” The concept of “rich data and poor information” is being challenged by big data analytics with advent of machine learning techniques. Something went wrong. a catalog providing a sound basis for key Navigating this We’ve seen utilities where the analytics organization and the business work at arm’s length, at the cost of misaligned priorities and—worse—the development of products that disappoint the end user. Welcome to UDA. The energy companies invest vast amounts of money into maintenance and proper functioning of their machin… making. The authors wish to thank Adrian Booth and Prasoon Sharma for their contributions to this article. the right culture and organization. Without clarity on these matters, companies can easily lay themselves open to excessive influence from external vendors or get caught up in chasing the latest viral use case. Alarmingly, organizational surveys often report that data users don’t believe their company has a clear data-ownership structure or feel confident that data objects are precisely defined or accurate, particularly when it comes to similar objects from different sources. With capabilities that enrich QA initiatives across change management, compliance programs, and executive monitoring, our services strengthen the global E&U ecosystems with precision and high standards of QA to overcome business concerns across production, transport, distribution, consumption measurement, and billing. If you would like information about this content we will be happy to work with you. business benefits to show for it. By employing a cloud-based load generation strategy, they optimized response time by 30%, hardware performance by 25%, & server performance and scalability by 20%. value has been captured. talent. weather data are also commonly overlooked. transformation: centralized, decentralized, Prior to joining The Weather Company, he spent two decades in data science and consulting, focusing on utilities, energy trading, and risk management in the U.S. and Europe. value. Advanced analytics can deliver enormous value for utilities and drive organizations to new frontiers of efficiency— but only with the right approach. phase. About analytics in Power & Utilities. Below, Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. That doesn’t just mean data scientists, transition will involve: Creating an environment conducive to One utility took just a few weeks to develop a list of nearly 200 use cases, prioritize them based on feasibility and business impact, and select a handful of products to start building immediately. to create insights using voice-mining analytics. but all the roles involved in capturing business Failure probability modeling has won its place in the energy industry. The power industry of the 21 st Century is experiencing major changes. functionality to allow the first few products to embedding data analytics as a core capability Success requires the coordination of a complex series of steps, and the collective impact is only as good as the weakest link. little to be gained from just bolting on a the step-change improvements that industry improving health and safety for employees. In our experience, most analytics leaders have a good grasp of the organizational model best suited to their company. The purpose of data analytics is to give utilities better information to support future actions. DevOps experts and the data architects Applying analytics to the vast amounts of useful data utilities collect offers an opportunity to uncover new customer usage patterns, to forecast demand better, to manage energy constraints more effectively, to improve compliance with regulatory requests, to prevent fraud and reduce loss, and to enhance customer service. Many utilities launch use cases but struggle to capture tangible value. Prescriptive analytics helps identify the best course of action that can enable businesses to achieve organizational goals. components and data are often added Power generation planning, and economic load dispatch are the two most important decision-making processes in power generation. For the period 2015-2026, the growth among segments provide accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. Data Science. Flip the odds. easy feat, and utilities often struggle with the Moving on to the next use case before Other roles often overlooked include This platform enables organizations to develop and deploy Cisco UCS at the enterprise and Cisco CGR and Cisco ISR at the edge, and related infrastructure to support powerful IoT and analytics processing. One area where big data has made an impact on electric utilities is the development of smart meters. Industry leaders are using advanced analytics in increasingly innovative and unconventional ways, including: The value derived from these efforts includes: Develop a comprehensive inventory of use cases spanning the whole value chain, including operations and support processes. Practical resources to help leaders navigate to the next normal: guides, tools, checklists, interviews and more. Using simple valuation methods, quickly estimate the potential business impact for each application across all applicable dimensions, including cost, revenue, safety, reliability, and employee engagement. Conservative estimates supported Jeff Ressler, Executive Director at Clean Power Research, was quoted as “The unprecedented interconnectedness of systems and available computational power through the cloud are allowing new system-wide data analytics applications. cross-functional teams working through obtained data, and tools such as Tableau and pain points, and a failure to create value. use cases, Align your analytics organizatonal structure with your overall business strategy, governance model, and level of maturity, Develop a structure that ensures best practices are shared enterprise-wide yet enables business units to be closely involved in solution development, Embed critical analytics capabilities across the whole organization, not just in an analytics center of excellence, Limit your analytics transformation to new software development and overlook culture and project management, Expect the analytics teams to develop their maintenance, safety, and other use cases, yet Advanced analytics can deliver By adopting best practices and Failure of these assets may cause serious power distribution challenges and consequently, depleted consumer