Business Strategy: IDC MaturityScape Benchmark Big Data and Analytics in Utilities in North America

Transcription

1 BUSINESS STRATEGY Business Strategy: IDC MaturityScape Benchmark Big Data and Analytics in Utilities in North America Robert Eastman Greg Girard Dan Vesset IDC ENERGY INSIGHTS OPINION Big data and analytics (BDA) is on the rise as a focus for many utility executives. The utility industry has used big data and analytics (and business intelligence) to understand and manage such things as financial metrics, demand forecasts, energy markets, optimization of generation, outages, operational efficiency, and customer service. However, given the number of other issues competing for the attention of utilities, many utilities are still in the early stages of understanding the full potential for analytics, what an analytics strategy entails, and what big data and analytics can deliver. This study presents benchmark data on the maturity of BDA capabilities of North American utilities, identifies the key capabilities that distinguish utilities whose BDA efforts have met or exceeded their overall expectations from their competitors whose BDA efforts have fallen short, and offers guidance for achieving BDA success. The key findings of IDC Energy Insights' research conducted with utilities are: Utilities are still in the early stages of deploying big data and analytics, as reflected in the distribution of utilities across IDC's BDA MaturityScape maturity stages. More than four times as many utilities are in the lowest two stages of maturity ("ad hoc" and "opportunistic") as in the highest two stages ("managed" and "optimized"), with nearly two-thirds of utilities in the middle stage ("repeatable"). Achieving BDA maturity is a multipronged effort across five core dimensions: intent, data, technology, process, and people. Success depends on maturity in each dimension and on an alignment in maturity across the five dimensions. As more utilities inevitably undertake BDA initiatives, the positive correlation between BDA maturity and successful outcomes of BDA initiatives should become dramatically clearer, as we see in other industries. On average, higher levels of BDA maturity lead to better chances of achieving expected or greater-than-expected benefits. The top 10 traits that most distinguish high-achieving BDA utilities span the five critical dimensions to include enterprise-level funding and justification; high-quality data; integrating multiple data types; scalability, reliability, and other capabilities; structured reporting and multidimensional analytical technologies; discipline around strategy and planning and other processes; retaining and developing critical skills; and culture of collaboration. March 2014, IDC Energy Insights #EI247404

2 IN THIS STUDY This study presents IDC's 2013 Big Data and Analytics Maturity Benchmark Survey results for utilities. This study should be viewed as a companion to IDC MaturityScape: Big Data and Analytics A Guide to Unlocking Information Assets (IDC #239771, March 2013). In that study, we described IDC's BDA MaturityScape framework that identifies the stages, critical measures, outcomes, and actions required for companies to effectively develop BDA competency. In addition, this study should be used as a complement to IDC MaturityScape Benchmark: Big Data and Analytics in North America (IDC #245197, December 2013), which presents an overall analysis of the survey results for seven industry verticals taken together. The current study presents research results that serve as basis for utilities in assessing their BDA maturity against market and industry benchmarks; use the baseline to define short- and long-term goals and plan for improvements; prioritize BDA technology, staffing, and other related investment decisions; and uncover maturity gaps among business units and between business and IT groups all in the quest to improve or optimize decision making. IDC Energy Insights' BDA MaturityScape benchmark will allow utilities using this methodology to answer the following three questions: How mature is our organization in its ability to utilize BDA capabilities? What is the value of investing in improving BDA maturity? What are the most common practices among high achievers that achieve higher-thanexpected value from BDA projects? SITUATION OVERVIEW Widely publicized case studies and academic and commercial research highlight the profitability and productivity advantage gained by organizations that increasingly rely on data-driven decision making. Big data and analytics have become a top agenda focus for a growing number of executives and are becoming a bigger focus for the utility industry. There is, at the same time, an abundance of hype about big data technology capabilities and inflated promises of outcome. This obscures the real challenges that companies face in deploying and leveraging analytics. As the utility industry moves toward a smarter grid, the industry is grappling with large data volumes and looking at growing volumes of new data, and types of data, from more devices and sources. Analytics has, therefore, been a hot topic for utilities for at least a couple of years and will be increasing in focus for the foreseeable future. BDA capabilities represent the complex interplay of technology, data, processes, and people looking for insight to propel their organization toward their strategic, operational, and tactical goals wherever they may be sought (e.g., operational efficiencies, production optimization, asset integrity, and fraud and theft detection) IDC Energy Insights #EI

