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Data Analytics Use Cases for Utilities

Building upon the collective wisdom for the Art of the Possible

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Your organization recognizes the importance of building a business-driven data strategy to enable digital transformation. You are having difficulty in

  • Building a list of value-driven business use cases required to develop a data analytics program.
  • Justifying the effort and cost to invest in the program.
  • Developing a practical and agile implementation plan to help solve business data challenges.

Our Advice

Critical Insight

Demonstrating the return on investment of data analytics programs rapidly is a vital step to digitally transforming your organization.

Empower data-driven decision making by leveraging Info-Tech's approach to accelerate the process and take advantage of the collective wisdom from your community.

Impact and Result

Info-Tech’s trends deep-dive report on data analytics use case for utilities can fast track the data strategy development by:

  • Accelerating your business case development by providing a curated data analytics use case repository.
  • Identifying the right data problems to solve which deliver the highest value by leveraging a rapid and effective prioritization. framework.
  • Developing an iterative and value proven roadmap based on your organizational scorecard.

Data Analytics Use Cases for Utilities Research & Tools

1. Data Analytics Use Cases in Utilities. Learn from a living repository consisting of a well-evaluated list of prioritized use cases and a multi-waved roadmap consisting of defensible prioritized business value driven use cases.

This research report provides utility leaders with a repository of pre-curated utility data analytics use cases as well as a filtering and prioritization framework. This approach can help fast-track the process of identifying the right business problems to solve strategically.

2. Data Analytics Use Cases Analysis Tool

This tool provides a pre-curated utility relevant data analytics use cases as well as a prioritization framework to develop a multi-waved roadmap consisting of defensible business value driven use cases.

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Data Analytics
Use Cases for Utilities

Building upon the collective wisdom for the art of the possible

Photo of Jing Wu, Principal Research Director, Utilities Research, Info-Tech Research Group


Principal Research Director,
Utilities Research
Info-Tech Research Group

Analyst Perspective

Transitioning to the adaptable utilities of the future, organizations are addressing unprecedented challenges through their Digital Transformation journeys. Data Analytics becomes all that more important to enable utilities to transform digitally. Although the level of maturity varies, the importance of data-driven business decision-making has been well acknowledged and somewhat supported across the utility industry.

The Big Data challenges manifest in the process of developing a data strategy, coined by the characteristics of 3V – Volume, Variety, Velocity and then expanded to 5V, 7V, and the latest 10V. With the advent of industrial 4.0, the amount of data collected by utilities will only grow as more and more IIoT devices are deployed. Utilities are struggling to figure out how to effectively leverage the data. Turning data into actionable insights is a key challenge for utility leaders.

Data strategy development could be time-consuming, and the heavy lifting is mostly building a list of prioritized business cases that truly deliver value. This research report provides utility leaders with a repository of pre-curated utility data analytics use cases and a filtering and prioritization framework. This approach can help fast-track the process of identifying the right business problems to solve strategically.

Executive Summary

Your Challenge

Your organization recognizes the importance of building a business-driven data strategy to enable digital transformation. You are having difficulty in:
  • Building a list of value-driven business use cases required to develop a data analytics program.
  • Justifying the effort and cost to invest in the program.
  • Developing a practical and agile implementation plan.

Common Obstacles

  • Large effort and long duration required to develop defendable use cases.
  • Risks of failure often due to complex and lengthy projects.
  • Uncertainties about how and where to start among the seemingly complex data strategy and data analytics landscape.
  • Ability to demonstrate the measurable business benefits to gain executives’ buy-in.

Info-Tech’s Approach

  • Accelerate your business case development by providing a curated data analytics use case repository.
  • Identify the right data problems to solve that deliver the highest value by leveraging a rapid and effective prioritization framework.
  • Develop an iterative and value proven roadmap based on your organizational scorecard.

Info-Tech Insight

Demonstrating the return on investment (ROI) of data analytics programs is vital to digitally transforming your organization. Empower data-driven decision-making by leveraging Info-Tech's approach to accelerate the process and take advantage of your community.

Data analytics is a key enabler for digital transformation

Utilities today are facing unprecedented challenges that are categorized into the Trifecta of Change in the Info-Tech Future of Utilities Strategic Foresight Report.

Digitally transforming the utility business to strengthen its business capabilities is key to building its resilience. Proactive plans to address the external disruptions and internal interruptions call on actions to enhance foundational technology capabilities.

Digitalization accelerates the risks; expanding attack surfaces.

