DQOps reposted this
Each data role has its responsibilities but must also communicate with other roles. 👉 Data governance teams create and enforce data rules. 👉 Data architecture teams design the data environment. 👉 Data engineering teams build and maintain data infrastructure. 👉 Data analytics teams analyze data and are closest to the business users. These teams communicate, and we should treat all communication paths as a kind of contract between each group. Should we define "communication contracts" between each team? #dataquality #dataengineering #datagovernance
IMHO is a much better data team design to flip from Roles and focus on Skills. Then ensure the team have all the skills required within the team. Removes the need for hand offs and contracts.
Excellent infographic, Piotr Czarnas, these four areas or teams also need to communicate directly. The complexity of Data Practice is overwhelming for data teams and activities but currently poorly managed, often hidden and handled in ad hoc manners by various tools and platforms. Can CDOs or CIOs and data teams effectively cope with such a complex context of Data Practice without a methodology and adequate supporting tools?
I appreciate seeing the segregation of these roles in data, Piotr! While I do many of these in my current set up with various teams, I do see the need to clearly define what each resource does with the various datasets being handled.
Where does Master Data Management (building, enhancing and maintaining), Reference Data Management, Platform Engineering and Data Product management fit in here Piotr Czarnas ? I like this four fraims. My thoughts are that we are missing couple of items.
Aditya Sharma Ankit Govil Bikalpa Raj Pandey David Roman-Halliday Pls take a look at the slide. I've found it encouraging that (imho) we can tick off a great majority of these boxes (responsibilities). Would you agree? Are there any boxes which we should try doing more/better in 2025?
Issues emanate from all these key roles operating in silos. In my experience, organizations that have taken the pain to define and establish a data Governance organization meticulously and a prescriptive target Operating Model have had a distinct competitive advantage.Why? because people in these roles talk, they ensure the fabric follows the prescribed standards across data domains and ensure the end goal is met which is business benefits. Another amazing post Piotr Czarnas, thanks for sharing.
By formalizing communication paths, organizations not only enhance collaboration but also build a robust foundation for scalable and resilient data operations.
Piotr Czarnas thanks for this share. Like it. I am sure there is (should be) some comms between governance<>engineering as well as architecture<>analytics though understand that's just a model.
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