Data management platforms are becoming increasingly distributed. Multicloud, edge, and other distributed cloud architectures are becoming more prominent in enterprise deployments, and data management architectures will need to keep pace to support evolving requirements.
To support distributed data architectures, enterprise will need to reassess and reorganize their data management teams to operate seamlessly across greater distances, diverse time zones, and more complex business environments that span diverse domains and jurisdictions.
In this sponsor panel, TDWI senior research director James Kobielus will lead data industry experts in a discussion of the challenges associated with building teams that can effectively manage data across distributed architectures. They will discuss such issues as:
- What are the principal challenges associated with managing enterprise data across distributed environments?
- How will data governance practices need to evolve to manage a single version of the truth in distributed architectures?
- What should enterprises do to keep a lid on the overhead costs associated with managing data across increasingly distributed architectures?
- What collaboration environments are best for augmenting the productivity of data management teams across distributed environments?
- What changes to an organization’s data management teams and staffing models are necessary to operate effectively across distributed environments?
- What roles, skills, and team arrangements are most effective for edge, federated, fabric, mesh, and other distributed data architectures?
- What should you do to recruit and incentivize data management professionals to operate effectively in distributed teams?