The enablement of an advanced data analytics platform to support data-driven digital utilities is the foundational step necessary to support any core business transformation initiatives.

Predicted 11.28% CAGR growth (2022-2027)
of big data analytics in the energy sector
[GlobeNewsWire, 2022]

Triangular diagram titled 'Trifecta of Change'. The three points are 'Expectation of regulators and customers', 'Commitment to decarbonization', and 'Risk of reliable infrastructure'.

Data challenges for utilities are elevated during digital transformation

How to process rapid generation?

Existing systems such as billing application is not effective in handling the fast generated data such as the interval data from smart meters.

How to develop actionable insights?

Collecting data is useless unless you turn it into business insights to deliver value.

How to synergize data consistently?

Data that is collected from different sources have disparate meaning and types. For example, a utility customer could mean different things for different departments.

[tdwi, 2017; IOP Conf.Ser.:Mter.Sci.Eng, 2020; IJOIR, 2021]
Circle of 'Big data challenges' beginning with the letter V. On the left from top to bottom are 'Velocity', 'Value', and 'Variability', and on the right from top to bottom are 'Volume', 'Variety', 'Veracity', and 'Visualization'. How to handle the exponential growth?

Number of IIoT devices and field sensors are collecting unprecedented amounts of data. On-premise data storage becomes costly and not elastic enough.

How to harvest different data, integrated?

Utilities are collecting varied data, both internal operational data and external data, but often siloed.

How to gain trust of data quality?

Quality of data could be measured in many ways such as accuracy, timeliness, and completeness, but the effort to track is often insurmountable.

How to present data meaningfully?

The same set of raw data can be presented in many ways. Effective and meaningful storytelling can drive decision making.

Shooting to solve the value delivery challenge should be the priority

A bullseye connected to the 'Value' section of the previous slide's circle. Three bullet points state 'Support business strategy', 'Enable business capabilities', and 'Empower business transformation'.

Read on to learn how to develop actionable insights

Info-Tech Insight

Without identifying the value data can deliver, solving the rest of the data challenges is like getting a hammer without finding the nails first.

Data analytics with value delivered insights is a strategic focus for utilities

Data analytics provide clear measurable value

UK Energy and Utilities measurable value

2015 – 2020 in million GBP

Economic benefits of big data analytics 15,213
Cumulative quality analytics efficiency benefits 2,577
Cumulative economic benefits of data equity 11,906
Cumulative customer intelligence efficiency benefits 3,128
[SAS via Statista, 2016]
[SAS via Statista, 2016]


of utility respondents are making Data-Driven decisions as of 2020 survey.


of Energy and Utilities are monetizing data assets/insights through our products and services.


of Energy and Utilities are quantifying the value of data in our accounting systems.

[Capgemini, 2020]
Regulators are making data analytics a strategic focus

The United Kingdom’s Energy Data Taskforce (EDTF) strategy mandates electricity distribution networks and gas distribution sectors to publish and maintain data to update a Digitalization Strategy and Digitalization Action Plan (DSAP) regularly reviewed by external audits. £1.8 million granted by Innovate UK to support 6 open data and digital projects starting July 2022.

Ontario Energy Board from Canada mandates Ontario electricity and natural gas utilities must provide their customers with access to their energy usage data in Green Button format no later than November 1, 2023.

[Gov UK, 2022; OEB, 2021]

Value proven delivery can drive momentum

Utilities have untapped potential to develop actionable insights with proven values. A 2020 survey of 300 North American water and wastewater utilities reported the following for data collection and effective usage:
20.6% — Much data is being collected and leveraged effectively 4.6% — Some data is being collected and not leveraged effectively 14.6% — Some data is being collected and leveraged effectively 57.4% — Lots of data is being collected and not leveraged effectively
[Black & Veatch, 2020]

Three boxes side by side, labelled 'Proven business benefits', 'High cost and long duration', and 'Complex data landscape'.

Demonstrating business benefits is the biggest challenge for utilities to adopt and implement data analytics initiatives on a large scale. Building a convincing data analytics business case takes much work. Finding use cases that can justify the cost and deliver business values is often difficult. Now, data security is a top risk to manage while navigating through the complex data landscape. [Capgemini, 2017, McKinsey & Company, 2022]

Solving the right problems is the key

Not all data problems are equal and worth solving in delivering business values. Some organizations attempt to solve this problem by spending lots of time and effort in collecting data across the organization from numerous systems in the hope of finding unique insights. Other organizations invest in data analytics platforms in preparation for solving their big data challenges.

