This series of blog posts builds on the 2018 CGI Client Global Insights, providing insights into how utilities are making progress toward digital transformation. The findings and perspectives are based on 1,400 in-person interviews with business and IT executives, of which 127 are from the utilities industry. CGI expert Ana Domingues explores the way forward for utilities, highlights the focus of other industries, and shares lessons learned from digital transformation leaders.
The 2018 CGI Client Global Insights reveal that utilities recognize data as the new “digital capital.” Yet, they also indicate that utilities are still in the early stages of exploiting advanced analytics and intelligent automation. What’s holding utilities back from delivering the benefits of data?
Google and Facebook are set to grab 24% of total advertising spend in the U.S., while the share of spending on traditional direct response media like newspapers has shrunk from 38% in 2008 to 14% in 2016. Why is this so? Quite simply, data. Specifically, more than 30 petabytes of user-generated data that is stored, accessed and analyzed to create precision ads and measurable goals.
In the utilities industry, the rapid proliferation of smart devices, rollout of smart meters and deployment of sensors across networks are generating mind-boggling volumes of data. In fact, it’s estimated the number of connected objects will reach 200 billion by 2020, up from just 2 billion in 2006. CGI’s utilities clients recognize the immense potential value of this increasing wealth of data, both from their own systems, as well as from the energy ecosystem. In the 2018 CGI Client Global Insights, 87% of utilities executives ranked analytics as both a top IT and business priority, compared to just 41% in 2016. Most notably, predictive analytics remains a top digital transformation initiative, with 84% of respondents, up from 71% last year. More than 70 percent of executives also have identified it as a top three IT spend driver for three consecutive years.
Emerging trends are creating numerous opportunities to unlock new customer experiences and business models for the digital economy, with advanced analytics serving as the differentiator in achieving operational, commercial and customer engagement excellence. However, data also is a disruptor and can threaten conservative businesses if not harnessed.
So where do utilities stand?
Despite making considerable progress, almost half of utilities are still in the very early stages of discovering the value of data. Last year, 62% of respondents were in the “Explore” and “Design” stages—a number that has decreased to 46% this year. However, analytics represents an immature capability, and utilities still have a way to go in establishing strong business cases to justify enterprise-wide digital strategies. Only 1% report having operationalized advanced analytics, with the remainder still either in the “building” or “being implemented” stages. Even though 87% of utilities executives cite predictive analytics as the number one innovation investment over the next three years, in the short term this falls to 75%. Therefore, although utilities view data as “digital capital” and are driving larger investments in IT, for now they are deferring some of these investments to the future.
Advanced analytics and automation: The business focus
Utilities identify improving the customer experience as a top business priority (88%) and see data as a digital lever for personalized customer engagements. Relevant timely content will drive behavior changes to support value-add services, personalized energy recommendations and cross-selling opportunities. Data is equally key to segmenting customers for targeted offerings and to staying on top of the customer journey across various channels to improve call center traffic and sales. For utilities, these are green field areas, and clients frequently ask me how CGI provides data support to more advanced industries like retail banking and telecoms.
There is an equally strong focus on optimizing operations, for both retailers grappling with shrinking margins and grid operators urged to “sweat” their assets. The latter seek greater visibility of grid load patterns and energy flows for asset performance management (APM), condition-based and predictive maintenance, and mobile field staff monitoring and control, moving toward CGI’s vision of an insight-driven digitally-integrated optimized network utility (ONU).
In addition to driving costs savings, capital deferrals and improved outage management, utilities want to be equipped to handle volatile generation, changing demand patterns and the emergence of flexibility services driven by increasing volumes of distributed energy resources (DERs), as well as edge analytics driven by IoT. On the flip side, because of the current emphasis on digitalizing existing processes, utilities are not rethinking how work gets done or how to adopt an end-to-end processes approach.
Intelligent automation: Grounded in reality away from the hype
Automation is the next natural step to expand the value of advanced analytics to accelerate improvement cycles, develop hyper-personalized marketing and deliver a superior customer experience. Amazon, for instance, changes product prices 2.5 million times a day or once every 10 minutes to drive competition and improve margins. This level of intelligent automation is still far from a reality for utilities.
However, utilities have largely achieved simple automation, based on scripts and workflows, with three-quarters of executives reporting they are implementing or have already implemented it. Their maturity falls dramatically for all other levels of intelligent automation, as defined in CGI’s framework, including more advanced levels of algorithmic automation, machine learning and artificial intelligence (AI). For robotic process automation (RPA), utilities are trailing 20 percentage points behind retail banking for the stages of “implementing” or “already done.”
At present, utilities are beginning with basic bots for administrative-heavy and repetitive back office processes. Chatbots are set to be a key AI focus area for utilities with extensive customer service needs. They help to improve business incrementally by enhancing the customer experience at a lower cost, without the need to put in place a large program. At CGI, we implement chatbots based on a gradual “move” of processes, reducing interactions with physical agents, without damaging the user experience.
Challenges and a future-proof platform
While how to exploit data might be simple to explain, execution is another matter. Once a utility secures its data, the data must be stored and integrated even with external data sources. There needs to be a single repository and a single “version of the truth.” Data also needs to be easily accessible by all users. Utilities often ask me how one can extract value from data and which use cases to focus on. Setting up a sand box to test the value of use cases of multiple integrated data sources shows promise.
Some utilities have built “data lakes” to handle structured and unstructured data (weather, social media, sensors, etc.), as well as ascertain how the cloud fits into the equation. For some, this has resulted in “data swamps” caused by poor data quality, governance and organization, along with the average user’s lack of skills to mine the data, visualize it within a context to extract value, and finally act upon the insight. Added to this, legacy architectures do not support collaboration, leading to problems with siloed applications and data sources, along with poor data quality. Allowing the mushrooming of tools for each use case within siloed units is not the answer either.
Unsurprisingly, utilities continue to report data quality and data management as top challenges to transform, along with cultural change—topics I covered in my first blog of this series.
Utilities still have some ground to cover before they can harness data as “digital capital” to transform and grow. They need a future-proof data analytics platform to enable scalability, agility, and the ability to change quickly, while ensuring data quality and security.
Stay tuned for Part 4 of the series where Ana Domingues discusses the creation of new services and revenues streams, including data monetization and open data platforms for the wider ecosystem.