3 To analyze the complexity of managing BDA programs, IDC's BDA MaturityScape examines four capabilities data, technology, processes, and people plus intent, a superset of strategy inclusive of justification and budgeting practices. The model assesses each capability against five stages of maturity. While it is a cross-industry model, IDC's BDA MaturityScape's measurements of maturity, when supplemented with IDC Energy Insights analysis, provide a picture of BDA maturity in utilities and, more importantly, identify some insights for higher-performance big data and analytics programs. Big Data and Analytics Maturity in Utilities Today IDC decomposes each of the five dimensions of BDA maturity (data, technology, process, people, and intent) into a set of supporting capabilities. Specific characteristics of these supporting capabilities define IDC's five BDA maturity stages: Ad hoc: Experimental, siloed proof-of-concept or pilot projects, undefined processes, lack of resources, and individual effort Opportunistic: Accepted, recurring projects, budgeted and funded program management, and documented strategy and processes with stakeholder buy-in Repeatable: Intentional, defined requirements and processes, unbudgeted funding, and project management and resource allocation inefficiency Managed: Measured; project, process, and program performance measurement influences investment decisions and standards emerge Optimized: Operationalized and continuous and coordinated BDA process improvement value realization The five maturity stages are described in greater detail in IDC MaturityScape: Big Data and Analytics A Guide to Unlocking Information Assets (IDC #239771, March 2013). Figure 1 depicts the distribution of utility companies across the five levels of BDA maturity. This is a composite view, showing maturity across all five dimensions: intent, data, technology, process, and people. The results show a skew toward the low end of maturity, with more than four times as many utilities in the lowest two stages of maturity as in the highest two stages and nearly two-thirds of utilities in the middle stage. While it may be somewhat surprising to see nearly two-thirds of utilities in the middle stage of maturity, these results are consistent with what we see across all vertical industries as a whole. If the other stages of maturity are considered, utility companies skew lower in maturity than a composite view of all industries. This is consistent with IDC's research and assessment that the utility industry is at a relatively early stage of understanding and deploying big data and analytics. There's another inference to be drawn from Figure 1. A class of "haves" and a class of "have-nots," comprising 31% and 7% of utilities, respectively, are emerging. This dynamic could set up a disparity where some utilities lag behind their peers in maximizing their revenue potential. Given the increasing importance of BDA insights to the utility industry, this disparity in BDA competence, to the extent that it 2014 IDC Energy Insights #EI

4 is sustained, will mean that we will see significant differences in the capabilities of utilities to effectively manage their operations and to capture greater opportunities at a time of considerable transformation in the industry. FIGURE 1 Big Data and Analytics Maturity Distribution Across Stages: Utilities n = 59 Source: IDC's Big Data and Analytics Maturity Benchmark Survey, 2013 A Deeper Look BDA Maturity Model Dimensions Although valuable as shorthand for evaluating overall BDA maturity, the single maturity score presented in this study (refer back to Figure 1) does not provide enough detail for utility line-ofbusiness (LOB) and IT managers to prioritize investments in specific BDA capabilities. To provide a deeper set of insights, IDC also measured maturity across the five dimensions of our BDA maturity model: Intent: The measure of intent includes attributes such as strategy, capital and operational budgets, performance metrics, sponsorship, and project and program justification. Data: The measure of data includes attributes such as the quality, relevance, availability, reliability, governance, security, and accessibility of multi-structured data. Technology: The measure of technology includes attributes such as the appropriateness, applicability, integration, support for standards, and performance of technology and IT architecture to the relevant workloads. Process: The measure of process includes attributes such as the processes of data collection, consolidation, integration, analysis, information dissemination and consumption, and decision making IDC Energy Insights #EI

5 People: The measure of people includes attributes such as the technology and analytics skills and intragroup and intergroup collaboration, as well as organizational structures, leadership, training, and cultural readiness. These dimensions are discussed further in the Actions to Consider section and described in greater detail in IDC MaturityScape: Big Data and Analytics A Guide to Unlocking Information Assets (IDC #239771, March 2013). Figure 2 presents the maturity distribution of utility companies for each of the five BDA maturity model dimensions and shows the variability of maturity distributions across the five dimensions. Maturity in the data and technology dimensions cluster toward the middle (repeatable) stage of maturity, with technology being more of a near-perfect bell curve while the data dimension skews to the lower stages of maturity. Intent shows both the flattest distribution across the five dimensions and the highest maturity, as represented by the utilities in the upper two stages of maturity in intent. Process shows the lowest maturity in utilities of any of the five dimensions, with the people dimension not far behind. The low maturity in the process dimension reflects, no doubt, the still-early experience of the utility industry in deploying big data and analytics. This is likely to present a challenge, but not an insurmountable challenge, as the utility industry feels an increasing motivation to deploy technologies to address a growing data challenge IDC Energy Insights #EI

