Dashboard

Data Quality insights are defined based on the data available in your tenant.

Dashboard Features

Data Quality (DQ) Dashboard, displays the following information and options:

  • Last update date and time - indicates when the Data Quality Score was refreshed.
  • Number of entities, Recently updated entities, Data Quality health meter, and Completeness health:
    • Number of entities - indicates the total number of entities at the tenant level and the entity count on the Entities page
    • Data Quality health meter and completeness health percentage define the performance of the entity.

  • You can compare and perform timeline analysis of the Data Quality Score. This in turn helps to review the improvement in the Data Quality Score or any activity that impacts the scores. The following charts appear on the dashboard:
    • Line chart for Data Quality, number of entities, completeness, and recently updated entities. The Data Quality graph displays the historic Data Quality as well as a comparison of Data Quality across entities. You can select the timeline (month, three months, year, and so on) to view the Data Quality Score progress.
    • Data Quality Score distribution graph - this gives you a graphical representation of the number of entities per Data Quality Score.

    • Heatmap chart - graphical representation of data where the count of entities contained in the matrix is represented as colors. For example, Rank vs Data Quality chart.
  • Refresh button - refreshes the Data Quality Score if recalculated before the scheduled time. The time involved depends on the number of entities updated and/or the configuration changes.

Understanding the Rank vs Data Quality Heatmap Chart

In the above figure, Rank and Data Quality are the entities based on which the Heatmap Chart is derived. Where,

  • Rank - calculates the relative importance of a profile to other profiles in your tenant
  • Data Quality - measures the quality of a profile relative to other profiles in your tenant. This is calculated using default out-of-the-box rules and evaluated for completeness and recency.
Note: The heat map can also be drilled down to the actual profile in the MDM UI.

The table above explains the quadrants of the chart with respect to the number of entities.
  • The quadrants assist in quickly identifying the high-value profiles. On clicking the entities, you are taken to their respective profiles.
  • Upon fixing the data quality issue, these profiles can deliver the most value in the shortest amount of time.

Displaying Nested Attributes

Click a particular entity type on the dashboard and view the following four facets displaying details of its simple and nested attributes. Note that only those attributes that are included in Data Quality calculation, are displayed on the four facets.

  • Most complete attributes - displays the list of most completed attributes (both simple and nested) along with the percentage of completion. The attributes are distinguished by different colors.
    Note: The icon denotes the simple sub-attributes of the nested attribute. When you hover over the icon, a tooltip appears displaying the tree structure.
  • Least complete attributes - displays the list of least completed attributes (both simple and nested) along with the percentage of completion.
  • Most frequent values - displays the list of most frequent attributes (only simple attributes and simple into nested attributes). When you expand the drop-down menu, the entire tree structure appears displaying the nested levels.
  • Uniqueness distribution - displays the distribution of entities (only simple attributes and simple into nested attributes) in terms of unique value, not unique value, and empty values. When you expand the drop-down menu, the entire tree structure appears displaying the nested levels.