Summary

Data governance is a complex endeavor, but scaling it to meet the needs of a complex or globally distributed organization requires a well considered and coherent strategy. In this episode Tim Ward describes an architecture that he has used successfully with multiple organizations to scale compliance. By treating it as a graph problem, where each hub in the network has localized control with inheritance of higher level controls it reduces overhead and provides greater flexibility. Tim provides useful examples for understanding how to adopt this approach in your own organization, including some technology recommendations for making it maintainable and scalable. If you are struggling to scale data quality controls and governance requirements then this interview will provide some useful ideas to incorporate into your roadmap.

Announcements

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Your host is Tobias Macey and today I’m interviewing Tim Ward about using an architectural pattern called data hub that allows for scaling data management across global businesses

Interview

Introduction
How did you get involved in the area of data management?
Can you start by giving an overview of the goals of a data hub architecture?
What are the elements of a data hub architecture and how do they contribute to the overall goals?
- What are some of the patterns or reference architectures that you drew on to develop this approach?
What are some signs that an organization should implement a data hub architecture?
What is the migration path for an organization who has an existing data platform but needs to scale their governance and localize storage and access?
What are the features or attributes of an individual hub that allow for them to be interconnected?
- What is the interface presented between hubs to allow for accessing information across these localized repositories?
What is the process for adding a new hub and making it discoverable across the organization?
How is discoverability of data managed within and between hubs?
If someone wishes to access information between hubs or across several of them, how do you prevent data proliferation?
- If data is copied between hubs, how are record updates accounted for to ensure that they are replicated to the hubs that hold a copy of that entity?
- How are access controls and data masking managed to ensure that various compliance regimes are honored?
- In addition to compliance issues, another challenge of distributed data repositories is the question of latency. How do you mitigate the performance impacts of querying across multiple hubs?
Given that different hubs can have differing rules for quality, cleanliness, or structure of a given record how do you handle transformations of data as it traverses different hubs?
- How do you address issues of data loss or corruption within those transformations?
How is the topology of a hub infrastructure arranged and how does that impact questions of data loss through multiple zone transformations, latency, etc.?
How do you manage tracking and reporting of data lineage within and across hubs?
For an organization that is interested in implementing their own instance of a data hub architecture, what are the necessary components of an individual hub?
- What are some of the considerations and useful technologies that would assist in creating and connecting hubs?
  - Should the hubs be implmeneted in a homogeneous fashion, or is there room for heterogeneity in their infrastructure as long as they expose the appropriate interface?
When is a data hub architecture the wrong approach?

Contact Info

LinkedIn
@jerrong on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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