Big data can be your company’s greatest asset — you own it, create it, and store it. However, more important is what you do with it. For example, as a director of customer operations recently stated, “We are data rich but information poor.”

Likewise, data and analytics (D&A) are creating great opportunities for electric and gas utilities. The value created is illustrated in a recent research study by KPMG and Institutional Investor Research,¹ in which financial analysts said that companies with a D&A strategy are likely to outperform their competitors and achieve higher equity valuations.

The business value of D&A is based on better decision-making and understanding risk. The ability to use data to discover actionable insights is complicated. To derive value from data requires the proper definition and alignment of your organization, technologies and strategy. We call this the analytics transformation. This transformation requires a synergy of all components of an effective D&A strategy.

Developing a data-driven ecosystem takes time. Few energy companies are immediately able to derive value from their data. Many companies lack the proper integration of big data technologies and processes, advanced analytic skillsets, and an organization-wide, analytic strategy. Many companies are making inroads into D&A by hiring data scientists, by purchasing big data platforms or analytical tools, or by pouring data from a variety of different sources into a common database. However without access to the right data or tools, data scientists will not be able to deliver on their promises. Big data platform and data aggregation by themselves do not generate new insights.

How do you effectively transition to a data-driven and analytical organization? What are the benefits you expect? The business value and insights come from an effective integration of different D&A components.

Elements of an Effective D&A Transformation

An effective D&A transformation will vary based on your organization’s needs and capabilities. However, there are four components, which are important for a D&A transformation to be effective:

Organization: It has the right capabilities with clear roles and responsibilities across the business. There is a well-defined prioritization and execution model that ensures minimal task duplication. It is combined with an adaptive, agile approach to building advanced analytics, consistently applied procedures, rapid prototyping and effective deployment to production systems.

Technology: A consistent, fit-for-purpose technology platform that ensures maximum-scale benefits and operational capability. It also maintains interoperability allowing different business units to share analytics and expertise across platforms.

Data: A common, corporate data layer that enables data sharing within appropriate Security controls. It facilitates the discovery of unknown correlations and business model innovations, as well as a strong data governance framework to support high-quality output.

Strategy: A clear strategy to ensure the alignment of analytic initiatives to organization objectives, combined with consistent and effective coordination of activities across business units. This minimizes duplication, maximizes the sharing of resources and speeds execution.

Collectively, these four components of an effective D&A strategy will allow your organization to efficiently use analytics by identifying and aligning priorities to who, when and where analytics should be applied; and to produce additional actionable insights and create business value. At the same time, they help reduce inconsistencies between different business units and an unnecessary propagation of solutions as analytics scales across the enterprise.

components of an effective D&A strategy will allow your organization to efficiently use analytics by identifying and aligning priorities to who, when and where analytics should be applied.

Each component relies and builds upon another—the better-developed one component is, the better the other components will perform. Each component can be developed to various degrees of sophistication.

Transition to a Data-Driven, Analytical Organization

In order for your organization, to embark on a D&A transformation, it is important to understand your current level of analytical maturity Using a customized assessment aimed at key stakeholders throughout the organization, you can gain insights into existing pockets of analytical capabilities and areas of opportunity. You also can better understand the barriers that keep your organization from reaching higher D&A capabilities.

Based on your organization’s current level of maturity, a customized, detailed strategic transformation plan can be developed to meet your organization’s individual requirements and desired analytical capabilities. You do not have to start with a comprehensive data analytics strategy. Sometimes it is beneficial to start with existing capabilities and build out from there. At KPMG, we actually have seen our clients use two different approaches successfully: A focused/need-based approach and a top-down/organization-wide approach.

Regardless of your approach, you should always start with the business value in mind. This will guide you in the development of your transition plan and will keep you focused on the overall objectives.

Focused/Need-Based Transformation

A focused/need-based transformation starts where your organization has developed some D&A capabilities, or where there is a strong business case. For example, a utility might have already invested in analytics and technology to reduce call volume at its call center. However, customer service operations may still be too busy to create daily scorecards or to manually address management’s requests for additional insights. This is a great opportunity to build and test the organization’s analytical capabilities. You can choose to focus on building out the four elements of the D&A transformation on a small scale, or outsource the analytics and technology together to determine business benefits.

If your organization is developing everything in-house, you can achieve quick wins, such as automated scorecards and answering ad-hoc queries instantly to reduce call volume. Advanced analytics can help identify call reasons, map out s customer behavior, and develop targeted customer initiatives to improve the self-serve rate.

Alternatively, your organization can leverage a managed service for analytics to determine new business values. In this case, a service provider simply takes your data and provides analytical insights for decision-making back to you. Here, you don’t have to invest into technology, or hire and train data scientists and software engineers. This approach particularly can be useful for an organization with little or no experience with data analytics.

