How Energy Analytics Can Help Drive Market Expansion

Energy sources are more diverse than ever. Due to government initiatives, the world is increasing adoption of resources like wind, solar, minerals, metals, hydrocarbons, and more join oil and gas as major contenders in the global energy landscape. 
‍

This growing variety of energy sources presents as many questions as there are opportunities. But the answers tend to be buried in vast, disparate data sources. The solution? A robust energy analytics tool. 
‍

What is Energy Analytics?

Energy analytics enables us to quickly find answers to specific questions and gain otherwise hidden insights from massive datasets. Energy participants and investors can use a combination of energy data analytics software and technologies to perform detailed analysis and answer questions regarding production, reserves, economics, inventory, commercial workflows, finance, and other industry factors. These answers can inform strategic energy business decisions throughout the entire full energy-value chain.

‍

One of the most complex endeavors that analytics for energy data can help drive is market expansion. Successful market expansion depends on our understanding of the energy value chain. 

‍

The energy value chain involves the process of linking specific functions from input to output to increase the economic value of a business through the value chain. This includes data from end-users, utilities companies, and from businesses upstream, midstream, and downstream, for both renewable resources and integrated oil and gas. And investors have specific criteria to meet as well. 

‍

Simply put, that’s a lot of factors to consider when deciding what, when, and where to invest. These decisions should be data-driven and supported by the latest modern energy analytics software and technologies. Energy analytics companies can help accelerate brokerage of diversified acquisitions by providing fast, actionable answers to the most difficult queries surrounding the full energy-value chain. Read on to learn more about the specific ways that advanced energy analytics can help drive market expansion. 
‍

Three Ways Energy Analytics Accelerates Market Expansion

Identify New Potential Energy Assets

Smart sensors and IoT based technologies supply a wealth of massive data streams with real-time updates on factors that will impact how and where various green energy options should be placed. Due to this mounting adoption of smart meters, which enable two-way communication between companies and customers, the global energy and utilities analytics market size is expected to grow from USD 2.0 billion in 2020 to USD 4.3 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 16.3% during the forecast period.

‍

Data analytics for renewable energy integration enables users to quickly and interactively assess the profitability of land using massive multi-sourced datasets representing location, time, landscape, and weather to determine where their customers should make wind, solar, biomass, hydroelectric, geothermal or other renewable energy investments. Vast data streams from sources like thermal infrared, GIS, geological and structural mapping, satellite imagery, Radar, LiDAR, optical remote sensing, spatial data processing, and image fusion help us make assessments to determine which energy asset to invest in.

‍

Energy data solutions enable acquisition and divestment professionals to aggregate, view, and apply thorough statistical analysis to the energy data, and economically evaluate asset acquisition metrics in the market. This provides a clear technological leap forward and clear competitive advantage.
‍

Analyze Existing Energy Assets

How is an existing energy asset performing? If they’re underperforming their estimated potential, why? Is this an easy fix? Or should we divest? What is the most profitable solution? 

‍

These questions are easily answered with the help of big data analytics in energy and utilities. 

‍

An advanced renewable energy analytics solution will provide energy analytics dashboards and integrated workflows that enable users to quickly and easily visualize and analyze relevant data from disparate sources, compare daily asset logs and evaluate production trends, do a quick screen for opportunities, and instantly interact with time-series data in a geospatial big data context to determine productivity drivers, calculate an investment's overall valuation and ROI, and understand benchmark performance.

‍

Energy analytics tools also empower users to conduct large scale database queries in milliseconds, which improves decision making and enables rapid evaluation of diversified energy asset acquisitions that fit investors’ acceptance criteria. Data analytics in energy provides dynamic insights and pinpoint clarity that help engineering teams focus on higher profitable solutions and improve acquisition offer acceptance rates.
‍

A Unified View to Get The Big Picture

The energy data analytics market is segmented and complex, and understanding the state of energy assets and how they are impacted by internal and external factors in such a competitive landscape can be daunting. Relevant data includes production, reserves, economics, inventory, commercial workflows, finance, and more. And this data is flowing from end-users and companies operating upstream, midstream, and downstream, for both renewable energy and natural resources like integrated oil and gas. 

‍

Predictive analytics in renewable energy is a major tool for predicting the success of an energy asset. Advanced big data analytics tools will not only be able to instantly aggregate and ingest these datasets in real-time, but they will also incorporate Machine Learning algorithms to use current and historical data to explain what happened, what’s happening, what will likely happen, and why. These tools will also display this data in an attractive, interactive, digestible, valuable way that enables the average user to easily gain insights and draw conclusions. 

‍

Being able to view all of the data at once in a centralized location makes it easy for users to see how data relates to and affects each other. This is crucial for investors who need to see the big picture before deciding on divestments and new acquisitions. 

‍