The advent of AI coupled with companies’ need to leverage context-rich data for smarter decision-making has highlighted the value of qualitative and unstructured data.
But traditional enterprise architectures have focused on the business application, which has led to continued data fragmentation across the organization, and poor business insights from that data. Low-quality, poorly governed data decreases business agility and increases integration costs.
A data-centric approach, by comparison, treats data as a workstream. It helps organizations effectively manage the full data supply chain, transform a siloed business environment for democratized data sharing and tap into its enterprise knowledge.
Daniel Roberts, Senior Manager, Sales Engineering at Progress, explores:
- Differences between app-centric and data-centric architectures
- Benefits of data centricity for complex data management
- The move towards knowledge graphs and the technology you need to support it
- Use cases that could benefit from transitioning to a data-centric architecture
- How to build data hubs, data fabric and knowledge graphs with Progress MarkLogic
Learn how to adapt and future-proof your current data architecture to sustain company growth and emerging market demands for the next five years and beyond.