Data Lake

How Utility CIOs Can Avoid Letting Valuable Energy Data Drown In The Data Lake

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As the volume of consumer data increases across global enterprises, advanced analytics is becoming the core of all corporate activities that empower companies to make smarter business decisions. MicroStrategy’s 2020 Global State of Enterprise Analytics Report (via Forbes) reveals 94% of enterprises globally agree that data analytics is essential for business growth, and more than half of all enterprises are already using advanced and predictive analytics to streamline operations.

There is also a heavy uptick in investments to democratize data-driven activities across all organization functions, with 65% of companies planning to increase their analytics spending in 2020. As evident across all industries, data intelligence can no longer be merely considered as a “nice to have” but rather the foundation for how a company runs all aspects of its business.

Similar investments into big data have shaped the utility sector over the last several decades, as the industry spent billions of dollars on infrastructure improvements, including the deployment of smart meters, meter data management, and CRM and CIS systems. Today, progressive utility chief information officers (CIOs) are further investing in data lakes, a repository of data in its rawest form. As access into greater data insights grows, along with the universal trend toward digitalization, there is a new urgency for utilities to integrate enterprise analytics into real-time strategies that use data to generate value for both their businesses and customers.

Data Without Personalization Is Just Data 

While big data techniques allow for efficient processing of large datasets, much of the data can remain underutilized due to insufficient layers of analytics and personalization support. Data analysts are often hired within utilities to run queries and find anomalies, but the immense depth of these data lakes leave data science teams overstretched across departments and inadequately resourced, forcing utilities to rely on a spot solution approach that addresses only one area of the organization.

Without the right machine learning techniques and resources, data analysts currently rely on generic algorithms available through standard anomaly detection packages, losing 50% or more of data insights along the way. This is where enterprise analytics comes in. Enterprise-class analytics solutions are designed to alleviate load constraints by deploying strategies that repeat the same queries and use cases that are common across the industry to satisfy 70% of the questions and challenges most utility and energy retailers face. Then, the remaining 30% is configured for customized solutions and requires the least amount of human and manual effort.

Using enterprise analytics and artificial intelligence (AI) that personalizes consumer data, utility CIOs can create an integrated ecosystem that effectively disseminates actionable intelligence throughout the entire organization and eliminates siloes created by inefficient spot solutions.

For example, when applying AI and enterprise analytics solutions to build personalized customer profiles based on meter data from individual energy consumption, we not only can detect which households have electric vehicles (EV) but also what time of day the person is charging and how much power they draw each time they charge. Based on this information, the utility can make changes that both enhance customer engagement and improve operations. On one end, they can deliver targeted marketing to this household for EV-specific programs. Internally, they can better address grid imbalances by planning for predictable load interruptions.

Another example we see is within utility call centers. Personalized energy profiles can be used to proactively inform customers of their energy usage, and also as a useful tool for customer service representatives. For example, utilities can send customized notifications alerting customers of potential increases in their bills, often resulting in a reduced number of incoming calls to call centers.

Personalized energy profiles also give utilities accurate and timely information to improve overall demand-side management (DSM) planning, time-of-use education and neighborhood comparisons. All of these use cases, among many others, give utilities a 360-degree view of their customers to help drive more strategic decision-making, all while providing customers with a greater relationship to their utility provider.

Enterprise Analytics And The Value Of Customer Intelligence

Comprehensive and customizable solutions are already being successfully implemented among nearly every industry to track customer touch points. Salesforce, for instance, provides an integrated customer interaction platform with a predesigned application that can be used across millions of organizations worldwide. Widespread adoption of Salesforce comes from its ability to streamline a company’s operations while still offering customized integration for each end client.

Similarly, utilities can seek enterprise analytics platforms that offer a universal approach to data analysis that equips them with the means to hyperpersonalize their customer interactions. This could include personalized energy-saving tips or rebate promotions based on a customer’s actual energy consumption, for example, delivered via the customer’s preferred communication channel.

Utility CIOs should seek solutions that integrate directly with a utility’s existing CRM solution or call center provider to disperse data insights across multiple divisions within their organization to drive their everyday work processes. Adding an enterprise analytics solution forms a bridge between third-party integrations and APIs and datasets with minimal effort on the part of the utility.

One of the largest challenges faced by utility CIOs when implementing an enterprise analytics platform is around data access. Look for enterprise analytics platform providers that work directly with smart meter providers to bundle hardware and software solutions. This way, the enterprise analytics provider can access the meter data already formatted from the meter data management system rather than CIOs needing to configure and normalize meter data via internal data lakes. This can save anywhere from three to six months of implementation time.

For a utility CIO, enterprise analytics solutions are an extension to traditional data lake infrastructures that help their teams leverage the value of customer intelligence in an efficient and productive way. Energy insights become more valuable, as utilities can quickly recognize and understand consumption patterns within existing applications.

With the potential to transform information into actionable insights, energy data is the focal point between a utility, its customers and its ability to successfully evolve in the face of complex information demands both within the industry and in society.

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