploration, production, skills and industrial equipment to ensure the health of their businesses. The IoT—with critical enablement from modern cloud technology—is proving to be one of the most interesting and successful ways to achieve this. For example, predictive maintenance and asset management facilitated by IoT data analytics, and augmented by machine learning, is one of the most common and promising use cases of energy companies using new technology to gain better control and intelligence. Let’s take a closer look at IoT in the energy industry
Most prominently, IoT data and IoT-based insight are essential in modern asset performance management. This data is expected to be practiced in 75 percent of utilities in 2019, resulting in an improvement of up to 10 percent in operational performance9. Many upstream mining and extraction companies or oil and gas producers have also connected their industrial assets to the IoT. They monitor the performance of machinery at a plant level to meet organizational KPIs and ensure the optimal productivity of their machines. Based on IoT data and benchmarks, they finetune equipment or perform predictive maintenance to avoid unplanned downtimes.
In some production processes, targeted performance levels, output and operational continuity may depend on the flawless functioning of one particular business critical machine or even just a small handful of machine components. For that reason, IoT analytics may be focused closely on that machine and its components to provide production and maintenance managers with the information needed to keep their key asset operating at peak levels without breakdowns. Plant managers, on the other hand, may need to receive meaningful data at a less granular level, so they can make timely, smart decisions regarding the productivity and profitability of large facilities with their collective assets.
Midstream operators of liquified natural gas plants, pipelines and storage as well as providers of multimodal transportation can bring both their moving and stationary industrial assets into the IoT. When it comes to marine and land-based fleets and specialty vehicles, IoT data keeps managers aware of the performance, payloads and locations of these assets. By reviewing IoT data analytically, distribution managers remain informed on potential deterioration in the integrity and workloads of pipelines.
In addition to enabling predictive maintenance, IoT-based analytics help midstream companies plan more efficient transportation, especially when transitions between pipelines and vehicles—or other steps from one mode of transportation to another—have to be performed smoothly. IoT analytics, sometimes in combination with demand data from the history of the business, also make it possible for companies to forecast transportation needs with greater accuracy; reduce the costs of operating and maintaining plants, storage facilities, pipelines, and fleets; and more closely meet consumer demand.
In the downstream realm of consumer distribution, utilities, power generation, oil and gas refining, and wind and solar power generation, IoT data analytics have yet more unique, valuable applications. Utilities are adopting IoT solutions at an increasing pace, leading analysts to expect over a 20 percent increase in this market segment’s IoT adoption by 202510. IoT scenarios in this sector of the energy industry once again include predictive maintenance and asset performance management. In addition, IoT analytics help utilities ensure the reliability of their grids and perform more reliable and profitable demand planning.
For utilities engaging in demand-response programs, IoT-connected, smart metering devices in homes and businesses help consumers obtain power at the most advantageous rates and reduce energy waste11. Enhanced by advanced analytics and machine learning, they also help utilities deliver electrical power in a predictably profitable manner and avoid losses from excess production of solar or wind power. Utilities can combine their power generation and demand forecasts and analytics with the insight they can glean from data sets about consumers’ actual preferences and behaviors.
The potential benefit of the IoT in maintaining compliance with environmental, quality, financial, consumer protection and other regulation is significant, but most companies have yet to make significant inroads with this use case. Gathering and analyzing real-time IoT data from extraction, production, distribution and consumption in the context of financial, environmental, market and other data sets makes it easier for companies to comply with regulatory mandates and corporate standards for the quality and consistency of the production output. It also ensures the environmentally sound and safe operation of plants, facilities, pipelines, fleets and distribution infrastructures.