A robust and comprehensive data strategy is the mechanism to ensure that data is leveraged effectively to deliver organizational value. The major step to building a data strategy is to build use cases for informing the business case. Collecting data analytics use cases across the organization is a significant endeavor among cross-functional teams.

Prioritization of the use cases can be difficult among many competing priorities. Some use cases could be too large to tackle, and data still needs to be available. The importance of different business drivers for the utility sector can also vary depending on the challenges and strategic timing.

Info-Tech Resources

Utility Data Analytics Use Case Analysis Tool underpins the acceleration

Info-Tech’s Utility Data Analytics Use Case Analysis Tool is a robust, yet scalable Excel-based tool that can be used to help fast-track business use cases’ development and prioritization. This tool can be used in conjunction with the Business Case Workbook to help develop an iterative roadmap deliverable. Features of the tool include:
What is the art of the possible? Tab 2 – Set-up
Tab 3 – High-level use case in-take
Pre-curated utility data analytics use cases that have proven values and implemented by your peers.
Are we solving the right problems? Tab 4. High-level use case output
An effort/strategic Alignment prioritization framework to generate a shortlist of use cases that are either quick wins or strategic initiatives.
How to solve the right problems strategically? Tab 5. Roadmap scorecard
Tab 6. Roadmap Gantt Chart
A balanced scorecard to develop an iterative roadmap grouping uses cases by implementation waves.
See the slides ahead for a full explanation of the tool.

Download Info-Tech’s Utility Data Analytics Use Case Analysis Tool

Sample of the Utility Data Analytics Use Case Analysis Tool.

Assemble a cross-functional team and establish guiding principles

Stock image of a team looking at a whiteboard. Assemble a cross-functional team comprising key business stakeholders, data scientists, business analysts, data architects, and enterprise architects. Focused and deep-dive sessions with these team members can help evaluate the pre-populated utility data analytics use cases and identify the ones that could deliver value and support the strategic direction of the organization. In addition, leveraging Info-Tech’s Data Use Case Framework Template during the sessions can enhance the list by adding specific use cases only relevant to your organization.

Agree on the guiding principles of using this tool:

  • Keep it simple.
  • Develop your copy of the tool, leveraging pre-populated data to optimize for your specific opportunities.
  • Use it frequently; you don’t have to get it 100% right the first time.

Info-Tech Insight

Update your copy of the analysis tool regularly to ensure it always reflects the current status of the roadmap. Check Info-Tech’s online tool for updated use cases.

Items to note

The Utility Data Analytics Use Case Analysis Tool is an interactive and customizable spreadsheet.

Once you’ve noted a few housekeeping items up front, you should find the user experience to be straightforward and friendly.

  1. Mind the cell shading; the colors are essential to a frustration-free user experience. The cells within the tables on each tab have been strategically shaded to help guide the user. Refer to the Cell Shading Legend found on the “1. Introduction” tab of the tool (see screenshot below).
  2. Cell shading legend.

  3. Work within the logic of Formula and Name Manager. This spreadsheet uses a tab “List and Calcs (Hidden)” and Name Manager on its back-end to help analyze data and create outputs. There are mapped values between the “Set-up” tab, “List and Calcs (Hidden)” , and other tabs in this tool. If you customize some configurations, be mindful of keeping them consistent across these tabs to maintain proper tool functionality.
  4. Devote some time to properly fill in the configuration. Tab 2, “Set-up,” is where you can configure the tool to your needs. Spend some time on this tab getting the set-up right before jumping into other tabs. The cost, benefit, risk, and alternative analysis can be done by Info-Tech’s The Comprehensive Business Case Analysis Tool, which is extremely customizable.
  5. Note the instructional call-outs for columns and tables. When you download the tool it will have instructional call-out boxes for many columns and tables. These boxes contain additional information that will make your use of the tool easier. Take note of these instructions before deleting these boxes.

Set up your strategy goals and effort impacting factors and priorities

Organizational strategic goal alignment drives the high-level business value assessment. Assigning priority to each major business driver (Table 1- Organizational Strategic Goals and Objectives, Tab 2) can help identify the right problems to solve that align best with organizational goals at any point in time.

A low priority for a driver or a goal does not mean that it is not important. It could mean that a certain level of maturity has been reached, and it is not currently a focus area for the organization. It is common for utilities to establish strategic goals and objectives in the following seven areas. SMART business objectives can replace the 7 high-level strategic directions if they can better support your data strategy.

