Overall Data Accuracy/Completeness

I work for a government health agency.

1. We have added a new field to applications - ‘Responsible Position Number’.  We have a script that gets data from our HR system, and that updates subscriptions as staff move around.
2. For the areas that have a large number of systems, we meet with managers roughly every 6 months to go through their systems and (at a high level) check that our data is correct.  Especially useful for systems that are near end of life.
3. for other systems we are planning to use the leanIX expiry of approval, and every few months send out a carefully crafted email to managers / executives to ask them to check data is correct.  The unfortunate reality is as we are dealing with time poor clinicians, we know that it will be hard to ensure timely updates.  However, for those systems that are essential to operations (i.e. required for patient safety) we follow up to ensure data is up to date in LeanIX.

Hi ​@rtuteja 
I work for a manufacturing company in France.
Technically, we have configured the completion rate of our application factsheet type to reflect the % of what we consider as mandatory fields that contain a value.
Second, we have enabled the draft status on the application factsheet type, because it prevents users from approving applications if they haven’t reached 100% completion for that application. 

We have also configured leanIX to break the quality seal after 1 year to force the application owners to check and reapprove their applications at least once per year.
Last but not least, we have established a leanIX community of 10 members representing all IT teams in our company.
I am sharing a dashboard with them which is based on only 2 KPIs

• completion rate of the applications of their team
• approval rate of their applications

The result is that , quantitatively, we are rather good (usually around 99% completion and 85% approval), but of course quantity doesn’t mean quality and, when it comes to quality, it is much harder to get.