What is data quality and why is it important?

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.

What is Data Quality?

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.

The main factors that affect data quality

1. Accuracy: Does the data correctly represent real-world conditions? If a sensor reads 0 RPM on a spinning turbine, the data is inaccurate and will mislead any analysis. 
2. Completeness: Are there missing values or entire gaps in the dataset? For example, a two-hour blackout in SCADA logs creates blind spots in performance evaluations. 
3. Consistency: Are data points logically aligned across sources and systems? A turbine labelled "WTG-03" in one system but "TURB3" in another can cause integration errors. 
4. Timeliness: Is the data available when needed? For real-time forecasts and alerts, even a few minutes of delay can degrade performance. 
5. Validity: Does the data conform to the expected formats and value ranges? For example, negative wind speeds or power values usually indicate a problem. 

Poor-quality data often manifests as:

• Outliers that skew results
• Frozen values that go unnoticed
• Duplicate or conflicting entries
• Inconsistent time zones or timestamp misalignments

All of these reduce the confidence that asset managers can have in their analytics, and that’s where the next section comes in.

Why Is Data Quality Important?

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.

Here’s why it matters:

1. Improved Forecast Accuracy
   • Forecasting energy production (wind or solar) depends heavily on historical and real-time data. If the inputs are wrong or incomplete, the model predictions will be too.
   • Asset managers relying on these forecasts to place market bids or schedule maintenance may face significant financial losses if the data is flawed.
2. Better Operational Decisions
   • Machine learning and rule-based algorithms can only recommend what the data shows. Garbage in, garbage out. Low data quality leads to bad recommendations.
   • For example, if performance degradation isn't accurately detected due to faulty data, corrective action might be delayed, resulting in higher losses or even equipment damage.
3. Asset Performance Optimisation
   • Decisions like turbine yaw adjustments, solar panel cleaning, or inverter recalibrations rely on accurate datasets.
   • By ensuring that these datasets are clean, complete, and validated, operators can maximise output and reduce downtime.
4. Regulatory and Investor Reporting
   • Accurate reporting to regulatory bodies or investors requires trustworthy data. Auditing bad data can be time-consuming, expensive, and damaging to credibility.
   • Data quality directly impacts the confidence stakeholders have in your reports.

In short: if you can't trust your data, you can't trust your decisions.

How Can DataTrust Help Improve Data Quality?

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.

What does DataTrust do?

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:

• Outliers that fall outside expected thresholds 
• Frozen values where sensors stop updating 
• Missing or inconsistent entries due to connectivity issues or sensor faults 

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:

• Historical patterns
• Correlated variables
• External data sources (e.g., weather reanalysis or nearby assets)

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:

• How clean and reliable the data is at any given time
• Which timeframes or assets have degraded data
• Trends over time that may indicate systemic sensor issues or maintenance needs

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.

Why It Matters to Asset Managers

With DataTrust:

• You gain confidence in your operational data. 
• You reduce risk by knowing when data is degraded. 
• You unlock more value from AI insights by ensuring the foundations are reliable. 
• And you increase transparency in stakeholder reporting, backed by clear data quality metrics. 

As we like to say, bad data leads to bad decisions. DataTrust helps fix that.

Final thoughts on the importance of data quality

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.

‍