5 Principles of Analytical Hub Architecture – Part 2

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5 Principles of Analytical Hub Architecture – Part 2

Continuing the discussion on analytical hub design, here’s the second part of my post on the architecture principles. If you missed the first two principles (1. Data from everywhere needs to be accessible and integrated and 2. Building solutions must be fast, iterative and repeatable) see this earlier blog post.

3. The advanced analytics elite needs to “run the show”

IT has traditionally managed the data and application environments. In this custodial role, IT has controlled access and has gone through a rigorous process to ensure that data is managed and integrated as an enterprise asset. The enterprise, and IT, needs to entrust data scientists with the responsibility to understand and appropriately use data of varying quality in creating their analytical solutions. Data is often imperfect, but data scientists are the business’s trusted advisors who have the knowledge required to be the decision-makers.

4. Solutions’ models must be integrated back into business processes

When predictive models are built, they often need to be integrated into business processes to enable more informed decision-making. After the data scientists build the models, there is a hand-off to IT to perform the necessary integration and support their ongoing operation.

5. Sufficient infrastructure must be available for conducting advanced analytics

This infrastructure must be scalable and expandable as the data volumes, integration needs and analytical complexities naturally increase.  Insufficient infrastructure has historically limited the depth, breadth and timeliness of advanced analytics as data scientists often used makeshift environments.

Read more about this in my free white paper on Analytic Data Hub design entitled Analytics Best Practices: The Analytical Hub.

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