We regularly post polls on LinkedIn on topics of data management and data lineage to collect insights on the experiences and thoughts of our colleagues. We would like to share these insights with you as well. The framework to implement data management is the topic for today.
TODAY’S POLL:
RESULTS:
1. 62 % of respondents use DAMA-DMBOK2.
2. 34% of respondents use their own self-developed/adjusted model.
3. 5% of respondents use DCAM v2.
4. And none of the respondents has reported using COBIT 5.
We interpret these results as the following:
Two remarkable conclusions come from these results:
- DAMA-DMBOK has a leading role compared to other approaches.
Several reasons can explain this result:
- Industry focus
DAMA-DMBOK2 is an industry-agnostic guide. DCAM® 2.2 has been initially developed for financial institutions.
- Representative sample
The poll targeted data management professionals active on LinkedIn and got 65 responses. This group may not represent the whole variety of data management practices worldwide.
- Presence on the market
DAMA International was established in 1980 and had a longer history than the EDM Council.
- Differences in the models
DAMA-DMBOK2 provides a framework for data management. DCAM®, The Data Management Capability Assessment Model, was initially designed to measure maturity.
- Accessibility
DAMA-DMBOK2 publications are open to the public. You can purchase these publications for a reasonable price. To get DCAM® 2.2 publications, a company must join The EDM council.
- Almost 1/3 of the respondents prefer to develop their model or adjust the existing one.
In my opinion, this also can have different possible reasons:
- The term “data management” has a different meaning in a different context.
Therefore, diverse companies treat and scope data management differently.
- The absence of a practical methodology to implement data management.
Both DAMA-DMBOK2 and DCAM® 2.2 lack a practical guide for the implementation of data management. These limitations of the leading industry frameworks force companies to develop their models or adjust industry ones.
For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl.