3 powerful ways a data model can benefit energy and utilities

Analytics Market Manager - Energy & Utilities, IBM

As analytics capabilities continue to advance and new tools and solutions provide additional ways to pull data from different sources, the need to understand the data—and the relationships among data—increases. This need is where data models come into play.

An industry data model provides a glossary of requirements, terms and concepts that can be clearly understood and communicated by both business and IT professionals, thereby helping to accelerate project scoping, appropriate reporting, data quality and data requirements and identifying sources of data. Ultimately, it acts as a blueprint by defining the structures necessary to build an effective data warehouse and provides managers with critical prebuilt reporting templates that offer a wide and deep view of their business through key performance indicators (KPIs) and other measures.

Like businesses in most industries, Energy & Utilities organizations are accessing more data than ever before, requiring solutions like data models that provide a way to synthesize, analyze and integrate data so it can be used to improve operations.

Powerfully leveraging data models

PPL Corporation offers a great example of an energy and utilities company that is using data models to derive high value from data. One of the largest investor-owned utilities in the US, PPL provides electricity for 10.5 million customers in the US and UK, and employs approximately 13,000 people. Over time, PPL had assembled multiple data warehouses to manage data gathered from 1.4 million advanced meters that were installed in customers’ homes and businesses to capture readings at regular intervals during the day.

PPL faced a data management problem. The amount of data being collected was growing explosively, and a new solution was needed for managing and integrating the data. The company turned to IBM for help transforming its data warehouses into a fully integrated data model that would provide flexibility for incremental expansion. Here are three of the most powerful ways PPL—and other energy and utilities organizations—can leverage data models.

1.  Manage enormous amounts of data in a scalable format

Perhaps the most significant benefit of data models is the ability to take streams of seemingly unrelated data and map it in a way that clearly depicts the relationships that exist among them. When you add integrated reporting and scalable flexibility, businesses are able to make smarter decisions quickly. The IBM Data Model for Energy and Utilities is helping PPL manage an enormous amount of metering data and turn it into valuable insight.

2.  Improve reliability while reducing operating costs

Data models are scalable, allowing for incremental expansion to remain on par with organizational growth over time. Having one solution that can adjust to business needs provides increased reliability at less cost than would be required to upgrade and replace outdated systems repeatedly. In PPL’s case, the targeted infrastructure investments allow the company to optimize reliability, reduce costs and provide enhanced levels of customer care.

3.  Predict behavior that leads to customer churn

When paired with other predictive analytics solutions, data models can identify at-risk customers and anticipate when they are likely to have difficulty paying their bills. Being able to predict payment behavior allows organizations to focus on accounts that are likely to fall into the collections process and stop customer churn before it happens. Within two years, PPL was able to predict at-risk accounts with 99 percent accuracy. This capability enabled it to identify, reach out to and support 5,400 accounts that may have otherwise been lost.

Intelligently providing enhanced value

See how PPL obtained real value with IBM Predictive Analytics:

Learn more about PPL’s success and take the Energy and Utilities Advanced Analytics Assessment to find out if your organization has an opportunity to harness the power of big data. 

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