Tom Siebel, CEO of C3.ai, weighs in on a data-driven, strategy-first approach.
“Putting the cart before the horse”, a cliché’ we inherited from the Greeks, means that the action that should come second is prioritized first.
This approach is all too common among business leaders who hear about the benefits of digital transformation, become fixated on specific transformation-enabling technologies (like the Cloud or AI) and look for ways to implement them into their current business model. We put the cart before the horse when we fail to see the big picture and put technology before strategy.
To be successful, digital strategies must extend beyond IT to encompass people, process, technology, business models, culture and a strong collaboration among partners.
A Starting Point
In a recent interview with Tom Siebel, CEO of C3.ai and author of the new book, “Digital Transformation, Survive and Thrive in an Era of Mass Extinction,” he discusses building strategies around big-pain, high-value, 6-month problems (use cases).
“Utilities should start by identifying important problems that need to be solved like improving predictive maintenance, reliability, safety, profitability, revenue protection or customer satisfaction,” he says. “They should then use analytics to distill these problems down to those that are most important and can be solved within a 6-month timeframe, like revenue protection and customer satisfaction.”
Elaborating on customer satisfaction, he recommends analyzing customer satisfaction levels. Where they are suboptimal, he suggests exploring what is negatively affecting them—outages, prices, services or bill confusion, for example. Utilities that find the causes of the problem should focus on making improvements that will impact customer satisfaction within a 6-month timeframe for quick, impactful wins.
In his book, Siebel names four technologies that enable digital transformation: elastic cloud, big data, AI (artificial intelligence) and IoT (internet of things). Each play a role in the collection, storage and/or processing of customer data. It’s important to understand the high-level, synergistic roles of these technologies:
- The elastic cloud stores and processes big data through universal access to unlimited amounts of storage capacity and computing resources on a pay-for-what-you-use basis. “Without cloud computing,” says Siebel, “Digital transformation would not be possible.”
- Big data, a term for extremely large data sets, are analyzed computationally to reveal patterns, trends, and associations. “Data, of course, have always been important,” Siebel writes, “But in the era of digital transformation, their value is greater than ever before. Many AI applications require vast amounts of data in order to ‘train’ the algorithm, and these applications improve as the amount of data they ingest grows.”
- AI, which includes machine learning and deep learning, makes machines and computer programs capable of learning and problem-solving in ways that historically require human intelligence. Fraud detection, predicting equipment failure and decision-making support are some examples.
- IoT, the largest source of “big data”, collects and delivers data to utilities from sensors on equipment and smart devices with adequate processing and communication capabilities. These include smart home appliances, HVAC systems, transformers, and power lines. According to McKinsey Global Institute, 127 new IoT devices connect to the internet every second which means that big data will just keep getting bigger. “The real power and potential of IoT derives from the fact that computing is rapidly becoming ubiquitous and interconnected…” writes Siebel. “As a result, cloud computing is effectively being extended to the network edge-i.e., to the devices where data are produced, consumed and now analyzed.”
Duke Energy’s SmartGen Program
Duke Energy is one of the companies that Siebel named as leading the industry in digital transformation. Its SmartGen program is an example of these technologies in action.
After losing a reported $10 million because of a major power failure, the company undertook a large initiative to improve equipment and infrastructure. A sizeable investment was made in online sensors, data management infrastructure, and equipment health and performance monitoring. They also developed monitoring, predictive analytics, and diagnostics infrastructure. According to Industrial IT, the utility now has access to:
- Remote equipment monitoring;
- Smart diagnostics and prognostics;
- Data integration & visualization;
- Enhanced reliability process (consistency across the company); and
- Zero event operations (safety and environmental).
“With over 20 billion internet-connected smart-phones, devices, and sensors generating a stream of continuous data at a rate of zettabytes a year and rapidly growing – one zettabyte is equivalent to the data stored on about 250 billion DVDs – it is now possible for organization to make near-real-time inferences based all available data,” writes Siebel.
The Risk of Missing the Boat
Digital transformation is not a “nice to have” for utility companies, it’s a need to have. Research shows that this current era of digital transformation marks a period of extinction for incumbent companies in all sectors. According to Siebel, “Digital transformation indices are cropping up everywhere to capture how prepared (or unprepared) CEOs and their companies are.”
Like “putting the cart before the horse, “missing the boat” is a cliché that illustrates a major misstep for companies, those that fail to act in a timely fashion. “Once you miss the boat, it gets harder to catch up,” writes Siebel. But, developing strategies based on problems exposed by data analytics is clearly a good place to start.
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