Fill rate over time
Learn about the data in the fill rate over time chart in the DQ dashboard.
Monitor the quality of data in your Reltio tenant on the Real-Time Data Quality (DQ) dashboard. After selecting an entity and attributes on the DQ dashboard, analyze the tenant-level aggregate information about the completeness of your data on this Fill rate over time DQ chart. For more details, see topics Real-Time Data Quality dashboard charts and Analyze DQ dashboard charts.
You can also view anomalies in your data. Anomalies are deviations from the expected pattern in the data. In this chart, you can view data points that are recorded at time intervals. These intervals generally vary but you can recognize changes in the data and identify repeating patterns. Data points not conforming to established patterns are flagged as potential anomalies.
If you're a Data Analyst, Data Steward, or Business User, this information is for you! Use the metrics in this chart to identify where you may need to monitor the fill rate of specific profile attributes, over time.
Data analysis scenarios
Armed with these metrics, what can you deduce about your data completeness?
Look at this data against the standard chart to view the trends over time, so you can see results of changes to your data validations. If the line goes up, the fill rate is improving (which is good).
Data definition
This chart provides metrics for the Fill Rate Over Time Chart.
| Data label | Data definition |
|---|---|
| Time period | Indicates the time frame for the attribute data:
|
| Number of profiles | Identifies the number of profiles that have exceptions to active validation functions defined for the selected Attribute. |
| Today or the date range shown |
Provides a summary of the fill rate for the selected attribute Today:
Note: If the date range changes and the current date is no longer in range, the date range displays instead of Today.
|
| Fill rate over time |
A line graph that identifies the percentage of profiles filled (up to 3 decimal places) for the selected attribute. Each line is a data validation function:
|
No data
You can view at a glance, by hovering over the graph, when data is not available for a particular period.
Anomaly detection
You can view error bands in this chart to determine anomalies in your data. For each of these data points, you can view an error band. An error band provides the upper and lower limit for the trend observed in the data. If the actual values are above or below the limits, it indicates an anomaly or deviation from the expected value. This information helps you resolve issues in your data.
How does this work? Well, the anomaly model uses historical data for a time period to make forecasts or predictions. For example, assume you opted to view data for the last seven days. The data from the start date of the selected time period to the day before the current date is considered to make the forecast.
The actual data is then compared to the forecasts. If the actual data is within the error band or limit, it indicates normal behavior. If the actual data is above or below the limits, it is considered an anomaly.
How does this work? Well, the historical data is used to determine the threshold or forecasts used to determine the data anomaly. The actual data is then compared to the forecasts. If the actual data is within the error band or limit, it indicates normal behavior. If the actual data is above or below the limits, it is considered an anomaly. A sample is given below:
Select the i next to the chart name to view more details about the chart, as shown below:
On the chart, you can hover over any data point to view the anomaly details . A sample is given below:
View this data for a specified time period and select the required time period from the drop-down, at the top right corner of the dashboard. The time series and anomaly data are refreshed to display details for the selected time period.