> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abisko.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Quality Overview

> Ensuring your performance data is accurate & complete.

## **Data Quality Tracking**

Abisko provides comprehensive dashboards and reports that highlight data quality and completeness for every meter, property, group, and portfolio.

* **Meter-level**: Gaps, data completeness (%), overlaps, monthly & annual jumps
* **Property level**: Issues summary, data completeness (%), data coverage (%), monthly & annual jumps, high & low values
* **Portfolio-level**: Issues summary, data completeness (%), data coverage (%), annual jumps

<Note>
  Data quality insights are included with every reported performance metric to provide users with a clear and immediate assessment of reporting readiness.
</Note>

Behind the data quality metrics is a comprehensive library of advanced algorithms that leverage a multi-tiered, automated analytical process. This robust, flexible system enables Abisko to flag and communicate issues in real-time at the right level for each user.

## Data Quality Calculations

The following general algorithm is used for calculating data quality metrics at the meter, property and portfolio levels:

<Steps>
  <Step title="Daily values are derived from each utility meter entry">
    Analytics break down every single utility meter entry into individual days.
  </Step>

  <Step title="A time-based window is applied to data for each individual meter">
    Data from each meter is included based on the meter’s activation dates and the property’s ownership period, ensuring that only relevant data is used in reporting.
  </Step>

  <Step title="Individual daily gaps and overlaps are calculated for each meter">
    Every missing and overlapping day (gaps & overlaps) between all entries for each meter is identified and indexed over all time.
  </Step>

  <Step title="Monthly values for consumption, gaps, and overlaps are calculated for each meter">
    Individual daily values are aggregated to provide monthly consumption, gap and overlap totals for each calendar month to normalize staggered utility data entries.
  </Step>

  <Step title="Secondary data quality metrics are calculated for each meter">
    Normalized consumption and gap values are analyzed to determine monthly data completeness (%) and monthly jumps.
  </Step>

  <Step title="Rolling 12-month period data is aggregated for each meter">
    Monthly normalized values are aggregated to provide annual consumption and data completeness (%) values for every reporting period.
  </Step>

  <Step title="Property-level data quality metrics are calculated.">
    Monthly and annual consumption and data quality values are aggregated across all meters of a property by utility type and subtype (e.g. Total Energy, Electricity, Fuel, DHC, Direct Emissions Indirect Emissions, Water, Waste, etc.)
  </Step>

  <Step title="Values are further rolled up into portfolio-level insights.">
    Monthly and annual consumption and data quality values are aggregated across all properties in a portfolio.
  </Step>
</Steps>

***

## Learn More

<CardGroup cols={1}>
  <Card title="Data Completeness" icon="check-double" href="/data-quality/data-completeness">
    Understand the extent of missing utility data over any reporting period.
  </Card>

  <Card title="Gaps, Overlaps and Jumps" icon="circle-xmark" href="/data-quality/gaps-overlaps-jumps">
    Identify specific periods of missing data to take targeted action.
  </Card>

  <Card title="Outliers & Benchmarks" icon="ruler-vertical" href="/data-quality/outliers">
    Identify properties that have unusually high or low intensity values.
  </Card>

  <Card title="EXCEL Reports" icon="file-excel" href="/data-quality/excel-reports">
    Export reports that detail data quality issues across a portfolio.
  </Card>
</CardGroup>

***