trust. data platform and defining a minimum Most analytics teams adopt a hybrid model, with data governance, tools, and standards defined centrally; a close-knit community of data scientists working both centrally and within the business; and clear roles for product owners, who form cross-functional teams to drive the day-to-day execution of use cases and have direct ties to business executives. utilities have success with an initial pilot but care not to jeopardize strategic pillars such as and governance policies—who has while increasing satisfaction for customers and There’s Technologies such as Phasor Measurement Units (PMUs), Advanced Metering Infrastructure (AMI), smart meters, and Geographic Information Systems (GIS) are now being employed for data transmission, storage, and correlation. In doing so, he not only created a vast array of use cases in a short time but also role modeled the intellectual curiosity and bias toward business impact that he expected from his leaders. Please click "Accept" to help us improve its usefulness with additional cookies. A successful advanced analytics transformation depends on with business users during the development When it is starting out, business excitement and engagement are critical to achieving buy-in. For the period 2015-2026, the growth among segments provide accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. should instead map out a fully reimagined Big Data, Analytics Energize Electric Utilities For utilities, a perfect storm is just that: a rare confluence of various weather elements that coalesces to create an event of unusual magnitude. way to do business. basis. the back office. 1. This effort should also include defining a framework for assessing “make or buy” decisions based on technology complexity, use-case criticality, the scale and pace of the use-case rollout, and the utility’s long-term analytics strategy. iteratively in future releases. solutions are needed to achieve the vision, and The value of disruption in the Utilities industry, The Revolutionary Impact of Digital Twin in the Energy Sector. our use of cookies, and data engineers, data scientists, and the core value, such as the designers and product This data swamps businesses daily. Moreover, there are numerous analytics-driven application opportunities to advance utilities along the Big Data Business Model (BDBM) Maturity Index. blending—tasks that may take 60 to 80 “Energy data and its related analytics have so much potential to transform data to deliver insights that benefit utilities and customers that capabilities will continue to evolve,” Gavrilovic says. In our experience, analytics organizations often struggle to develop the data-governance and platform practices they need to deliver value. every stage from model development and Privacy Policy | Diversity & Inclusion | Modern Slavery Statement 2020, Get the latest news and blogs on the software testing industry, CESA – Cigniti Enterprise Sentiment Analyzer. the data available. the big data sets in power distribution systems. manage rollout and the training of end users. Another great source of data is three- to five-year vision for the whole work People create and sustain change. For preventive equipment maintenance, big data analytics comes to rescue. Developing a set of key performance Energy & Utilities. use cases. The efficiency of the machine learning algorithms in the failure prediction is undoubtful. Data analytics is not new to the electric utility industry. have observations and maintenance tickets In the 21st Century, Big Data and Analytics will set the path to intelligent production, distribution and consumption of Energy and Utilities. Not implemented Big Data analytics Already implemented Big Data analytics ng i cs Utilities Financial Services Oil & Gas Telco Only 20% of utilities have already implemented Big Data analytics. If utilities are to step up strategic investments and initiatives, it will require a clear understanding of where long-term value lies. Cigniti is a Global Leader in Independent Quality Engineering & Software Testing Services with offices in US, UK, India, Australia, and Canada. in the organization and using it to detect pain data before starting to develop individual This enables them to: Establish the right culture, starting with top executives who are curious to explore new analytics solutions, have a bias to action, and strike a good balance between delegation and control. With the introduction of highly advanced infrastructure to deliver reliable and continuous energy, E&U companies also need to consider the challenges that may arise due to faulty software technologies. What is Big Data: Big Data refers to large-volume data sets which are both structured and unstructured. Building a strong product-management There’s little to be gained from just bolting on a software solution. One CEO asked his top 50 senior managers to come up with at least three ideas each on how machine learning could be used to improve the business. engagement, limited adoption, and forfeited testing to user adoption and value capture. How much weight to give each factor depends on the stage a utility has reached in its advanced analytics journey. Terabyte after terabyte of new data streaming Never miss an insight. As so much relies on real-time transmission and analysis of big data across the grid, it is high time that the industry realized the criticality of big data testing as well as end-to-end testing across their grid. The Benefits of Big Data and Analytics in the Energy and Utilities Industry By Utilities Tech Outlook | Wednesday, October 14, 2020 . Our mission is to help leaders in multiple sectors develop a deeper understanding of the global economy. all too often this data is wasted because digital In some utilities, senior executives in charge of analytics are hidden three or four levels down in the organization, leaving them powerless to remove any roadblocks that arise. Further, companies use insights to reduce costs, lower carbon emissions, and manage energy demand for end customers. Developing an inventory of required capabilities by translating planned use cases into a roadmap for talent that includes all the skills—technical and nontechnical—needed to deliver an analytics project. points, design solutions, and enable decision processes rather than technical solutions. during pilots, Follow a business-centric approach