Big Data and its commercial value to organisations is transforming industries across the globe, including the utilities sector. With the buzz surrounding data management and the high, strategic stakes involved, utilities firms continue to struggle to unlock the full potential of data analytics. To support executives with seizing the opportunity, PA Consulting Group has developed a roadmap for successfully implementing and embedding utility analytics within the organisation.
The energy and utilities market is currently undergoing massive change, driven by among others regulation, changing customer demands, a transition to green energy and the rise of new technologies. One of the largest trends impacting the sector is Big Data – large volumes of data from customers, internal operations and value chains – which when analysed successfully can translate to tangible improvements in performance. A research from IDC Energy Insights conducted last year highlights the rapid rise of Big Data up the management agenda in recent times: more than 90% of executives indicated they at the time adopted tools & techniques to unlock value from data, up from below 20% five years ago. And looking ahead the phenomenon is only expected to gain ground, with 45% of the respondents indicating that by 2025 more than 10% of their company’s revenue will be driven by data-analysis.
Benefits for utilities
The application of analytics can create significant benefits for utility organisations. This includes not only commercial opportunities through better customer’s conversion and client service as outlined above, but also potentially massive savings to operations, ranging from lower run and maintenance costs, improved asset and load management, reduced outage frequency and more efficient back-office functions. The benefits can be significant – another study from GTM Research reveals that the ROI multiplier of analytics typically ranges between 2x to 12x ROI. While returns will vary across companies and functions, due to differences in among others maturity levels, networks and in-scope segments, the researchers state that with relatively confidence healthy returns can be realised across the globe. There are examples of utilities recovering half the costs of their smart grid programs by detecting and preventing energy theft. Other companies are reporting improvements in service reliability of over 35%, enabled in part by the deployment of sophisticated analytical capabilities, while also sustainability is a domain where analytics is making a large difference.
Against the backdrop of the massive potential of and attention given to Big Data, bottom-line results can be labelled disappointing. Many utilities discuss and draft Big Data strategies, yet fail to (successfully) execute the strategy, others do manage to execute a large share but extract too little throughout the process, and overall organisations tend to focus on the transactional nature of data analytics, leaving the more strategic and high-value areas untapped. In other words, utility managers are nowhere near unlocking the full potential of data, in part the result of factors such as funding constraints and organisational limitations, but also for a large part the result of a lack of knowledge, expertise and experience with tackling a still relatively greenfield terrain.
Utility analytics roadmap
For utility executives planning to implement analytics, or even embark on a complete analytics transformation, PA Consulting Group has developed a roadmap that can guide them through the complex, arguably even daunting challenge. PA has over 30 years of experience in the energy and utilities sector, and specialises on topics at the intersection of business and IT. Based on its experience and hundreds of client engagements, the firm has developed a 6-step methodology from strategy to implementation and post go-live management.
The first phase of the methodology is to complete a holistic assessment of the value and potential scope of analytics. This first phase should examine current applications as well as data availability and quality across the organisation. Once this is complete, utilities can look across various application domains such as customer analytics, grid analytics, reliability analytics etc. to assess their existing capability and determine which areas should be considered. To support clients through this phase, PA provides an inventory of key areas which can serve as a reference list against which utilities can conduct a gap analysis and maturity assessment. This will then provide a view of the most interesting opportunities.
With a list of potential improvement areas in hand, the next step is to map and prioritise them. PA’s methodology recommends doing so across two core dimensions Return on Investment (ROI) – in essence the value an initiative will add – and Ease of Implementation (EOI) – the degree of difficulty to execute the transition, also typified as the ‘cost’ of implementation. Using the ROI and EOI dimensions, initiatives can be plotted in a matrix, quickly highlighting which projects should be undertaken immediately (high value; low effort), which ones should be ‘killed’ (low vale; high effort) and those situated in between.
An illustrative plot of Return on Investment and Ease of Implementation.
