Data Architecture Vison

1. A Core Model, not an Overweight Enterprise Model

2. Data models depict and enable an organization to understand its data assets.

3. "Data is a tremendously important corporate asset." Yawn. How many times have you heard a CXO say something to that effect? I'd bet quite often. Most of the time, those words fall on deaf ears. That is, an organization's actions betray its credo. Put differently, there's a world of difference between talking the talk and walking the walk. Not at Google. If anything, the company might be too good at the data game.

4. Het is onze missie om alle informatie ter wereld te organiseren en universeel toegankelijk en bruikbaar te maken.

5. the starting point in defining a data strategy begins with understanding how the organisation use the data to satisfy a variety of use cases within your enterprise,

6. Data Architeture

7. You’re a tech company – regardless of your industry or sector. Regardless of sector, most companies need to reorganize themselves to take advantage of emerging “human-machine” opportunities. To be competitive in speed, product and business innovation, and to recruit and retain top talent, businesses will need to adopt a modular core data and tech architecture in the back-end combined with highly scalable processes and agile front-end teams. We know of financial services companies that are turning what once were massive sales and “relationship” business into apps. Leading industrial players are working to become “bionic companies” – creating units to develop software, tech platform businesses and ventures, while simultaneously deriving value from legacy assets. Face it: You’re a tech company.

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10. Data-Centric

10.1. Technology Angnostic

10.2. Enterprise View on Data

10.3. Major Culture Shift

10.4. Data is "Forever"

10.5. Compliant By Nature

10.6. Integrated by Design

10.7. Data-Centric => Data-Driven

10.8. Share and Absorb

10.9. Low Code \ No Code

11. Application-Centric

11.1. Technology Dependent

11.2. Application View on Data

11.3. Business As Usual

11.4. Technology is Transient

11.5. Eventualy Compliant

11.6. Integration at a (high) Cost

11.7. Data-Drive ≠ Data-Centric

11.8. Duplicate and Integrate

12. "...Alas, customers usually presume their technology partners will also provide them with good data management, failing to understand that this is exactly what their partners are NOT providing because it's essentially not technology.." Martijn Evers

13. Data-Driven : Usage

14. Data-Centric : Control

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16. Data Integration Fintech's

17. Metadata

18. Metadeta

19. Metadata

20. Blackstone

21. Systems of Record Systems of Engagement Systems of Understanding