High-level effort assessment determines the effectiveness and feasibility of delivering high-level business value. Assign priority to each major factor impacting the effort (Table 2 - High-level effort estimate, Tab 2 ) to deliver value in a timeline manner.

The high-level assessment is based on several factors without getting into a detailed analysis. You can adjust the factors and weights that suit your organization.

Visualization of a balance board with 7 strategic goals weighing down one side and 2 SMART business objectives on the other end.

Start with the pre-populated use cases for the art of the possible

Pie chart of the utility types included in 30 cases. Most are 'Electricity', the 'Water and Wastewater', 'All', and 'Natural Gas'.

Operational excellence

23 use cases across multiple sectors

Customer experience

13 use cases across multiple sectors


14 use cases across multiple sectors
Screenshot of columns B to K on Tab 3 – High-Level Use Case In-take of the Data Analytics Use Case Analysis Tool Column F is a drop-down list that populates the key business capability from the Utility Business Reference Architecture Blueprint Use cases to meet the compliance mandate has to be included in the roadmap regardless of strategic alignment.
Sample of Tab 3 of the Data Analytics Use Case Analysis Tool, with columns B to K: 'Utility Sector', 'Use Case Name', 'Problem Statement', 'Use Case Description', 'Impacted Business Capability Level 1', 'Required Data Management Capability', 'Required Data Analytics Maturity Level', 'Compliance Mandate', 'Business Benefits', and 'Data Source'.
Use the drop-down in column G to select a key data management capability required to support the implementation. Use the drop-down in column H to categorize the required analytics capability maturity level to implement the use case.

Evaluate effort vs. strategic alignment score to generate a shortlist

The purpose of the high-level scoring is not to be prescriptive but rather an approach to discuss with key business stakeholders. Both IT and Business will face the challenges of overwhelming requests to solve various business data problems. This framework intends to balance between the growing business requests and the available resources and funding from both IT and business.

Screenshot of columns L to T on Tab 3 – High-Level Use Case In-take of the Data Analytics Use Case Analysis Tool. Use the drop-down in columns L to R to assess how the proposed use case aligns with the strategic direction of the organization. Use the drop-down in columns S and T to assess high-level effort required to implement the business use case.
Sample of Tab 3 of the Data Analytics Use Case Analysis Tool, with columns L to T: 'Health and Safety; Environment, Social and Governance', 'Operational Excellence', 'Customer Experience', 'People and Culture', 'Business Growth', 'Risk and Resilience', 'Stakeholder and Regulator', 'Problem Statement Readiness', 'Data Readiness'.

Info-Tech Insight

Spending the effort to ask questions to formulate the correct problems to solve is half of the battle to identify the Right Problems!

Identify the right problems with prioritized use cases

Screenshot of columns A to D on Tab 4 – High-Level Use Case Output of the Data Analytics Use Case Analysis Tool

Sample of Tab 4 of the Data Analytics Use Case Analysis Tool, with columns A to D: '#', 'Use Case Name', and an unnamed drop down menu'.

The graphics on this page are for illustration purposes.

Example matrix of 'Strategy Alignment vs. Effort'.

Info-Tech Insight

Only use cases that are in the quadrant of Quick Wins and Strategic Initiatives; these are the right problems to solve for the organization.

Detailed business case analysis for each possible use case

To sequence the implementation of uses cases strategically, detailed analysis should be done by leveraging Info-Tech’s Comprehensive Business Case Analysis Tool to calculate the estimated cost, benefit, net present value (NPV), and risk.

Visualization of an iceberg with 'What organizations need to focus on' above the water and 'What organizations overlook' below the water.

Download Info-Tech’s Comprehensive Business Case Analysis Tool

One caveat to the right problems to solve is the regulatory-mandated use case for utilities to be compliant.

Not strategic to the utilities, compliance data analytics use cases must often be done without any choice.

This type of use case takes time, effort, and funding away from implementing the rest of the use cases. It is imperative to go through the analysis and highlight that in the roadmap.

Create a balanced scorecard

Many factors play an important role in developing the right implementation sequences on the roadmap that suit your organizational needs. The roadmap scorecard needs to more exhaustively and comprehensively capture the cost of ownership of the whole program. It is intended to be a quantifiable framework to help sequence the right problems to solve strategically and to attain success. The following four aspects should be considered as a starting point for your organization. The level represents the importance of the aspects impacting the sequence of implementation.

Level 1 - Regulatory Compliance

Within the utility industry, compliance of regulations can ensure a license to operate. Even if use cases do not support any strategic direction of the organization, the use case implementation is required for compliance.

