How To
Summary
The Data Governance module scans raw time‑series data from the database to detect irregularities or missing values. It classifies data into two main categories: Data Availability (missing data) and Data Quality (erroneous, zero, or constant values). The module provides portfolio‑level charts and plant‑level heatmaps, enabling users to monitor data quality across devices and parameters, enabling effective monitoring and troubleshooting.
Objective
The objective of the Data Governance module is to ensure the accuracy, completeness, and reliability of solar monitoring data. By leveraging plant‑level heatmaps and supporting charts, the module provides visibility into data availability and quality across device classes and parameters. This enables proactive identification of missing or erroneous values, supports effective troubleshooting, and strengthens confidence in operational reporting and analytics.
Steps
Inputs
Please select the plant name from the Plant Search box to proceed.
Please select the required time period using the Calendar Selection option. You may choose from Day, Week, Month, or Custom Date as per your requirement.
By default, charts display the last 1 week’s data for all plants accessible to the account.
Outputs
Portfolio View
Pie chart showing data categories:
Not Available
Erroneous (values outside thresholds)
Zero (during generation hours)
Constant (sensor stuck at constant value)
Normal
Table summarizing Data Quality and Data Availability for each plant.
Plant View
Heatmaps for Data Availability, Erroneous, and Zero/Constant.
Daily percentage of data points per parameter falling into these categories.
Day View
Clicking inside heatmaps for Erroneous or Zero/Constant opens a time‑series chart highlighting errors for that parameter and day.
“View all Parameters” option shows the percentage of failing data across multiple parameters for the selected device.
Additional Information
Technical Definitions for each category:
Data Availability:
For example If the data used for analysis is at 5-minute granularity. For a single day, the expected number of data points would be 288. If the actual data points are less than 288, then (Number of Actual Data points / 288) would be the Data Availability percentage.
Erroneous Data:
For erroneous data detection, we have defined minimum and maximum correlation limits between Inverter Active Power and WMS POA Irradiance.
For example, if the correlation between Inverter Active Power and WMS POA Irradiance falls below 80%, the data will be classified as erroneous.
This approach ensures that deviations from expected performance are accurately identified and flagged for review.
Zero/Constant:
For example If a data point is zero during generation hours (6 AM to 8 PM, as considered in solar projects).
This ensures accurate representation of plant performance.
For constant, calculate the percentage difference between two consecutive timestamps. If the percentage difference is zero, then the count of those is taken to determine the number of constant values. This is also considered during generation hours.
Device‑Level Analysis:
By selecting devices under the General section, users can view data availability metrics for each device and parameter. This enables granular monitoring and supports proactive issue resolution.
Document Location
Worldwide
Product Synonym
Prescinto, IBM Maximo Renewables
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Document Information
Modified date:
08 February 2026
UID
ibm17256069