Digital Transformation is an ongoing process for utilities, but they must focus on technologies that deliver the services customers want to be successful.
FREMONT, CA : Digital Transformation is essential for utilities today. Organizations, on the other hand, frequently find the results of their Digital Transformation efforts disappointing. One of the fundamental causes of this underperformance is a concentration on new technologies instead of consumer requirements.
For utilities, machine learning and data analytics provide a solution. This unique combination can provide utilities with insights that help them better understand their customers. These data can then be used to drive customer-focused Digital Transformation.
Most of the difficulties that utilities encounter regularly are already being addressed by machine learning. Machine Learning models aim to tackle most of the issues related to utility while also generating new economic value.
The cutting-edge models are already being used to understand customers better and solve problems with energy utilities. There are several areas where next-generation Machine Learning models are making significant progress.
Why Digital Transformations Fail
According to a recent survey conducted by Deloitte, 95 percent of energy executives agree that "digital transformation is a high strategic objective." But only 5 percent of businesses say their digital transformation has met or exceeded their goals.
This seems discouraging for a lot of companies. Many hours have been spent looking into the data, and much has been published about the failure.
Let's take a look at two frequent reasons why Digital Transformation attempts fail to meet expectations.
Investing without Understanding Customer Need
An attempt to proceed too quickly through transformations is a common reason for Digital Transformation attempts failing. Many businesses, including utilities, want to rapidly increase their digital capabilities to meet the demand that doesn't exist or had not yet materialized. They take the "if they invest and build it, customers will come" attitude without considering the demands of their clients.
A Slow Acceptance Rate of the Technologies that Deliver Customer Insights
Machine learning and data analytics are two of the popular subjects in the industry right now. Utilities are eager to see how these technologies might add value to the massive amounts of data they already have by providing insights into customer behavior.
Acceptance has been delayed since businesses are either still deciding how to employ these technologies or are in the early phases of putting them in place. There are various causes for this, ranging from technical challenges like data quality issues to a lack of technical expertise within the business.
Machine Learning and Data Analytics are the solutions to the difficulties associated with digital transformation failure. This unique combination enables utilities to comprehend their customers' needs and the measures necessary to fulfil them. Utilities must work to achieve a competitive advantage in a competitive market. They must operate in a focused and customer-centric manner.