Incorporating IoT use cases into their operations greatly increases the masses of potentially valuable data that energy companies must collect, store, analyze and safeguard. The cloud is essential in facilitating their data storage and management. As is the case when companies generate large volumes of geo-chemical data while evaluating the potential of new oil and gas reservoirs, analytics become invaluable when there is so much information that humans would never be able to review and assess it in a non-biased, consistent manner— and do so quickly
For downstream energy companies, which often serve millions of commercial and residential consumers, analytics can make the difference between meeting demand and satisfying customer expectations in a profitable, sustainable manner—or not. Downstream providers need to do more than accurately forecast consumer needs and deliver energy. They also need to address consumers’ preferences for carbon-alternative energy sources like solar and wind power, along with their expectations of increased transparency and better service quality.
As consumers better understand their choices and become more educated regarding energy and electricity issues, energy providers must take steps to ensure customer retention by innovating their consumer engagement models, departing from the institutional behaviors of the past12. Energy companies can use customer and market analytics to achieve strategic intelligence and foresight. Already, gas and electricity suppliers are establishing large budgets to improve the customer experience, dedicating half of the IT portion of these funds to personalized services, digital channels and product marketplaces13. The result could be a two-point improvement in customer effort scores—a significant number of positive influencers when you serve a population of millions.
For years, business and technology leaders in the energy industry have looked for ways to translate data assets into business advantages by means of advanced analytics. Such efforts typically required companies to plan and implement state-of-the-art data infrastructures and processes for data management and protection. The maturation of cloud technology has significantly reduced the costs and labor of establishing these foundations. Digital initiatives in energy businesses have become more strategic as technologies evolve and intelligence-driven business transformation helps energy companies address operational, competitive and regulatory challenges.
Analytics can help an energy company gain a competitive advantage and outperform the field when it comes to the production, processing, generation, transportation and distribution of energy and its raw materials. For example, in the realm of utilities, analysts estimate that advanced analytics can increase profitability by five to 10 percent, boost customer satisfaction, and improve employee health and safety14.
The upstream companies that locate, assess and exploit oil and gas reservoirs have historically relied on analytics to assess the business value of tapping into supplies in challenging locations and feeding them into the energy supply networks. More recently, these companies have been exploring harsh environments like the deep arctic sea and—as established wells approach the end of their productivity—have been investigating the potential of reservoirs that previously were deemed either too large or impractical for extraction.
Companies are using advanced analytics and digital modeling in the cloud, sometimes combining their own data findings with geo-physical and seismic data from scientific institutions, to better understand the potential of an untapped reservoir in a challenging location. Many have embraced unconventional drilling as an alternative or in combination with more traditional extraction methods. Geo-physical analytics are critical in reducing the risk and complexity, and accelerating the progress of unconventional extraction.
More recently, upstream companies have given their remote scientists and industry experts access to cloudbased collaboration to assess the outcomes and potential of oil and gas explorations and drill sample collections. Instead of spending time traveling, experts can access shared analytical tools and models to discuss their insights with their colleagues in other business groups. In some companies, augmented reality (AR) and virtual reality (VR) tools on cloud platforms expand the power of data visualizations, provide better substantiation for data-driven collaboration, and make it possible to arrive at sound decisions in less time and with minimal ambiguity
Analytics complemented by AR and VR also help energy companies attract talented professionals who value working environments with modern digital technology, constant mobile access, and cloud-based capabilities to help them grow. Since generational change across the industry may remove many experienced workers with decades of experience—ranging from technicians servicing specialized equipment to scientists analyzing drilling samples—this is a business-critical effort.
Some organizations use analytics, often supported by creative machine-learning applications, to circumvent the challenges of talent recruitment and retention and bring greater intelligence to their planning, decision-making and operations management. Today, machine learning helps energy companies anticipate market demand as they operate more efficient and profitable power distribution systems15 ; ensure predictive, nondisruptive maintenance of industrial assets; achieve the best possible yields from nonconventional drilling and extremely remote reservoirs; and more.
