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.
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.
Use Cases for Utilities
Building upon the collective wisdom for the art of the possible
Principal Research Director,
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.
Your ChallengeYour organization recognizes the importance of building a business-driven data strategy to enable digital transformation. You are having difficulty in:
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)
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]
|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
Read on to learn how to develop actionable insights
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
|[SAS via Statista, 2016]
|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 momentumUtilities 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|
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.
Utility Data Analytics Use Case Analysis Tool underpins the accelerationInfo-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:
Download Info-Tech’s Utility Data Analytics Use Case Analysis Tool
Assemble a cross-functional team and establish guiding principles
|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:
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.
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.
Start with the pre-populated use cases for the art of the possible
Operational excellence23 use cases across multiple sectors
Customer experience13 use cases across multiple sectors
HSE and ESG14 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.|
|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.|
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|
The graphics on this page are for illustration purposes.
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.
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 scorecardMany 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.
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.
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.
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
|Define your digital business strategy
|Build a business-aligned IT strategy
|Future of utilities trends report
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