Monitor Data Quality with SAP Reltio

Get a clear view of your Data Quality

Maintaining high-quality master data is essential for reliable business processes and informed decision-making. The SAP Reltio Data Quality Dashboard provides a central overview of the quality of your master data, helping users identify inconsistencies, monitor data completeness and detect potential data issues.

With both entity-level metrics and detailed attribute-level analyses, users can gain deeper insights into their data, understand where improvements are needed and track data quality over time.

Entity-Level Data Quality Overview

The main dashboard provides a high-level overview of the selected entity type and its data quality status.

Users can monitor several key indicators, including:

• Active Profiles: The total number of active master data profiles.
• Inactive Profiles: The number of inactive profiles.
• Attributes: The total number of attributes defined for the selected entity type.
• Source systems: How many source systems have contributed data to this entity.
• Consolidation Rate: Is the measurement of the total number source profiles versus the number of SAP Reltio profiles after removing duplicates. It helps to understand the data reduction achieved by matching. 
• Profiles with Invalid Data: Show the number of profiles that violate active validation functions.
• RDM Value Mapping Status: The relative number and percentage of reference data values that have been mapped to canonical values and transcoded for use by Reltio Reference Data Management (RDM). Mapped values are values that have been mapped and transcodee, unmapped values are values that have not been mapped and transcoded.

The dashboard also provides a list of all available attributes for the selected entity type, giving users an overview of the data fields that can be further analysed.

Each graph in the entity-level dashboard can also be exported as a CSV file, allowing users to download the data for further analysis and reporting.

Attribute-Level Data Quality Analysis

Beyond the general entity overview, SAP Reltio allows users to investigate the quality of individual attributes in more detail.

By selecting an attribute from the attribute list, users access a dedicated analysis view with several charts and metrics. These provide insights into data completeness, validity, uniqueness, value distribution and source system contributions.

Users can explore the following analyses:

• Attribute Summary: Provides an overview of the selected attribute, including the number of active profiles containing it, its data type and properties such as whether it is required, included in a match rule or has validation functions.
• Fill Rate: Shows how many profiles contain a value for the selected attribute and how many are missing one. Users can select either segment to view the corresponding profiles in Advanced Search.
• Fill Rate Over Time: Tracks how the percentage of profiles containing a value changes over time, helping users monitor improvements and identify unusual trends.
• Profiles with Invalid Data: Shows the active Data Validation Functions (DVFs) defined for the attribute and the number of profiles failing each validation rule. Users can drill down to the affected profiles in Advanced Search.
• Profiles with Invalid Data Over Time: Tracks the number of profiles failing at least one active validation function over time.
• Uniqueness Analysis: Shows the distribution of unique values, which appear on only one profile, and non-unique values, which appear on multiple profiles.
• Uniqueness Analysis Over Time: Tracks how the proportion of unique and non-unique values changes over time, providing insight into the impact of data cleansing and deduplication activities.
• Frequency Analysis: Displays up to 100 of the most frequently occurring attribute values. Users can select a value to view the profiles containing it in Advanced Search.
• Source System Analysis: Shows which source systems contribute values for the selected attribute and how many profiles are associated with each system. Users can drill down into profiles from a specific source system.
• Length Statistics: For String and Blob attributes, shows the distribution of values by character length, including the minimum, average and maximum length.

For analyses that display historical trends, users can adjust the time period to review data over the last 7 days, last month, last 6 months, last year or a custom date range. This also allows them to identify trends and anomalies over time.

Each graph in the attribute-level analysis can also be exported as a CSV file, allowing users to download the data for further analysis and reporting.