More and more energy companies rely on advanced customer and market analytics and sophisticated forecasting to expand beyond their traditional horizons. This allows them to not only satisfy customers’ increased expectations for more choices, but also improve transparency and sustainable business practices. Doing so also helps them stave off challenges from new and unconventional entrants in the energy market16. Analytics enabled innovations like energy-planning services for enterprise customers demand effective management, storage and protection of large data volumes.
The analytics scenarios in customer-centric innovation, IoT initiatives, machine learning and digital collaborations force companies to find ways to bring their numerous sources of business data—like scientific, market, financial and operational data —together, make them accessible, and reveal their meaning. For many organizations, that still proves to be challenging, especially when they lack the expertise to make business sense of evolving cloud technologies.
While segments of the energy sector, including some upstream innovators and many customer-focused downstream distributors, have made great strides with advanced analytics and digital innovation, adoption across the industry is uneven. Packaged vendor solutions for operational aspects like IoT-enabled maintenance or machine learning in production management may fit the business conditions of energy companies, but they can be hard to integrate with existing systems and processes.
Since energy companies generate masses of data every minute they operate, implementing analytics and machine learning at the appropriate level of scalability and power to enable real-time decisions can be difficult. Yet, it requires timely action based on the most current data findings to improve the performance of machinery, plants and distribution networks, or to prevent unwanted downtimes of machinery and specialty vehicles.
CIOs and technology teams in energy companies only rarely rely on proven, available approaches and solutions to analytical and data-management challenges. Often, they need to create their own comprehensive business insight strategy and tools that support the use cases for analytics across the entire operation – devising custom solutions that can connect analytics, IoT, machine learning and other technologies in the cloud.
Seismic and geological data, explorative and production data, consumer demand patterns, business financials, market trends, global economic indicators—these are just some of the diverse types of data that matter to energy companies. Data ownership, gathering, creation and storage are often tied to distinct business groups and systems, and many companies lack a consolidated way to access, manage and analyze it. The understanding of newer technologies – like machine learning or IoT – or how best to integrate them, may be more advanced in one area of the business than another. Analytics experts and their stakeholders may not always be aware of the shortcomings and opportunities of the business intelligence tools and approaches within their domains.
Under these conditions, it can become difficult to perform strategically sound data analysis and gain the full value of business data. Understanding this limitation, some companies focus on areas of the business where the potential benefits are obvious, where there is maybe just one main data source involved, and where analytical requirements can be easily defined. That could be, for example, predictive maintenance based on IoT data traveling through the cloud, which then becomes a sort of proof-of-concept for the business value of real-time analytics. However, these companies may still find it overwhelming to benefit from machine learning or align predictive maintenance with other business activities without drawing on outside expertise.
Bringing advanced analytics supported by IoT data and machine learning into an energy company may require modernizations in the entire business infrastructure, as well as the interfaces and solutions used by the individual contributors. Data analytics, management, storage and protection need to balance intelligence and access requirements with optimal performance and reliability
Many companies are hard-pressed to provide their various business roles with the valuable insight and analytical tools that let them work autonomously. When drilling down to the proper level of granularity in any area of the business is not possible—or only is with assistance from a technologist—employee productivity may suffer. For example, without being able to review the details and history of a work order, it may be difficult to connect work orders to capital projects or control costs and investments. It can also be confusing to determine whether a given data resource or analytical application should be in the cloud or on-premises, and how to elevate business analytics to an organization-wide culture instead of letting it languish as an individual practice.
Given the complex data flows and data lineages in the energy industry, data-management skills are in consistently high demand— yet very few qualified data managers with an understanding of the energy sector seem to be available. The unique business conditions of upstream, midstream and downstream energy companies require a relatively high level of specialization from IT, data scientists and business analysts. Many companies are reluctant to invest in consulting expertise without the assurance that doing so will help them reach the outcomes they look for more effectively.
Energy companies cannot let their competitors gain the advantage or leave their customers’ expectations unmet while they grow into the cloud, evolve their analytical and data management disciplines, and integrate IoT and machine learning into their business intelligence initiatives. They need to chart a course that delivers the insight and decision-making enablement they require sooner rather than later.
