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Change Data Quality Issues dashboard

The Change Data Quality Issues dashboard enables you to understand your overall data quality, from a data completeness perspective. Using this dashboard, you will be able to identify areas in which there is no proper data maintenance, details about current data completeness situation, data completeness trends (whether it is improving or reducing over a period of time), areas in which it is lagging and by what percentages, list of changes that need to be completed from a data quality perspective, current stage of change life-cycle, data quality performance, and so on. With this information, it is possible to analyze and reduce data quality issues. This will also potentially lead to overall improvement in resolution parameters, adds value for Knowledge Bases and helps in re-usability of changes for reference perspective.

The Change Data Quality Issues dashboard can be of great value to answer some of the following business questions:

What is the overall data quality from completeness perspective of the changes and how does that fare at different status of change life cycle. What are the changes that contribute to the data quality in each status?
What is the average DQ incompleteness at changes level and across changes? Sort them based on the maximum DQ incompleteness so that those can be looked in closing for DQ.
Which attributes have a higher rank of DQ incompleteness. Sort them so that there could be a organization level plan for these attributes and teams can be trained for those

There are two chapters in this dashboard that can be used for analysis. The first chapter is the Change Data Quality dashboard which gives you numeral statistics about change data quality. You have the option to view data sorted on basis of department or leadership.

The second chapter is the Change Data Quality Details using which you can deep dive into the Change Data Quality trends, based on calendar month.

Change Data Quality Issues

The Change Data Quality Issues chapter has two pages:

Change Data Quality Issues by Department
Change Data Quality Issues by Leader

The Change Data Quality Issues by Department page gives insights into some of the KPIs, based on the department selected. A heat map is displayed to indicate data quality positioning of each department. On selecting the required department, KPI values are refreshed (displayed to the right of the page). The KPIs available in this page are:

Weighted Avg. Poor Data Quality across Status: Overall Change Completeness Data Quality with weighted averages by Change Status
% of Fields with Poor DQ across status: Average of Factors where the data quality issues are there to the total of all the factors reviewed for Data Quality. It gives the %age of factors where data quality is not maintained properly.
% of New Changes with Poor DQ: Ratio of Changes where Completeness Data Quality issues are there and the Change is in New Status
% of Poor DQ Changes Failed: Calculated as the number of Changes with Change Failure value as Yes and DQ as Yes divided by the total number of Closed Changes with DQ as Yes
% of Poor DQ Changes: Displays the ratio of Changes that have poor data quality as compared to total changes for a specific period of time.
% of Poor DQ Changes Caused any Impact: Rate of Changes with Change Impact Flag as Yes and where Changes have Data Quality Completeness issues
% of Closed Changes with Poor DQ: Ratio of Changes where Completeness Data Quality issues are there and the Change is in Closed Status
% of Poor DQ Changes delivered past due date: Ratio of Delay in delivery of changes where Changes have Completeness Data Quality issues to delivery age of Non Data Quality issue changes

The Change Data Quality Issues by Leader dashboard displays the same KPIs, only difference being that the KPIs are sorted on the basis of leadership. A bubble graph is displayed to indicate the data quality positioning based on leadership. KPI values are refreshed upon selection of bubble.

Change Data Quality Issue Details

The Change Data Quality Issue Details chapter has three pages:

New Changes
Closed Changes

The Change Data Quality Issue Details dashboard basically lets you deep dive into the change data quality trend, on the basis of a given calendar month.

Data displayed on every page is based on the status of 'Change' with data completeness issues. However, the KPIs displayed for each remains the same. The first page displays changes in 'New' or 'Open' state. The last page displays changes in 'Closed' state.

A graph to indicate the Creation DQ ratio for a given month is displayed in the first panel. If you select a month, data in the subsequent panels are refreshed. For example, on selecting Dec 2019, attributes such as assignee, assignment group, business criticality, so on that affect data quality completeness by change status is displayed for that month. Similarly, the panels to the right indicating factors such as Total Changes with Data Quality Issues, Change Creation Data Completeness, Select the value display criteria, and so on are refreshed based on the attribute you select in the 'Factors Completeness Data Quality by Change Status' panel.

Similarly, tabular data in the 'What Changes have poor data quality?' and 'What are the field values for the selected Change?' panels are refreshed based on the selection made in the 'What are the values for the selected field?' panel.

The KPIs available in this page are:

Total Changes with Data Quality Issues Count of all changes irrespective of current status, which have data completeness issues.
Factors Avg Completeness DQ: Average of Factors where the data quality issues are there to the total of all the factors reviewed for Data Quality. It gives the %age of factors where data quality is not maintained properly.
Select the value display criteria Calculated as DQ Changes Across Factors by total number of changes
Change DQ Issues Across Fields:If the value of this metric is 1, it means the change has a data completeness issue in at least one of the factors

Here's a list of the metrics used in this dashboard to calculate the KPIs:

Metrics Used

Metrics Used	Description
Overall Data Quality	Overall Change Completeness Data Quality with weighted averages by Change Status
Creation DQ	Ratio of Changes where Completeness Data Quality issues are there and the Change is in New Status
% of Poor DQ Changes in Changes Data Quality Issues	Displays the Ratio of Changes that have poor data quality as compared to total Changes for a specific period of time.
% of Closed Changes with Poor DQ	Ratio of Changes where Completeness Data Quality issues are there and the Change is in Closed Status
% of Poor DQ Changes Failed	Rate of Changes Failure where Changes have Data Quality Completeness issues
Change DQ Impact	Rate of Changes with Change Impact Flag as Yes and where Changes have Data Quality Completeness issues
% of Poor DQ Changes delivered past due date	Ratio of Delay in delivery of changes where Changes have Completeness Data Quality issues to delivery age of Non Data Quality issue changes
Avg. DQ Completeness Parameters	Average of Factors where the data quality issues are there to the total of all the factors reviewed for Data Quality. It gives the percentage of factors where data quality is not maintained properly.