Level 2 - Cost/Benefit Rankings

The cost/benefit rankings are based on the NPV results from the Cost-Benefit Analysis (CBA). The use cases with high benefit/low cost would have the highest ranking in the shortlist. The ranking of benefits are all relative among the already shortlisted use cases that are strategically important to the organization.

Level 3 - Risk Level/Expected Duration

The risk level is calculated based on the risk factors outlined in Info-Tech’s Comprehensive Business Case Analysis Tool. The use case with lowest risk level should be assigned to the earliest waves. Use cases with shorter expected duration should also be assigned to the earliest waves to demonstrate quick wins.

Level 4 - Data Analytics Capability/Data Sources/Dependencies

The technical considerations influence grouping of the use cases to ensure project efficiency. The plan to develop certain data analytics capabilities through the incremental implementation of use cases is a tactical step to accelerate future use cases implementation. Group use cases that rely on similar data sources can often shorten the development cycle.

Sequence the use cases by adjustable assignment methodology

How to solve the right problems strategically? The answer is part science and part art.

An iterative placement process following four levels of prioritized factors is recommended to decide on the wave of implementation roadmap for the use case grouping. There is no such formula to automate the assignment process. The placements of the use cases should follow the first round of assignment and second round of adjustment iteratively as you work your way through the levels. “Assign, review, discuss, and finalize the sequence” is an important interactive exercise for the team. Depending on the count of the shortlisted use cases, you can start with four waves and divide up waves that contain too many use cases to extend the total number of waves of your roadmap. The color tag represents the assignment done through the consideration of different levels . The arrow represents movement of the placement based on the level of factors.

Iterative placement process for the four levels discussed on the previous slide. Levels 1 & 2 fall into 'Round 1 Assignment' and Levels 3 & 4 fall into 'Round 2 Adjustment', each level has its own Wave, and Use Cases are numbered and have a color-coded icon beside them indicating which level they fall into.

Develop a tactical roadmap that is intuitive, consistent, and defendable;

regardless of the methodology used to develop the sequence.
  • Proof of concept use cases should always be at the very first wave. Ideally, the first wave of the use cases must be quick wins (short expected duration) with a high-benefit rating, low-cost rating, and low-risk level.
  • The Data Analytics Capability helps to group use cases focusing on developing one key technology capability. For example, some use cases require a data lake platform instead of a traditional business intelligence platform. How to strategically implement use cases to gradually enhance the maturity of your data analytics platform is key to accelerating the implementation of future use cases.
  • If the expected duration exceeds nine months, your problem statement is unclear. Consider breaking the problem statement into a few achievable smaller use cases on the roadmap.
  • Consider grouping use cases that require data from the same or similar sources. The focused effort can accelerate the implementation and reduce the risk of running into unknowns.

Sample of the 'Use Case Roadmap Gantt Chart' with columns '#', 'Initiative Name', 'Wave', 'Steering Committee Owner', 'Planned Start: Month, Year', 'Expected Duration', and the roadmap timeline mapped across two years.

Track and measure value iteratively to adapt and scale

Incremental implementation will rapidly provide business value to stakeholders and maintain momentum to achieve the desired outcome. Establishing buy-in and ways to keep business support during the whole project are essential. Measure the return on investment (ROI) of delivered value frequently.

Data Strategy is the roadmap for steering the direction. Iterative and tactical implementation should be able to adapt and scale.

A target surrounded by a semi circle of puzzle pieces labelled 'Establish an internal use case in-take process', 'Info-Tech refresh of the repository', 'Peer connect of lessons learned', and 'Iterative prioritization and adjustment'.

Info-Tech Resources:

Use this utility data analytics use case analysis tool as an input to different blueprints

Use this utility data analytics use case analysis tool

This can be used as a standalone report, or an input to digital strategy, IT strategy, reference architecture and/or more.

Utilities business architecture
Capability Map Key Capabilities Prioritize Capability Gaps

Define your digital business strategy
Innovate the Business Transform Processes Build Customer-Centricity

Build a business-aligned IT strategy
Current State Strategic Initiative Plan Foundational Elements

Future of utilities trends report
Challenges Key IT Element Future Trends

Contributing Experts

Gaudy Jandron
VP Information Technology
EnergyUnited Electric Membership Corporation

Loic Barancourt
Chief Commercial Officer, UnaBiz


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Jing Wu


  • Gaudy Jandron, VP of IT, Energy United Electric Membership Corporation
  • Loic Barancourt, CCO, UnaBiz
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