For the largest enterprises, this may mean major investments in talent, technology and consulting expertise. For others, it may entail less radical tactics: purchasing generic solutions—for instance, for proactive maintenance—or exploring more generalized yet intuitive products like Microsoft Power BI, perhaps in conjunction with specialized industry software. However, either approach can reveal shortcomings. A sizeable investment cannot guarantee the best fit of technology and strategy or ensure the outcomes a company seeks. Relying on standardized solutions may result in delayed goals or functional gaps that would either need to be bridged in a later phase of the initial project, or by the tenacity and resourcefulness of technology users.
As an alternative, an energy company could draw on the industry and technical expertise of a consultancy that is deeply familiar with the energy industry. Doing so can help the energy company articulate its business intelligence strategy and realize it by using proven, established solutions together with custom-created technology to fit its specific needs. This integrated approach is what makes AgileThought unique and valuable. The company’s energy industry team is familiar with the events and trends in the sector. Contributors with decades of experience in working with the largest and most innovative energy companies collaborate with technologists and developers using fast-moving, results-driven processes and practices to help clients meet their goals.
AgileThought draws on tools like Microsoft Power BI and other analytics resources on Azure cloud to keep technology environments and deployments simple and to enable productive, autonomous user experiences. However, the analytics practice is not attached to any platform or technology provider—that choice depends on the intelligence requirements of any given energy company.
The company’s organizational skills are both broad and deep: For example, AgileThought’s experts have advanced accounting knowledge to help companies set up their cost center ledgers, which enables them to transfer transactions and work orders among call centers. The company’s experts also understand how to assist energy companies in tracking data lineages for Federal Energy Regulatory Commission (FERC) reporting.
AgileThought’s integration experts help connect multiple IoT data sources and machine-learning tools, enabling companies to overcome the limitations of human judgment and attention when managing industrial assets across enterprises or in other complex scenarios. But, to help people in various business roles make better decisions, AgileThought can also build individual, optimized dashboards that connect to the relevant data streams and make information actionable. AgileThought’s data scientists help furnish energy companies with predictive analytics, which help synthesize and extract findings from all pertinent data sources in a data lake or data mart.
Some companies explore predictive analytics not just by gathering and analyzing IoT data findings, but also by means of synthetic sensors created from operational data. There are many use cases for predictive analytics with such synthetic sensors: improving production and yield; mitigating environmental and safety risks; and meeting OSHA, Clean Air Act, and other regulatory mandates. One could also combine meteorological, geologic and seismic data with equipment performance and forecasting analytics; doing so helps generate and store solar and wind power that meets market needs, avoids production issues and prevents power generation shortages.
Energy companies have many possible avenues to the cloud. The sheer number of proven architectures, solutions and methodologies—let alone emerging and promising cloud technologies—often makes it difficult for CIOs and IT teams to plan analytics strategies that suit their organizations. In working with diverse energy companies, AgileThought has established best practices for data and process optimization to prepare for low-risk, results-driven cloud migrations. The AgileThought cloud and energy industry teams know how to take advantage of typical patterns and keep cloud projects efficient, but are also highly adept at designing cloud environments for company-specific requirements.
Energy companies and industry observers have long known that technologies like artificial intelligence and machine learning can help modernize distribution grids to improve efficiency, boost reliability and reduce consumers’ costs17. But accomplishing those results, just like implementing dependable demand-response programs18, requires the best of what today’s data analytics can offer.
AgileThought’s data scientists, developers and technologists leverage specialized technical skills and deep industry expertise to help energy companies generate results from predictive analytics. The organization’s history includes many successful engagements with well-known clients, including some of the largest Fortune 500 companies in industries that have high innovation potential, are subject to far-reaching regulatory mandates, and experience complex security requirements.
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