The Data Quadrant Model (DQM) is typically a model that can be used by an organisation to make sense of their data domain. One of the sense making aspects is 'how to organise' and although every organisation should translate the DQM to an organisation model that fit their context (culture, maturity, timing, people, system landscape, etc..), there might be some heuristics that help you on your way.
In general the DQM - if travelled from I to II, IV to III (see figure) - will be characterised by increasing
entropy, something I have tried to explain in an earlier blogpost. For the sake of simplicity in this blogpost, the question regarding 'How to organise' is translated into the degree of centralisation versus decentralisation.
Heuristic: systems with lower entropy are more prone to centralise as opposed to systems with high entropy which are prone to decentralise.
So, quadrant I and quadrant II have the highest propensity to be organised centrally and quadrant IV and quadrant III have the highest propensity to be organised decentrally.
Easy, peasy..? So we need one central quadrant I organisational entity. Lets go overboard and give it a name; a Data Service Center (DSC). The DSC is comprised of Data- and Information modellers, engineers and architects that model, validate and process the data and give access to the data to serve application development (II), BI professionals (II), data scientists (IV) etc..
But how is the DSC organised in different operating models? Lets refresh our memory a bit with the four quadrant model developed by Ross and Weiss in their landmark book ' Enterprise Architecture as strategy'. They identified four types of operating models; diversification, replication, coordination and unification. Yes, my dear data architect, this is stuff you need to know. This is stuff you need to be able to translate to the data domain and advise your management on the consequences.
I know....I am preaching....sorry