Energized Data

Energized Data


This is the first of a four-part series on Energized Data, written by Yes Energy’s Director of Data Products, Sonya Gustafson.  In this series, Sonya will explain what Energized Data is, how to create it, analyze it, and empower your organization to use it.

I am the Director of Data Products at Yes Energy and I’ve been working closely with power market data since 2008.  Throughout this time I’ve had opportunities to see data fail, data lie, systems crash, technology change for the good, and technology change for the bad.  I’ve learned that analyses can be wrong and small changes can lead to big results.  For much of my career in power markets, I’ve had the benefit of working for a company that closely partners with its customers to create data and analysis strategies.  On the surface, our data solutions, Data Signals, may look like “standard data aggregation products”.  Most data aggregation products collect data, store data, put it in place for customers to use - and then rinse and repeat.  At Yes Energy we take it a step further and become an integral part of our customer’s data engineering teams.  We value knowing the ins and outs of how data is used, created, and delivered.  Some of our enterprise solutions also allow us to immerse ourselves in the system and data management needs of our customers.  I’ve summarized a number of Yes Energy’s standard best practices in a series of concepts I call Energized Data.

Energized Data is data that is utilized and “powered up”.  This means data is not only collected and reviewed but each row of data is considered, connected, and integrated into the system.  The system is a series of analyses that improves decision-making.  It is your pipeline for moving data through an analysis workflow.  Data that is not energized is not utilized to its full potential.  It’s either collected and forgotten, or collected and used incorrectly or inefficiently.  At Yes Energy, we spend the bulk of our time working with power market data.  An equivalent analogy to non-energized data is like building substations without connecting them to the grid.  Or worse - not even constructing the transmission system - stranding megawatts, isolating the people and industries that need power.  This can significantly impede progress. 

Energized Data Organizations make faster, more accurate, informed decisions.  Energized Data can answer questions like

  • What data, collection method, and tool will help us find the indicators of shortages in supply in the energy markets?  

  • What analysis is most efficient to identify when we are likely to see a forecast error? 

  • How can I combine all of the transmission and price data in nodal markets to best understand what drives congestion price spreads across the grid?  

Each of these questions requires utilizing comprehensive data to solve the problems.  However, successfully answering these questions extends beyond data.  The step of energizing data allows you to bridge the gap between the data collection and finding the solution.   Energized Data is what allows for better efficiency within your organization - enabling repeatable, accurate analysis with defined processes.  This type of workflow allows organizations to scale and move into increasingly complex data problems.

Energized Data is achieved by first identifying the problem or problems you want to solve.  With this in mind, you can then step through data capture and collection.  Often, there is a data engineering and data creation component to turn raw data into information.  Solutions aren’t always found in the location where your data is collected and stored.  There may be a faster, tailored platform.  In this case, a pipeline needs to be created to move data from the location where it is collected and stored to the optimal location where the problem can be solved. This is only possible after discovering the right tool for the job.  

Finally, while automation is incredibly important - you still need a person building the engine to be familiar with the data, the business, and the technology.  A successful, Energized Data Organization empowers its staff and has a culture built around data literacy and learning. While it may feel safe and comfortable to rally around simplicity and “the old way of doing things” – that doesn’t lead to innovation, improvements, or progress.  Creating a data plan is the first step, executing the data plan throughout your organization is the hardest step.      

The next three parts of this series will cover the actions that can be utilized to create an Energized Data Organization.  

  • Data collection, engineering, and creation 

  • Data pipelines and analysis tools

  • Empowering your organization to energize data  

These steps will allow you to avoid some of the biggest mistakes in working with data.  After reviewing these articles you will have a framework and starting point to help you build better data strategies into your organization.

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Sonya Gustafson is the Director of Data Products at Yes Energy. In this role, Sonya works closely with our customers to identify the new data we should collect and the next technology our customers require to utilize data. She's passionate about data engineering and creating actionable data from the data we collect. Sonya's an avid eco tourist who loves experiencing new places with her fly rod.

 

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