Regardless whether you leverage a managed service or build the initial business case in-house, you quickly will see related use cases and can build out your capabilities as quickly or slowly as you like. One utility, for example, was investing in customer service analytics to improve customer satisfaction and reduce customer operations costs, when it discovered that it also was extremely helpful for credit and collections. Customer behavior, analyzed for different customer segments, gave new insights and a basis for creating predictive models. It helped determine the likelihood of different customers falling behind on payments, going into collections and ultimately getting disconnected. The analytics can better predict bad debt, but also to develop targeted counter measures to prevent customers from defaulting, by signing up for assistance programs, shut-off protections and other programs.

As this example shows, while this approach started with a particular area and business case, it functioned as a catalyst to spread into other areas: from customer operations, to credit and collections, and accounting. Thus, starting small can be a good entry point for leveraging analytics, but keep scalability and economies of scale in mind.

Top-Down, Organization-Wide Transformation

The larger, top-down and organization-wide approach requires more planning, strong organizational buy-in and commitment from leadership. It also means a larger upfront investment. While this approach may sound daunting, it can deliver a variety of different business values within a relatively short amount of time. In addition, a D&A transformation remains manageable with a relatively low technical complexity, compared to enterprise-wide, IT implementations. This approach might be best suited for organizations that have a need for the implementation of several different analytical use cases, and want to take advantage of economies of scale.

Develop the transformation plan after the current-state assessment. This approach is more structured and follows the following five steps to creating a strategic transformation plan:

  1. Strategy: This outlines the organization’s vision in six key areas: mission, objectives, services and use cases, sourcing and location, governance and business alignment, and data and technology. Key stakeholders who are connected to any or all areas are engaged during this step.
  2. High-level design: Conceptual designs are created in three key areas: macro organization design, data and technology future states, and conceptual analytic scenarios. Each design is distributed to the appropriate stakeholders, and the design is altered to fit each stakeholder’s requirements, and current and desired capabilities.
  3. Detailed design: After the conceptual designs are reviewed and approved, detailed plans are developed within five areas: use case prioritization, use case execution methodology, micro organizational design, data and technology acquisition plan, and workforce transition plan.
  4. Build: The build phase takes the detailed designs and begins the physical and cultural transformation. Use case execution templates are created for various functions within the organization. Workforce transition build begins and the required skillsets are developed. Analytic technology set up puts the proper infrastructure in place. Process tools are developed and distributed throughout the organization. Data consumption begins and is shared across functions.
  5. Implement and improve: Continuous monitoring of the transformation carries on during and after the implementation occurs. Workforce transition execution monitoring, technology testing and monitoring, and operational handbooks are continually altered and improved based on the overall success of the strategic transformation.

The five-step strategic transformation plan is tailored to your organization’s current and desired state of analytic capabilities. Once the strategy is executed, various safeguards and review checkpoints are integrated throughout the process to review performance and make alterations. Implementation is a continuous process that evolves, adapts and is scaled to meet an organization’s changing analytic requirements.

Benefits to Expect

Once your organization embarks on the D&A transformational journey, you can expect a variety of different benefits:

Financial benefits – through lower capital and operational expenditures, understanding financial drivers, and identifying innovation opportunities.

Regulatory benefits – through the capacity to adapt, at minimal cost, and answering regulatory and compliance demands.

Customer service benefits – through the anticipation of customer behavior and changing needs, quicker and improved customer communications, and overall greater insight into customer segments.

Cultural and organizational benefits – through fewer silos, increased transparency, and improved data-driven decision making.

A simple, focused/need-based use-case can be all that is needed to understand the benefits of analytics. Based on these benefits, you can spread analytics into other areas within the organization, or decide to go forward with a top-down approach.

One mid-sized energy company for example, started its analytical transformation with a single, need-based analytic use case. It sought insights into how to better manage the preventative and corrective maintenance of its assets. Pressing questions included:

  • Which assets are likely to need unscheduled maintenance?
  • Where should we focus our capital investment based on our asset performance?
  • What are the forecasted financial implications of data-driven preventative and corrective maintenance?

Starting with a single-use case, and focusing on its most capital-intensive critical infrastructure, the company built out predictive models for asset performance. These analytics soon expanded to help make better capital investment decisions, more accurately develop maintenance budgets and provide better information to regulators. Thus, the energy company was able to capture not only direct financial benefits and better work with regulators, but also experience a transformation throughout its organization.

While the D&A transformation looks different for everyone, the potential benefits to be gained are vast and often come from expected areas.

  1. Data and Analytics: A new Driver of Performance and Valuation, July 2015.