6 FIGURE 2 Big Data and Analytics Individual Dimensions Maturity Distribution Dashboard: Utilities n = 59 Source: IDC's Big Data and Analytics Maturity Benchmark Survey, 2013 The Value of Investing in Improving Big Data and Analytics Maturity Knowing these distributions enables an assessment of an organization's current BDA maturity. Every improvement initiative should start with the "as is" assessment that leads to prioritization of investment of financial and human resources and time. Understanding an organization's current BDA maturity level and comparing the maturity level with the market or an industry benchmark is a valuable performance management process IDC Energy Insights #EI

7 However, there is also a need to make a case for the aforementioned investments. What is the value of investing in BDA capabilities to reach higher BDA maturity? Our conversations with utility companies over the past months have shown an increasing understanding of the value of BDA insight applied to line-of-business concerns. These survey results buttress this assessment. We designed the BDA maturity model benchmark study in part to discover whether there is evidence that investing time, resources, and talent in BDA maturity improves business outcomes. To accomplish this, we asked organizations to self-assess the aggregate benefits derived from their recent BDA projects (see the Methodology section for the possible options selected by organizations in response to the question about BDA project benefits). We segmented organizations into two categories: Highest achievers. Organizations where the benefits of recent BDA projects, in aggregate, met or exceeded expectations Lower achievers. Organizations where the benefits of recent BDA project, in aggregate, did not meet expectations or resulted in no benefits Note that we excluded organizations that had unquantified benefits or whose BDA deployments were too new to allow for the assessment of benefits from this categorization. The comparison of maturity levels of each of the five BDA maturity dimensions for these two groups is shown in Figure 3. The results across multiple industries in IDC's research point to a correlation between maturity and outcomes. At the higher levels of maturity (repeatable, managed, and optimized), the distribution of high achievers skews toward higher levels of maturity, with a notable skew toward low achievers at the lower stages of maturity. The utility industry, unique in so many ways, shows high-achiever/low-achiever maturity distributions that contrast with what we see in other industries, due, we believe, to the still-early nature of the utility industry's foray into BDA. The intent dimension shows the type of correlation that we see across the multiple dimensions across other industries, where higher achievers predominate at higher levels of maturity and lower achievers predominate at lower levels of maturity. The usual cautions against relying too much on correlations to draw causal relationships applies. Although we did not attempt to quantify the impact of any one-stage maturity improvement (ad hoc to opportunistic, opportunistic to repeatable, etc.), what these figures show, more than anything else, are the mixed results that we are still seeing in the utility industry IDC Energy Insights #EI

8 FIGURE 3 Comparison of Big Data and Analytics Maturity Between High and Low Achievers Dashboard: Utilities n = 59 Source: IDC's Big Data and Analytics Maturity Benchmark Survey, 2013 Maturity Alignment Our research findings underscore the importance of alignment across all five dimensions in achieving better outcomes from BDA efforts. Common guidance tends to stress two sets of BDA competencies technology and people over and above the other three, but common guidance that stresses the importance of any capabilities more than the others is misleading IDC Energy Insights #EI

9 Our research demonstrates that coordinating all five dimensions of maturity regardless of their absolute level leads to better outcomes from BDA initiatives. We found evidence for this by constructing three levels of maturity alignment and comparing them with the distribution of high and low achievement in BDA outcomes. We established three levels of maturity alignment: Tightly aligned. Organizations where the difference between the highest and the lowest maturity stages across the five dimensions is one stage. In this category, all five BDA maturity dimensions for any given organization fall into a very narrow span of a single maturity stage. Loosely aligned. Organizations where the difference between the highest and the lowest maturity stages across the five dimensions is either two or three stages. Misaligned. Organizations where the difference between the highest and the lowest maturity levels across the five dimensions is four levels. In this category, all five BDA maturity dimensions for any given organization vary widely across the maturity stages. Figure 4 presents the distributions of maturity alignment for high and low achievers for all industries. There is some evidence of a correlation between positive outcomes and alignment. The difference among high achievers and low achievers gets progressively bigger as the maturity alignment score improves. From "misaligned" to "loosely aligned" to "tightly aligned," the percentage of low achievers declines, while the percentage of high achievers increases, providing some evidence of a correlation between positive outcomes and alignment. FIGURE 4 Big Data and Analytics Maturity Alignment Among High and Low Achievers n = 760 Source: IDC's Big Data and Analytics Maturity Benchmark Survey, IDC Energy Insights #EI

