In today’s renewable energy sector, data drives every critical decision — from forecasting energy production to scheduling preventive maintenance and ensuring regulatory compliance. But not all data is created equal. Poor-quality data can undermine even the most advanced models and lead to costly mistakes.
That’s why understanding what data quality is and why it is important is essential for every asset manager in the wind and solar industry. In this blog post, we’ll break down the concept of data quality, explore why it matters so much for renewables, and introduce DataTrust, Enlitia’s algorithm for ensuring data integrity.
Data quality refers to how suitable the data is for its intended use. In other words, is the data accurate, complete, consistent, timely, and reliable enough to support trustworthy insights and decisions?
In renewable energy, data comes from multiple sources — SCADA systems, weather forecasts, sensors, maintenance logs — and it’s often used to feed machine learning models that predict failures, calculate energy forecasts, and detect curtailments. If the data is flawed, the outputs will be too.
Poor-quality data often manifests as:
All of these reduce the confidence that asset managers can have in their analytics, and that’s where the next section comes in.
High-quality data is not just a “nice-to-have” — it’s foundational to the renewable energy sector’s ability to operate efficiently, profitably, and sustainably.
In short: if you can't trust your data, you can't trust your decisions.
That’s exactly why Enlitia developed DataTrust, an AI-powered algorithm designed specifically for the renewable energy sector. DataTrust ensures that every insight generated by our platform is backed by high-quality, validated data and makes that quality visible and measurable for asset managers.
1. Cleans and Validates Wind and Solar Data
At its core, DataTrust runs continuous checks on incoming datasets from wind and solar farms to detect common issues, such as:
Once identified, these issues are flagged — and when possible, automatically corrected using intelligent algorithms.
2. Fills Gaps with Smart Synthetic Data
When DataTrust detects missing values, it doesn’t guess. It reconstructs those points using:
This ensures continuity in datasets without introducing random or misleading values.
3. Tracks and Scores Data Quality Over Time
One of the most powerful features of DataTrust is the Data Quality Index. This metric provides asset managers with a clear, quantitative view of:
This visibility means that whenever an insight, forecast, or alert is generated, the asset manager can see the associated data quality score — and decide how much to trust it.
4. Generates Synthetic Values Where Needed
Rather than simply removing corrupted or missing data points, DataTrust applies logic and machine learning to generate synthetic values that reflect the most probable, realistic scenario, maintaining data continuity and usability for forecasting and optimisation algorithms.
With DataTrust:
As we like to say, bad data leads to bad decisions. DataTrust helps fix that.
The renewable energy industry thrives on data, but only when that data can be trusted. By addressing data quality as a first-class concern, Enlitia empowers asset managers to make better decisions, operate more efficiently, and deliver stronger results.
Whether you're optimising daily operations, forecasting next week’s output, or preparing reports for investors, DataTrust ensures the insights you act on are backed by reliable, validated data.
Want to learn more about how DataTrust can elevate your data pipeline? Schedule a Q&A session with our team or explore our platform today.