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How to Modernize Service Management for Data and AI

Data Governance

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Recently I have been working on our strategy for culture and capability management for Azure Data Strategy. Traditional service management approaches sink when you are talking about data governance and managing large data lakes with the promise of machine learning to bring out new capabilities for organizations.

With Azure Purview, things needed to be stickier to the technology moving forward. Process flows, data flows, and data governance needs to be automated to enable ML at scale for organizations. We need to address the new way of working and the organizational structure needed to support it.

For the process approach, we needed to modernize the organizational structure, policies, procedures, and governance controls to leverage Azure Data and Purview. Through analysis with Customers, we identified areas that need to be reviewed:

How strong is their current Azure footprint? – to build a new service on Azure, you need to ensure that the platform is appropriately managed and built

What does the organization look like now, and what does the future organization need to be? You can’t innovate in silos, and you need to address organizational resistance.

What are the current data policies and governance controls in the organization? How do these need to be modified to enable a secure and highly automated data estate?

Next was to understand how the business leverages data and who are the key audiences in the organization that will need to use this data in their role. Identification of the roles and audiences impacted was necessary to apply an Adoption and Change Management strategy to support the new way of dealing with data in the organization.

When organizations move to Data as a Service model, the potential challenges usually are:

  • Organizational alignment between the Data and IT teams to adopt an enterprise strategy for data
  • What does the new Operating model look like to support governance, data management and IT Operations?
  • How to shift to DevOps as a culture and not a CD/CI capability. How do teams work in Agile ways with data?

Moving forward, organizations need to address and begin the cultural shift needed to support a DevOps culture required to support a modern data strategy. It starts with sharing the future vision of data for an organization and defining the roles, responsibilities, and application of tools to support the new processes and governance model.

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