10 These market realities point to the need to create a BDA strategy that ensures investment is aligned across all five key aspects of BDA maturity. Any BDA strategy is likely to be only as strong as the weakest link in its chain of capabilities no matter the strength of its other capabilities. Note that on its own, having a better maturity alignment score does not suggest greater BDA maturity. For example, an organization that has all five dimensions at the ad hoc stage and another organization that has all five stages at the optimized stage will have the highest maturity alignment score (i.e., tightly aligned). It is maturity alignment taken in combination with individual maturity stages of each dimension that utilities should measure and manage. THE APPROACH The results in this study are based on IDC's 2013 Big Data and Analytics Maturity Benchmark Survey of 760 organizations, including 59 utility companies, in the United States and Canada conducted in the fall of Results from each of the respondents were scored, and key metrics, as discussed in this study, were derived from the scored results. See the Methodology in the Learn More section for a description of the data collection and analysis methodology employed in this study. FUTURE OUTLOOK We are still at the very early stages of a data-driven world where not only access to information but the ability to analyze and act upon it in real time or near real time can create a competitive advantage in the marketplace; enable sustainable management of communities and natural resources; and promote more appropriate delivery of social, healthcare, and educational services. In the utility industry, where there are quite a different mix of pressures than in many other industries, BDA shows tremendous promise and will continue to increase its focus for utility companies for the foreseeable future, with an expanding number of use cases. IDC Energy Insights sees current and future use cases for BDA in the following areas: Customer insights: Understanding customers' wants, needs, and expectations, including from the analysis of unstructured data found in social media or s Account management: Identifying problems or irregularities in customer accounts Outage detection: Sensing and assessing outages through the analysis of meter data delivered through AMI Gas leakage detection: Determining the maximum allowable operating pressure on gas pipelines to support pipeline integrity Transformer loading: Combining smart meter data with other grid data points to highlight complete transformer load flows for project planning Demand response: Issuing a day-off call for demand response based on customers enrolled, network connectivity, devices associated with loads, and supply 2014 IDC Energy Insights #EI

11 Vegetation management: Using analysis of tree species and location to plan vegetation management Theft and fraud detection: Identifying theft and fraud using smart meter and credit data Predictive asset management: Using situation intelligence to identify and preempt maintenance issues and asset failures and perform condition-based maintenance ESSENTIAL GUIDANCE As irrational exuberance about BDA subsides and its real benefits come into focus, it is becoming increasingly critical for utility companies to focus on improving the maturity of their organization's BDA capabilities. Although there are many factors, or combinations of such factors, that LOB and IT management can effect to move utilities toward greater BDA maturity, we have found a shorter list of factors that have the greatest impact on each of the five BDA maturity model dimensions. This section identifies these high-impact BDA maturity enablers. Factors with the Greatest Impact on Maturity Table 1 depicts the most significant factors that define high achievers with respect to their BDA capabilities and project outcomes. They are culled from a set of about 30 capabilities and about 90 more subordinate capabilities across the five dimensions of BDA maturity evaluated in our survey. They are the capabilities where we observed the greatest difference between the number of high achievers and low achievers at the optimized level of maturity (and sometimes at that level combined with the numbers falling into the managed level of maturity). The sections that follow provide additional commentary about these high achievers' BDA traits IDC Energy Insights #EI