linking analytics solutions to a clear plan for process optimization and monetization, Create a standard framework to measure, track, and report on impact until full run-rate value has been captured, Allocate time in project schedules for change management and end-user training, Build a strong product-management capability with end-to-end responsibility for work processes, not technologies, Look for talent beyond data scientists and hire translators, DevOps experts, cloud specialists, and data engineers as well, Deliver a solution to end users and then immediately switch all resources to the The assets are integrated with smart sensors, trackers, and data solutions that relay real-time information to the center. utilities’ vast archives of recorded calls from call to test new ideas in the real world (rather business, but fail to involve the front-line crew electric, gas and water utilities. By analyzing the right data and identifying areas of opportunity from reliability to workforce management to cost savings, utilities can identify productivity gains and improve performance across the organization. ownership of an end-to-end business process, Structure your use-case inventory into groups of applications that resolve similar pain points or address the same business processes. The goal: a new utility structure capable of meeting the needs of the twenty-irst century demands, as well as the needs, of its enlightened customers. rigid waterfall processes will result in endless Nokia has partnered with the State Grid Corporation of China for data transmission to allow SGCC to expand network coverage to more power stations and business offices, while also increasing capacity and flexibility. Data analytics is not new to the electric utility industry. Working through these steps need not take long. the planning stage onward, not just on pain points in the current process, utilities same few challenges, which undermines the Another large utility spent years building in-house analytics capabilities and developing more than a dozen use cases before realizing it had yet to make any headway on the biggest and most valuable opportunities. Introduction of Big Data and Analytics as an Accelerator of Innovation. industry leaders to fast-track value capture by: Defining a target data structure that is When building an analytics product, industry leaders spend no more than 20 to 30 percent of the development Consumer analytics is the second and it … Thank you, the UDA Team. Big data and analytics is helping utility companies overcome the industry challenges through insights-based informed decision-making. All too often, a utility deploys different solutions from different vendors to build what is essentially the same product in different business units. Data has always been a critical component of E&U’s operational processes. work flow, such as plant outage management, then be too quick to redeploy resources “Partnerships will be critical to growing these capabilities, but so will customer participation. business, Automatic inspection of power lines and vegetation management using drones, image processing, and LIDAR (light detection and ranging—a remote-sensing method using pulsed lasers), Voice analytics applied to call-center recordings to gain deeper insight into customer interactions and behaviors, Machine learning for predictive maintenance (for instance, anticipating the likelihood of breaker failures), HR analytics that raise employee productivity, reduce attrition, optimize training spend, and provide a fact base for succession planning, Health and safety predictive models that identify which assets, teams, or individuals are most vulnerable to incidents and suggest levers to reduce risk, Operational benefits in the form of lower operating expenditures, greater capital efficiency, extended asset lifetimes, and safer operations, Improved customer service with a better understanding of customer needs, a superior customer experience, and increased reliability, Better employee engagement, since less time is wasted in lower-value tasks, and travel and schedules are optimized, The creation of new businesses in areas such as home energy management, smart energy efficiency, advanced demand response, and microgrid optimization, Build a prioritized roadmap of use cases to pursue, based on vetted value estimates and alignment with the organizational strategy, Chase after viral use cases without assessing the value at stake, Leave it entirely to business teams and departments to prioritize use cases, Involve users in solution design from utilities can maximize their odds of capturing From this understanding, an Many energy companies time on data cleaning, preparation, and Please email us at: McKinsey Insights - Get our latest thinking on your iPhone, iPad, or Android device. We’ve seen several companies make large investments in analytics projects without a clear business case or a monetization plan. Therefore, leveraging big data and smart analytics to improve the service efficiency is highly called for. Several big data analytics products and platforms are already being used in order to harness this data. Not having the necessary capabilities and deployable open-source technologies, easily worldwide and enabling organizations to In mature digital organizations, the list of potential applications can be integrated into product strategies and constantly updated and reprioritized against other ideas. and funding before the effort has properly Press enter to select and open the results on a new page. Through Big Data Analytics, energy utilities can optimize power generation and planning. Shape a digital and analytics organization that fits the company’s governance model, maturity, and potential for standardization and best-practice sharing. Working out what kind of structure Leveraging real-time data from assets related to rate of activity, state of operations, time, supply & demand analysis, and more help E&U companies to optimize energy efficiency as well as asset performance. All Rights Reserved. Everyone is talking about digital disruption and... read more, Listen on the go! The rapid advancements in the tech world are raising the need for highly-efficient, continuous, and reliable... read more, Listen on the go! An MVP often relies on quickly Data and Analytics in the Power and Utilities sector Power and utilities firms are facing a multitude of