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How to ensure data quality in LeanIX and having a good governance established?

  • 4 August 2023
  • 5 replies
  • 287 views

Userlevel 4
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Most of us have experienced the challenges of maintaining data quality in LeanIX, even when using the best practices such as ensuring Fact Sheet completeness, conducting Surveys and obtaining the Quality Seal. Despite these measures, motivating stakeholders to maintain their data and provide Quality Seal approval can be quite difficult.

In light of this, it is essential for us to come together and exchange ideas and insights on the best practices for establishing a well-functioning governance framework in LeanIX.

Let's share our experiences and discuss effective approaches to fostering data quality and stakeholder engagement in LeanIX.

Thank you all for your valuable contributions!


5 replies

Userlevel 6
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Hi Irene, all,

For me, data quality is such a critical topic that I decided to develop an own Data Quality Management add-on module for LeanIX which can be purchased as official LeanIX Partner product 😉

In my opinion, the most crucial steps are:

  • Understanding our data quality. What are our rules, when is a fact sheet maintained “well”? Getting clear visual reports which organizations are doing a good job and which organizations have trouble keeping their data quality up.
  • Improving our data quality: Sending out automated reminders to inform subscribers that their fact sheets have quality issues. The notifications have to be crystal clear, so that users can fix their gaps quickly.
  • Avoiding future quality gaps by making sure that data quality is kept high automatically: Calculating relations/tags/attributes instead of manually setting them, synchronizing data from 3rd party systems like a CMDB, and automatically ensuring that subscriptions are always correct and that outdated users are replaced, e.g. via an integration with AD or HR/Org Management.

If anybody wants to see a demo of what I have built, feel free to get in touch.

Userlevel 3

Agreed!

I would like the ability to create logical / mathematical rules to cross-reference different attributes and raise flags where appropriate for the app owner to follow up.

Example: raise a flag if an app has a retirement date set but no successor set. Or if a business app supports only technical capabilities.

There is power in cross-referencing different pieces of data beyond the simple check of mandatory attributes being populated.

In the past I was the product owner for a in-house built tool similar to LeanIX and we had a Drools (rule engine) implementation to support complex rules

Userlevel 4
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Hi @irene.bakanakis and other discerning readers,

Technology goes some way to help with data quality but ultimately you need to incentivise (e.g. make an HR objective) and monitor those responsible for data quality, whichever stage of the LeanIX operating model journey you are on.

Userlevel 4
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Thank you all for your comments.

As an fyi here we have an upcoming webinar on 15.02 that might be interesting in that case regarding the LeanIX Rollout and Adoption Toolkit https://info.leanix.net/leanix-rollout-and-adoption-toolkit-webinar 

Userlevel 3

Agreed!

I would like the ability to create logical / mathematical rules to cross-reference different attributes and raise flags where appropriate for the app owner to follow up.

Example: raise a flag if an app has a retirement date set but no successor set. Or if a business app supports only technical capabilities.

There is power in cross-referencing different pieces of data beyond the simple check of mandatory attributes being populated.

In the past I was the product owner for a in-house built tool similar to LeanIX and we had a Drools (rule engine) implementation to support complex rules

I should also have added another “obvious” strategy: leverage SSoT’s as much as possible, assuming they are available (big IF in many companies). E.g. ServiceNow, SAP, Apptio, etc. for different aspects of app data and master architecturally relevant data in LeanIX. 

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