12 TABLE 1 Top 10 Traits of Big Data and Analytics High Achievers Maturity Dimension Intent Data Data Technology Technology Process Process People People People Trait Fund and budget BDA at the enterprise level under the discipline of a business case. Ensure secure, trusted, actionable, timely, complete, and high-quality data. Integrate multiple data types, particularly transactional data, and sensor or machinegenerated data. Strive for scalability, reliability/availability, security, manageability, and performance in analytics technologies. Focus on development of structured reporting tools, multidimensional analysis tools, advanced analytical tools for predictive analysis or data mining, dashboards, and apps for consuming data in support of decision making on mobile devices. Develop defined, disciplined, and continually improving practices around analytics strategy and planning, including big data and analytics strategy development, business requirements definitions, business case development, and value justification. Develop information management and application development, governance and data analysis, and information consumption processes. Equip your organization with big data and analytics skills around project planning, strategy development, and governance; data analysis tools, such as multidimensional analysis and visual discovery tools; and performance measurement. Build a culture of communication, collaboration, and coordination across IT, analytics groups, and lines of business. Hire, develop, and retain technical analytics people, including data scientists, and staff involved in administration and maintenance of big data and analytics related hardware. Source: IDC Energy Insights, 2014 Actions to Consider Align Capabilities Utility companies should take action to achieve the traits of high achievers (refer back to Table 1) within an overall effort to align the maturities of BDA capabilities. Our analysis of the top traits of high achievers across all industries confirms the importance of alignment: achieving a balance of maturity across the critical dimensions intent, data, technology, process, and people. Our analysis of the top traits of high achievers confirms the importance of alignment IDC Energy Insights #EI

13 Focus on Business Leadership Paying attention to where responsibilities for various aspects of BDA programs are assigned is another indicator of high achievers. Utility companies should approach BDA programs as business-led, analytics-supported efforts. Tight and agile coordination of line-of-business and analytics organizations emerges as a best practice among high-achieving BDA utilities. Table 2 displays how high achievers assign responsibilities to lead various efforts. TABLE 2 Units Taking the Lead in Big Data and Analytics Business Unit Function Business group Data analysis and exploration using advanced or predictive analytics methods such as statistical analysis, data mining, and machine learning Analytics group Business intelligence and analytic application development, dashboard, and structured report development IT group Data or content collection, integration, preparation, and management Collaboration with any two of the abovementioned groups Performance measurement (e.g., monitoring of technology usage trends, postdeployment decision quality, and business outcomes evaluation) Strategy development and project planning and management Data analysis using multidimensional analysis or OLAP, spreadsheets, and visual discovery tools Governance (e.g., technology or service evaluation, selection, and purchasing; external service provider or vendor management; information governance, security, and data privacy; and master data management) Source: IDC Energy Insights, 2014 Intent Dimension of BDA Maturity Assess and Approve BDA Initiatives with a Formal and Enterprisewide- Accepted ROI Evaluation Method Better outcomes come from better preparation and better justification going into an initiative. At a time when the number of use cases and number of pilots is on the increase, we recommend closer attention to the up-front work. Preparation means, in particular, documenting the expected cost and benefit using a method that is templated, understood, and accepted across the enterprise. This tactic shows up more often in high achievers than low achievers by a more than 2:1 ratio. Using an enterprisewideaccepted costbenefit methodology shows up more often in high achievers than in low achievers by a more than 2:1 ratio IDC Energy Insights #EI

14 Fund and Budget BDA Projects Annually at the Enterprise Level Thirty-five percent more high achievers than low achievers fund and budget their BDA projects through an annual enterprisewide budget (and some further supplemented the budget with ad hoc funding for special projects). This high achiever low achiever gap in the utility industry is much greater than the gaps for industries overall. We recommend that organizations elevate BDA funding and budgeting to the enterprise level rather than fund BDA project with ad hoc, unbudgeted funds, or dollars reallocated from other sources. However, this should not preclude "skunk works" or proof-of-concept projects at the business unit level. Data Dimension of BDA Maturity Focus on Data Quality There are several characteristics of data such as trustworthiness, timeliness, quality, security, and completeness and the extent to which data is actionable. Of all of these data characteristics, however, one stands out. The characteristic with the biggest margin between high-achieving BDA and low-achieving BDA in utilities is security, followed closely by trust, and whether data is actionable. Data is obviously at the heart of BDA, and so focus on several dimensions of data integrity are usually well rewarded in terms of outcomes. IDC's analysis shows that high-achieving utilities are placing particular emphasis on their data being secure, trusted, actionable, complete, and high quality. Exploit Enterprise Data Assets The characteristic with the biggest margin between high-achieving BDA and lowachieving BDA in utilities is security. High achievers are also distinguished from low achievers in that they are integrating multiple data types, particularly transactional data and sensor- or machine-generated data. Utilities are still relatively early in the use of and leveraging big data and analytics. While myriad opportunities exist for integrating such things as customer interaction related data and text, mobile device data, content repositories, and geographic or spatial data, high achievers are taking the approach of focusing first on these two specific data types. Utilities are grappling with large, and growing, volumes of data and an increasing diversity of data types. Utilities have always had access to SCADA data via historians, but with the increased penetration of sensors on the grid and smart meters, data volumes have increased. The diversity of data types comes with the availability of new sources of unstructured data, such as customer photos of downed electric lines texted to the utility. While this entails a certain amount of data integration, IDC's research indicates that broad-based integration of a large range of data types is not yet the significant differentiator of high-achieving BDA from low-achieving BDA that we expect to see as maturity increases IDC Energy Insights #EI