Through the identification of quick-hits, and targets in the ‘Potentially’ and ‘Deploy’ range, utility firms can in a relatively easy manner unlock value. The result is a list of projects that will likely be rolled-out, and what remains is the translation into a multiple year planning document. To do so, a variety of new internal factors come into consideration, such as availability of quality data resources, capabilities, current commitments within the organisation and organisational impact. Incorporating these factors leads to a roadmap including a time horizon, from short-term implementation (often quick-hits) to long-term planning.
Now that project selection and planning has taken place, further detailing the business plan is the next step. As the ROI simply gives an indication of an order of magnitude of benefits for planning purposes, the projects also need to undergo examination of the detailed benefits, costs and risk identification, and implementation planning. In this phase, managers should take the benefits – both tangible and intangible – and costs associated with implementation into more scrutiny. In the case of costing, an integral approach is advised, with process establishment, FTEs, change management, program management, benefits realisation and IT-costs typically serving as the main components. The risks also need to be carefully identified, managed and mitigated.
On the back of a detailed business case, project teams can start drafting detailed requirement plans. When doing so, it is important to involve all those who benefit from the analytics solution, adding that requirements should be inventoried holistically, from a people and organisational viewpoint to IT and culture. Key is that requirements are fed back into the business plan, and project plan, as well communication of the requirements to relevant stakeholders, such as directly impacted individuals and their superiors.
Following a thorough preparation and planning period, utility analytics concepts are now ready to be implemented. The implementation phase is by far the most intensive, and can range anywhere between 6 to 8 weeks for the first killer cases and 2 to 3 months to a number of years to build the data driven organisation. End-goal is implementing the new solution and process, including embedding the new way of working within the organisation. In this phase PA Consulting highlights a number of key focus areas, to an extent similar to standard project and implementation management. Although specific considerations should be taken into account, the deployment of utility analytics in some areas requires a different process than that used for traditional software and solutions. Prior to embarking on the roll-out, project managers should ensure they have a solid implementation plan in place, an approach for change management and up-and-running program governance.
Once projects start running, the so-called ‘benefits realisation’ phase kicks-off. The key objective of this phase is to identify, track and realise benefits associated with the utility analytics agreed in the business plan. These benefits typically found in the utilities sector can be divided across eight areas:
- Grid – both avoided cost and lower capital spend on equipment and reductions in operational costs
- Voltage – better visualisation and management can boost voltage issues on the network
- Customer – theft recovery, and revenue assurance as a result of better processes such as meter to cash to prevent leakage of revenue
- Reliability – the reliability of the network will benefit from better visualisation, optimised planning of solutions and management
- Load – insights in load can improve capacity management and trigger right sizing in people/equipment
- Asset – enhanced management of assets and setting a base for predictive maintenance
- Work – integrated data combined with works information will ensure better scheduling of crews for planned and unplanned work optimising operational cost and increase productivity
- Enterprise – enhancements to reporting and analytics across customer, financial and HR functions will improve decision making
When setting the benefits, it is imperative that they are set up in a detailed and SMART (Specific, Measurable, Achievable, Realistic, Timely) manner. In addition, benefits should undergo a rigorous system to check with process owners and finance to ensure there is ownership to follow-up. With the benefits unravelled – performed typically parallel to phases 4 and 5 – the next step is to establish a plan and mechanism to track and bank these (and their associated costs) throughout the deployment. For this an effective governance structure is required, including management of benefit owners, as well as clear metrics and targets supported by a reporting/dashboard mechanism.
As initiatives will run in parallel, so too will the benefits realisation phase. Key is that the infrastructure is in place as soon as the first benefits are being banked. Early benefits help build momentum for the program. Proven early success will also create trust from stakeholders and may help secure funding for subsequent deployments.
By applying the roadmap, PA Consulting believes utility executives have a “significant” edge in bolstering the success of their analytics ambitions. “The value of data is unlocked by making small steps against a vision of a data driven future of your organization”, conclude the authors.