tdwi data governance maturity model

In this article I’ll provide an introduction to the TDWI data governance maturity model, but here are the other ones covered so far:


If you are interested in learning more about data governance maturity model, A LOT MORE than what is covered in these articles, please checkout my online Data Governance Maturity Model(s) course that is available for enrollment right now.


TDWI Data Governance Maturity Model

Overview: Published in July 2008, the TDWI’s data governance maturity model appeared in a white paper on the four imperatives of data governance maturity. The model has 6 levels, similar to Oracle’s data governance maturity model or the 2008 Gartner EIM maturity model, as well as 2 gaps. As you can see, these levels can be compared to the life stages of a human. TDWI actually refers to them as life-cycle stages. Here they are:

tdwi data governance maturity model
TDWI life-cycle stages

Level 1 – Prenatal: At this stage, there are mostly manual and ad-hoc solutions to a business or technology problem.

Level 2 – Infant: Business requirements lead to a technology or practice and have a proof-of-concept

Gulf: Organization needs to institutionalize the solution concepts

Level 3 – Child: Expansion of the new technology or practice it committed to

Level 4 – Teenager: Growth slows down as it occurs in a few departments or shortlist of IT systems

Chasm: Enterprise adoption or solution re-architecture

Level 5 – Adult: Continue maturing solution best practices and technology implementations

Level 6 – Sage: Cross-departmental coordination and technology scalability

 

TDWI also has a list of 4 domains or data governance imperatives and they are action items. 2 fall under organizational imperative and 2 under technical imperatives.

Organizational Imperatives

1. Maintain a cross-functional team and process

2 Align with data-intense business initiatives

 

Technical Imperatives

3. Govern data usage via technical implementations and

4. Automate data governance process via technical implementations


The imperatives, as a group, imply a time sequence. For example, it’s obvious that imperative 1 must create a cross-functional team before imperative 2 can align team goals with business initiatives. Less obvious is that imperative 3 should be governing IT systems before imperative 4 starts using IT systems to automate governance processes. Although dependencies like these determine an order for commencing the imperatives, the imperatives must eventually coexist and interact. In the TDWI data governance maturity model, each of the 4 data governance imperative goes through the 6 levels and 2 gaps outlined above.


Takeaway: TDWI indicates that most organizations are in these middle levels, child and teenager, and that one requires a considerable effort to cross over the chasm and head into the adult and sage levels. As a note, you will be able to find the same life-cycle stages (or levels) being used in other types of models from TDWI such as Business Intelligence and Data Analytics, and Big Data so you will encounter this breakdown in other areas.

If you are interested in learning more about data governance maturity model, A LOT MORE than what is covered in these articles, please checkout my online Data Governance Maturity Model(s) course that is available for enrollment right now.

{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}

About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

You may also like:

George Firican

07/15/2020

TDWI data governance maturity model

>