Thursday, August 16, 2007

Data Management Principles

Usually IT departments are very infrastructure or application focused. Information or data is seldom the first priority. In order to establish data management as one of the cores in the organisation it is required to publish some key principles; it is a bit like a consitution for a country.

Establishing these principles and then repeating them ad nauseum is the only way to get people to know the importance. The principles should be easy and should be part of the overall IT architectural principles (as mandatory).

Here are (what I think) the main points:

1. Data is an Asset (so it should be managed like an asset)

2. Data should have an Owner

3. Data should have known Quality rules

4. Data should have a guaranteed integrity across the Lifecycle

5. Adopt international standards where possible

6. Classify each element in the right security class (is there any confidential data?)

7. Ensure data is accessible to whom needs it (open up by default!)

8. Ensure meta data is in place

9. Adopt principles for data architecture (more on this next time!)

10. Ensure internal & external information is treated with the same diligence

I may have missed one or two (but 10 was such a nice number), but if you can get your projects to adhere to this, than you're pretty mature in terms of data management!



At 5:30 AM, Blogger Unknown said...


Good overview from an IT perspective, but seen from users there are four other as important principles:
a. Data modeling must become part of knowledge modeling and management,
b. Design data is often created by several roles, each performing methods and contributing separate parameters and values,
c. Data modeling needs to become a collaborative work process among disciplines, teams, and other roles,
d. DM, KM and life-cycle management need to be captured in user-driven models managed in a federated knowledge architecture.

At 4:14 PM, Blogger Evert Ruijs said...

Frank - I fully agree, data should not live in isolation and the principles mentioned are quite data centric. The world is moving now to a place where data, information and knowledge is all fluid & connected, hence the need to look at data from a wider perspective. The last few years I pick up a few trends:
- an increased profile for data in the organisation (and as a result an increased focus on quality, models, etc)
- the move from building data models to 'catalogues of information'
- the move from relational modelling to semantic modelling


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