15 Technology Dimension of BDA Maturity Work with Users to Understand and Use a Range of BDA Technologies We recommend that utility companies work with users in their companies to use a range of BDA technologies. High-achieving BDA utilities have users that are satisfied with using multidimensional analysis or ad hoc analysis tools, structured reporting tools, advanced analytics tools (for predictive statistical analysis or data mining), and dashboards and apps for consuming data and making decisions on mobile devices, with a considerable gap over low-achieving BDA utilities. Use of structured reporting tools and multidimensional analytical tools are the two most significant differentiators between high and low achievers. Given the data-rich environment of the utility industry, even historically (even before the recent proliferation of data), the priority being given to multidimensional analytics and structured reporting tools is not particularly surprising. Multidimensional analytics brings more sophisticated capabilities to the more complex data management requirements that utilities have. Structured reporting delivers vital functionality when it comes to using more data to make more decisions faster and to the challenges of responding to the numerous regulatory mandates and regulatory bodies. Making sure that users are satisfied with the functionality delivered in these areas will help utilities have better outcomes with BDA. Making sure that users are satisfied with the functionality delivered in structured reporting, and multidimensional analysis, in particular, will help utilities have better outcomes with BDA. Focus More on the Types and Capabilities of BDA Technologies Rather Than Features/Functions We recommend that utilities focus more, particularly early in the learning curve and when doing pilots, on the types of technologies and the capabilities of these technologies rather than on specific features and functions. Although this may seem somewhat counterintuitive, our research shows that high-achieving BDA in the utility industry have focused more on capabilities (scalability, reliability, and security) and on types of BDA technologies (structured reporting tools and multidimensional analysis or ad hoc analysis tools) than on specific features and functions needed. This conclusion is buttressed by lessons learned from early smart grid pilots that did not take into account the need for scalability when moving from pilot to enterprisewide implementations. We ascribe this to the early maturity cycle that many utilities are in. IDC's research shows that just 10.2% of utilities are in production with big data at the enterprise, or business unit/departmental level (17.1% for analytics), with each of these numbers increasing over the next 12 months (to 13.5% of utilities for big data; 20.3% for analytics). As enterprises get more mature in their use of BDA, the feature and functionality becomes more critical. People Dimension of BDA Maturity Recruit, Hire, Reward, and Retain a Strategic Blend of Skills A BDA initiative requires a mix of talents, both technical and nontechnical, from business and IT, including project managers, strategy developers, business or program analysts, data scientists, electrical engineers, mechanical engineers, and statisticians. Data scientists, already in short supply, are particularly rare talents in the utility industry. So utilities should start now to assess their available Focus more on capabilities than on specific features and functions IDC Energy Insights #EI

16 resources for big data and analytics initiatives and develop plans for how they will assemble the right mix of skills. We recommend that utilities pay attention particularly to equipping their BDA initiatives with strategy development and project planning. With many utilities still in the early stages of initiating their BDA strategies, strategy development and project planning skills are proving to be key differentiators between high achieving and low achieving BDA initiatives. Also important to the success of BDA initiatives are skills and qualifications in data analysis and governance (assessment of technology or services providers, information governance, security and data privacy, and master data management) and skills in performance measurement. We see high-achieving utilities putting a high focus on these areas. IDC's research shows that utilities should pay particular attention to strategy development and project planning skills. Hiring and developing for these roles is of course very important. A critical role that utilities have to hire, develop, and retain is the data scientist. Utilities that are high achieving also report being more successful in hiring, developing, retaining, and rewarding their data scientists by a more than 50-percentage-point gap, highlighting the critical role of this function. Utilities are also depending on hiring and developing strategy and development personnel, business and program analysts, and people who can maintain big data and analytics related hardware. We recommend that utilities be sure to nurture the roles of evaluating business and decision outcomes, people for data collection and integration, people who can develop business intelligence and analytic applications, and people who are responsible for governance. Big data analytics requires a mix of technical and nontechnical roles, including people who are skilled at being able to interpret the data to recommend actions that the utility should make from an analysis of all of the relevant data. This last step can sometimes get lost in the process. Complement Skills and Development with a Culture of Collaboration High-achieving utilities are more successful in hiring, developing, retaining, and rewarding data scientists. While skills, development and hiring, and technology are all important, the result is not optimized without a good deal of collaboration across the enterprise. The BDA initiative requires a strategic blend of skills, which draws on resources from across the enterprise. High-achieving BDA utilities ensure that there is collaboration with lines of business, executive management, and other operational employees, so that the insights from BDA technologies can serve to influence and guide decision making across the enterprise. Apply BDA Insights to Management Decision Making We recommend that utilities companies embed BDA insights in the data-driven decision-making culture among business managers and executives. Utilities are High achievers build a culture of collaboration, communication, and coordination across analytics groups and lines of business IDC Energy Insights #EI

