What Do Data Quality and Observability Mean in Power Markets?

Understanding Data Observability 

• Data observability refers to the ability to understand the health and performance of your data systems. It’s about having comprehensive visibility via monitoring, alerting, and end-to-end tracking on a data flow to ensure it’s properly functioning. We break down the elements of data observability below. 
  

Data Monitoring

• Data monitoring is the continuous tracking of data flows and processing activities to detect data anomalies, errors, and performance issues in real time.

Data pipeline monitors ensure we have valid baselines throughout history to help track trends and events over time. Much like a control room, the pipeline monitor represents the holistic system and status checks of greens, yellows, reds, and trend lines. It’s the pulse of what is happening in the system right now. The right monitors help identify data-quality problems as they occur, which allows for quick intervention to prevent data issues from escalating.

Data Alerting

• Data alerting represents the automated notification that predefined conditions or thresholds trigger that indicate issues in the data pipeline.

Alerting takes monitoring one step further by proactively alerting the right people to a known issue in real time. 

In fast-paced environments like the power market sector, where you must make data-driven decisions quickly, timely alerts that immediately highlight potential issues are essential. Effective alerts minimize downtime, reduce the risk of incorrect analyses, and help maintain the integrity and reliability of the data pipeline.

The power market industry continues to evolve each year with new reports added, existing reports updated with new fields, and existing reports updated with removed fields – all of which impact data pipelines. A good alert acts as a first line of defense in maintaining data pipeline integrity.

Data Lineage

• Data lineage refers to the documentation, movement, and transformations of data throughout its lifecycle.

Data lineage provides a clear map of where data comes from, how it has been altered, and where it is ultimately stored and used for analyses. 

In the power market industry, we deal with several different data granularities, from five-minute, real-time market data and hourly day-ahead data to daily profit and loss reporting to everything in between. It can be easy to miss changes and interactions that data undergoes along the way. 

Data lineage is essential for troubleshooting, auditing, and compliance.

WIthout the right monitoring, alerting, and lineage in place, there is little hope the resulting data will be high quality. In this next section, we’ll further define what high data quality looks like.

Data Quality 

Data quality encompasses several dimensions, such as completeness, freshness, and correctness. Ultimately, data quality is about ensuring that you can trust the rows of data you consume for an analysis to make informed decisions.

Data Freshness

• Data freshness (or “latency”) is a time measurement between an observation being reported to the time that observation is available to customers.

In its simplest terms, “freshness” answers the question, “Is data coming in as expected right now?” 

The power market industry is dynamic, with real-time conditions having a large impact on effective decision making. Weather patterns, load, generation, unit, and transmission outages change each minute of the day. Having access to the most up-to-date information can mean the difference between capitalizing on or missing an opportunity. 

Having fresh data can lead to better trade execution, optimized strategies, and ultimately, improved profitability.

Data Completeness 

• Data completeness means having all the expected data points within a given period. If you imagine a daily weather report, data completeness would mean having a weather report for every single day without missing any day. If some days are missing, the data is incomplete.

Data completeness answers the question, “Do I have all the data I expect for this time range?” Some data models are sensitive to missing information, which could cause aggregations and analyses to be inaccurate or misleading. Missing values from a particular data stream could lower the correlation coefficients between other data streams when comparing data, which could make a variable seem less important to the final result than intended.

Reported Versus Modeled Data Completeness

When it comes to data completeness, there are two key perspectives to consider: reported data and modeled data. 

In the power market industry, it's essential to first focus on data completeness from the standpoint of reported data. For any data pipeline in your organization or from any data vendor, it's crucial to ensure you have 100% of the available reported data. Without this verification, your analyses may incorrectly fill in data gaps that wouldn't have been gaps in the first place. 

Modeled data should always be a last resort, allowing you to base your analyses on the information available to participants when it was released. 

This approach ensures that your data-driven decisions are grounded in the most accurate and comprehensive dataset possible.

Data Correctness/Accuracy

• Data correctness/accuracy represents the degree to which data accurately reflects the real-world conditions it should represent. It’s the certainty that data is free from errors and represents the truth it’s supposed to model.

You have to make many power market decisions based on publicly available data. Most people consider “correctness” to mean something like, “How accurate is the data?” 

But there are several ways to define “accuracy.” For us, there are primarily two ways to think about accuracy, depending on the type of data you look at:

• Power Market Data: This category represents all the publicly available, factual information reported about the energy markets. For this type of data, “correctness” should represent whether or not the data matches the reported data at the source. In other words, it measures whether we’re consistently capturing all revisions of the data and going back and collecting any data that wasn’t posted originally but was posted later. This ensures you’re making decisions based on what the market saw.

• Modeled Data: This category represents derivative data that is created from other data. This includes things like forecasts and modeled output. Here, correctness should represent how accurate the forecast/model was to actual verified results. In other words, if someone builds a load forecast, “correctness” should measure how accurate the forecast was to the observed values in the market after the fact.

See Eli DeQuiroz, Sr. Director of Tech Operations, explain how Yes Energy ensures better data quality and observability.

Conclusion

We defined our terms as they apply to the power market, outlined the importance of data observability, and showcased the relationship it has to data quality. Collectively, they ensure accurate and reliable data for informed decision-making.

For the remainder of this series, we’ll do a deep dive into each data pillar and cover industry-specific examples across the globe. Subscribe to our blog to get access to each post as we create them!

Learn More 

Power market data collection is a dynamic, complex process requiring constant oversight and proactive management. 

At Yes Energy, our clients make important decisions every minute of every day, and it’s our job to ensure those decisions are based on accurate, timely data. That’s why we have multiple, multi-tiered mechanisms to ensure our data is fresh, complete, and comprehensive. 

Schedule a demo to learn how our tools can support your business needs. 

About the author: Eli DeQuiroz has more than 10 years of experience building and maintaining data-intensive energy market software solutions. The majority of that experience focused on data collection and data engineering of real-time energy market data. At Yes Energy, he oversees the technical operations of all Yes Energy solutions, ensuring the reliability of the applications and data.