Today I wanted to discuss the ‘Internal Processes’ section of the scorecard. In case you missed the previous parts of the series, you can read the introduction here, and about the ‘customer’ section here.
Perform, Perform, Perform
Throughout the business world people are measured upon their performance. How well do they carry out their responsibilities? Do they hit their objectives? Do they adhere to any applicable SLAs? The Data Quality team should be no different and we should look towards measuring our performance against our internal processes.
Our Internal processes are the procedures and tasks we follow to ensure data quality is managed, and communicated, throughout the business community.
Consider the following Internal Processes:
- Publishing and Review of a Business Terminology / Data Dictionary
- Resolution and Communication of DQ issues in a timely manner
- Identification of appropriate system, data & report ownership
All of the above are critical processes within the day to day responsibilities of a Data Quality team. If we under-perform in delivering any of these processes, it will have a knock-on impact on how data quality management is delivered within an organisation. In some cases, poor performance within our internal processes could even be a contributing factor to poor data quality.
For example, a Product Manager has noticed that sales data for their product is not accurate in the data warehouse. They raised a data quality issue to your team. The data warehouse is also used by the Finance team, and is currently being used to provide financial figures for a last minute board meeting. The data quality issue was raised yesterday, and is currently being investigated, but there has been no communication to the business community to advise them of the issue. The board of directors are now looking at inaccurate data, questioning the figures and wondering whether they can trust the data or not?
How can we measure our Internal Processes?
We can measure the performance of our internal processes by benchmarking them against our objectives, or against targets based upon our objectives. As an example, let’s take the process of ‘Resolution and Communication of DQ issues in a timely manner’.
All known Data Quality issues should be immediately communicated to the business community, and be resolved within 3 days of being raised
DQ Issues raised – 125
DQ Issues resolved within 3 days – 70 (56%)
Issues communicated to community – 100 (80%)
Upon seeing the measures above, we could ask:
“Why were only 56% of DQ issues resolved within our target time period? Do we need to involve more resources to fix issues? Do we need to adjust the target SLA?”
“All issues were due to be communicated to the business community immediately. Why were 25 issues not communicated? Do we need to set up reminders? Was no one able to pick up the issues?”
As Satesh suggested in a comment to my previous post: “What gets measured improves”. This is exactly what we are trying to achieve from a scorecard. Poor Performance within our Internal Processes could have a knock on effect on the perception of DQ management from our customers. Therefore, a process of continuous measurement, analysis and improvement is required, in order to ensure that we do not get complacent and adopt poor DQ Management habits.
The next post in this series will deal with the ‘Financial’ section of the scorecard, and we’ll look into how we can begin to measure the financial impact that DQ management can have on an organisation.