17 accustomed to working with a lot of data. The challenge, however, is growing by an order of magnitude and presenting new challenges and opportunities that utilities need to embrace. With many challenges vying for the attention of utilities, it is easy to overlook the opportunities that BDA offers. Utilities need to manage a growing volume of data; they are looking for ways to respond better and with more services to customers and are looking to operate their enterprises more efficiently. BDA addresses these challenges in multiple ways. However, this will require utility executives to look at new ways of doing things and at using new streams and types of data to make decisions in new ways and to have the ability to use new kinds of data in new ways. Often, the hardest part of analytics is being ready to make new data-driven decisions. Utilities' executive management will need to lead the way and, perhaps, offer inducements, incentives, and even mandates to implement the use of big data analytics in their organizations. Process Dimension of BDA Maturity Start with Strategy and Planning We recommend that utilities pay particular attention to incorporating strong strategy and planning processes into their BDA initiatives as more utilities enter into pilots and proofs of concept. It is often difficult, early on, to pull end-user requirements from uninitiated users. A lack of clearly articulated requirements from users should not be the go/no decision factor on BDA initiatives. This makes it even more important to focus on having a managed, defined, and measured business case development, strategy development, value justification, and other planning activities. The importance of strategy and planning processes is, therefore, consistent with where utilities are in their initiation to BDA. High-achieving utilities have, by a 38-percentagepoint margin over low-achieving utilities, strategy and planning processes that are defined, measured, and managed and undergo continuous process improvement. In fact, no low-achieving utilities reported having strategy and planning processes that are defined, measured, and managed and continuously improved upon. Focus Also on Information Management and Application Development Close behind in importance to the strategy and planning processes is the information management and application development process. High-achieving utilities focus nearly as much on having defined, measured, and managed processes in these areas. Utilities are seeing the results they expected to see because they are focusing on such things as data collection and integration, dashboard and structure report development, and analytics infrastructure development and support. High achievers in the utility industry were more advanced in these processes by a very significant 33-percentagepoint margin. A managed, defined, and measured strategy and planning process contributes to more successful outcomes. Highestachieving utilities have, by a 38-percentagepoint margin over lowachieving utilities, strategy and planning processes that are defined, measured, and undergoing continuous improvement IDC Energy Insights #EI

18 Data Analysis and Information Consumption The Last Mile of BDA Success High achievers distinguish themselves from low achievers in this "last mile" of the process. Highachieving utilities are making sure that processes for such things as advanced or predictive analytics, multidimensional analysis, visual discovery tools, or deployment of analytic models are defined, managed, and measured and are continuously improved upon. We recommend that utilities pay a lot of attention to this last mile of BDA success and instill these processes with discipline, continual management, and continuous learning and improvement. As utilities become even more data driven, this last mile may just be the "litmus test" by which the overall success of the BDA initiative is remembered. LEARN MORE Related Research IDC MaturityScape Benchmark: Big Data and Analytics in North America (IDC #245197, December 2013) Business Strategy: Utilities and Business Analytics Meeting Today's Needs While Preparing for Tomorrow (IDC #EI240519, April 2013) Business Strategy: Opportunities to Align Big Data and Analytics with Needs for Insight (IDC #GRI240422, April 2013) IDC MaturityScape: Big Data and Analytics A Guide to Unlocking Information Assets (IDC #239771, March 2013) Business Strategy: The Big Data and Analytics Pillar of 3rd Platform Retail IT (IDC #GRI236643, August 2012) Methodology The results in this study are based on IDC's 2013 Big Data and Analytics Maturity Benchmark Survey of 760 organizations in the United States and Canada. The survey was conducted in the fall of Each respondent's results were scored and segmented into the five maturity stages of IDC's BDA MaturityScape. The survey, executed online, was based on a structured questionnaire of 31 questions. Fifteen questions had subquestions that in aggregate resulted in 89 additional subquestions. The survey questions were categorized logically into five segments and focused on each of the five dimensions of IDC's BDA MaturityScape. Note: All numbers in this document may not be exact due to rounding. Survey Respondent Segmentation The survey respondents were segmented as follows: 97% from the United States and 3% from Canada 2014 IDC Energy Insights #EI

