5 Key Steps for Implementing Data-to-Action in Energy Transformation
With new technologies emerging every day, traditional ways of doing things can become obsolete overnight. However, this doesn’t have to be a bad thing. The world has faced similar renaissances before, and we have adapted successfully because we had a comprehensive plan for what was going to happen next. We’re facing such a transition in the way we power our world. We’re moving away from fossil fuels and towards renewable energy sources, which are projected to make up 27% of global energy demand by 2050. In the face of the global energy transformation, we must take a similar approach to avoid losing time and to achieve the most optimal outcomes from the inevitable changes we will face.
By 2025, 38.6 billion smart devices will be collecting, analyzing, and sharing data. Data can be used to identify areas for improvement, monitor performance, help with future planning, inform decision-making, and prioritize projects. Data about changes in energy usage patterns can help us understand what is happening in our world and where we want to go next. Still, massive amounts of data become useless if we’re unable to make decisions that have a measurable impact. That’s where data-to-action comes in.
Key steps to implementing “Data-to-Action”
Data-to-Action is a framework that helps organizations take data and turn it into actionable insights. The framework is comprised of these steps:
- Data Discovery: Identify what data you have, where it comes from, and how it can be used.
- Data Analysis: Analyze the data to find insights and patterns and identify opportunities for improvement.
- Contextualization: Add context to data sets to establish accurate comparisons, patterns, and predictions.
- Verification: Align decision on efficiency and sustainability to correlate with an overall strategic vision. Organizations can support business cases with data to obtain credibility and build longevity.
- Data-to-Action: Act on the insights learned from analyzing the data. Implement changes or take other steps to improve processes and programs.
How is data transforming energy?
Data analytics and machine learning help the energy industry:
- Increase safety and prevent accidents
- Reduce operational costs
- Optimize processes and increase production
Utility companies use data analytics to identify energy consumption and energy saving to manage power outages, optimize peak times, and set energy pricing. Oil and gas firms use data science to drive refinery and distribution processes and adjust to market demands in real-time. Clean energy companies use data for smart grid management and regulation. Machine learning algorithms are also used for weather prediction and maximizing the efficiency of renewable energy sources such as wind and solar power.
Why you should start acting on your data today
Even with a fifth of the world’s corporations committed to net-zero initiatives, the majority of companies are not taking an active approach to sustainability. Data is fundamentally changing the models of power generation, pricing, and consumption, and organizations that elect to observe the market passively are failing to take advantage of new opportunities. The competitive edge will lie with those that can adapt the quickest and successfully operationalize new technology. Data discovery, contextualization, and verification at scale are the new table stakes for what business leaders need to make smart, data-based decisions–and ultimately allow us to achieve sustainable energy objectives as we face unprecedented environmental challenges.
SparkCognition offers scalable, data-powered solutions that unlock the full potential of energy projects. We’re here to help you meet the future head-on. Contact us today.
