Big Data Analytics in Utility Sector



Today, the amount of data being produced every day is truly unfathomable. There are 2.5 quintillion bytes of data created each day. For some perspective, in the last two years alone, 90 percent of the data in the world was generated. Analysts have also estimated that upward of 80% of enterprise data today is unstructured.

And, like any other industry, even the utility sector generates a tremendous amount of data. This huge amount of data brings with it, its own set of challenges.

Two of these major challenges include managing this ever increasing and heterogeneous type of data, that too from multiple sources, and second, to draw meaningful insights from the same.

These valuable insights can be gained by performing analysis in an integrated manner to make more informed operational and strategic decisions and to take well-planned initiatives.

Additionally, this growth is happening in the unstructured form of data as well, thanks to multiple advancements in IT systems, SMS, Social Media, Web portals, etc. To leverage this huge data for better decision making, big data tools are used, and advanced analytics is performed to draw results in a quick manner. This is vital to gain an edge in the current competitive scenario.

Big data management and analysis have crucial implications in the power supply sector as well.

Some of the past use cases that have been implemented in this sector are mentioned in the following segment.

1.Revenue Leakage

One of the use case/application of big data tools in the power sector is the theft detection or revenue leakage.

By analyzing the extreme fluctuations in the meter load, along with the revenue segment of the customers, a propensity score of committing the theft can be generated to find the probability of a customer to commit the theft.

2.Customer Segmentation

Using historical data and advanced analytical techniques a list of potential customers can be built to target them for Solar power, Home automation, ADR (Automatic demand response), etc.

3.Sentiment Analysis

By analyzing the social media activities of the customers, sentiment scores can be derived. These scores can then be analyzed month-wise and they can be either positive, negative or neutral.

These are few of the many use-cases of big data and analytics that Pentation Analytics has worked on, with a major player of the power supply sector in India.

Scroll To Top