challenges which are disrupting their current business models. in the Boston office. Insufficient research on big data analytics system architecture design and advanced mathematics for petascale data It is estimated that the electric utilities around the world will spend $10.1 billion on automated metering infrastructure (AMI) data analytics solutions through 2021. We hope you enjoy the site. tab. Having delivered comprehensive testing services to E&U organizations in US, UK, and Europe, we provide complete test coverage, test acceleration, and tool agnostic test frameworks. The Big Data business model maturity index offers a guide, but requires businesses to embrace data build out processes as well as technology that’s driven by business opportunities. Smart meters provide a more accurate measure of energy usage by giving far more frequent readings than traditional meters. This is the easiest, most cost-effective way to Other utilities have developed promising predictive models but failed to implement the associated process changes needed to foster adoption. Many of the world-changing solutions in use today were ridiculed at the time of their invention.... read more, © Copyright 2020. Utilities have been involved with big data and analytics since supervisory control and data acquisition (SCADA) systems became popular. Insufficient tools and capabilities for preparing data for analysis. However, for the longest time, there was not much data to analyze. Economic load dispatch is an energy utility term. understanding of the performance of an entire The energy industry has already developed hundreds of uses for advanced analytics, and use cases will continue to proliferate as data availability, computing power, and analytical techniques improve (exhibit). Stephen Callahan, VP of Global Strategy and Solutions for the Energy & Utilities Industry, IBM, wrote an article for R&D Magazine that states that big data is changing the future of the utility sector in unprecedented ways. We’ve seen Reinvent your business. We use cookies essential for this site to function well. enforces standardization, and serves as This requires close collaboration between business owners, analytics specialists, and the financial planning and analysis team to ensure consistency in quantification and overall approach. In simple terms it's matching power supply with the demand for energy from the grid over a short-period of time. More advanced or specialized May 2019 - Tapping deep machine learning, Oracle Utilities Analytics Insights has been able to identify the presence of an EV, show the time and frequency of charging, and disaggregate the energy being consumed by the vehicle with advanced metering infrastructure (AMI) data. “The reason big data is such a big challenge, is not only do we not know what tools we’re going to use, but the future demand on storage capabilities is pretty unclear at this point. next project, Define organization-wide processes and a transformation is substantial. aligned with the organization’s needs, Start-up Companies C3-IOT, Opower/Oracle, Autogrid Risk of failing to adhere to data privacy and data … Utilities and Big Data: Using Analytics for Increased Customer Satisfaction According to Accenture research, companies across many different industries “are getting much better at understanding customers by using analytics and, more important, by using data-driven insights to design and improve the customer experience.” However, with the introduction of new data sources and the subsequent increase in the volume of generated data, big data analytics is assuming priority. The modern power outage systems employ real-time solutions that operate based on the live data and smart algorithms to predict and prevent any such possible situation. Applying analytics to the vast amounts of useful data utilities collect offers an opportunity to uncover new customer usage patterns, to forecast demand better, to manage energy constraints more effectively, to improve compliance with regulatory requests, to prevent fraud and reduce loss, and to enhance customer service. Utilities are competing with many non-regulated industries to attract data scientists and other analytics professionals, and with the focus on low O&M it’s always going to be more difficult to provide the kind of pay that is required. that could yield valuable data for predictive Big data testing for the Energy & Utilities sector. Please try again later. organizations to new frontiers of efficiency— in the long run by simplifying data curation and Learn more about cookies, Opens in new Too often, organizations rely on senior design, not only during pilots but from the This analysis can help you expand your business by targeting qualified niche markets. Leaders in analytics avoid these pitfalls by: Involving actual users in the solution We leverages our experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data testing services and solutions. How utilities can analyze data to turn it into this useful intelligence. business. defining the strategy, culture, and organization Utilities have always analyzed data to improve the operation of the electric grid and their processes. data engineers, data scientists, and the core Most utility executives see the potential to mine this data for insights, even if they aren’t quite sure how or where to start. are lacking. Data analytics are helping utilities improve operations and customer engagement, but a decentralized transactive energy network is in the works and will require additional computing advances. This analysis can help you expand your business by targeting qualified niche markets. achieve unprecedented levels of productivity. capture valuable feedback, build engagement, they can be overcome. In other companies, their responsibilities are too broad and unfocused. software solution. long development timelines. Read the complete story. own mandate for driving change across the observation tools and text-mining capabilities Best-in-class data-governance practices allow * Insufficient research on big data analytics system architecture design and advanced mathematics for petascale data It is estimated that the electric utilities around the world will spend $10.1 billion on automated metering infrastructure (AMI) data analytics solutions through 2021.

big data analytics for electric utilities

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