19 16% from IT function and 4% from centralized business intelligence or analytics function of their organization (The rest of the respondents were distributed across a range of line-ofbusiness, government program, or executive management functions.) 94% of respondents with title of manager/supervisor or higher Eight industries were included in the study with the criteria and sample size as shown in Table 3. TABLE 3 Sample Size and Selection Criteria of Research Participants by Industry Industry Sample Size Selection Criteria Telecommunications services provider million connections in its network Commercial, retail, or investment bank 101 Total assets over $1 billion U.S. federal government, executive department, or independent agency 98 Department or agency having 5,000+ employees Non-food retail 100 $100+ million in annual sales Oil and gas 100 $10+ billion in annual revenue Utilities 59 $500+ million in annual revenue Acute care hospitals beds CPG manufacturer or retail grocer 100 $100+ million in annual sales Source: IDC's Big Data and Analytics Maturity Benchmark Survey, 2013 Categorization of High Achievers and Low Achievers Among the questions asked in IDC's 2013 Big Data Analytics Maturity Benchmark Survey was a question about typical outcomes of BDA projects. Outcomes were assessed based on the following question and response options: Q. If you think about recent big data and analytics projects in aggregate, has your organization experienced any of the following types of benefits from these projects? (Select one.) Have not experienced benefits Unquantified benefits Quantified benefits that did not meet expectations Quantified benefits that met expectations Quantified benefits that exceeded expectations We just deployed the solution and it's too early to assess 2014 IDC Energy Insights #EI

20 High achievers were defined as those organizations where the benefits of recent BDA projects, in aggregate, met or exceeded expectations. Low achievers were defined as those organizations where the benefits of recent BDA project, in aggregate, did not meet expectations or resulted in no benefits. Organizations that either had unquantified benefits or those where BDA deployments were too new to allow for the assessment of benefits were excluded from this categorization. Synopsis This IDC Energy Insights report presents results of the first IDC BDA MaturityScape benchmark based on a study of 760 organizations including 59 utility companies at $500+ million in revenue. "The utility industry is not as mature as some other industries in their use of analytics but has some terrific opportunities ahead. We are already seeing more utilities piloting or experimenting with big data and analytics. Given the mix of challenges facing the utility industry, we expect to see more pilots and proofs of concept going on over the next months. So this is an opportune time for most utilities to be assessing their maturity with regard to big data and analytics. This report delivers some key insights and tools," says Robert Eastman, research manager, IDC Energy Insights, and lead analyst for the Worldwide Utility IT Strategies practice. "This BDA maturity framework developed by IDC will assist utility companies in assessing their current capabilities and to evaluate gaps in reaching higher levels of BDA maturity." 2014 IDC Energy Insights #EI

21 About IDC International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications and consumer technology markets. IDC helps IT professionals, business executives, and the investment community make factbased decisions on technology purchases and business strategy. More than 1000 IDC analysts provide global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries worldwide. For more than 48 years, IDC has provided strategic insights to help our clients achieve their key business objectives. IDC is a subsidiary of IDG, the world's leading technology media, research, and events company. Global Headquarters 5 Speen Street Framingham, MA USA idc-insights-community.com Copyright Notice Copyright 2014 IDC Energy Insights. Reproduction without written permission is completely forbidden. External Publication of IDC Energy Insights Information and Data: Any IDC Energy Insights information that is to be used in advertising, press releases, or promotional materials requires prior written approval from the appropriate IDC Energy Insights Vice President. A draft of the proposed document should accompany any such request. IDC Energy Insights reserves the right to deny approval of external usage for any reason.