Enterprise Context Sharing

Large enterprises accumulate organizational knowledge in silos. The HR department maintains org charts in Workday. Engineering tracks service dependencies in a custom Memgraph instance. Product manages feature hierarchies in Jira. Sales stores account relationships in Salesforce. Each team chose the right tool for their job, but now no one has a unified view of how these domains connect.

MemGQL solves this by providing a federated knowledge graph that overlays canonical context—org charts, product hierarchies, ontologies—onto each team’s existing data stores. Departments keep their preferred tools and schemas while gaining a unified graph view that crosses organizational boundaries.

The Problem

Enterprise context sharing faces four fundamental challenges:

  1. Tool Diversity: Each department optimized for their specific needs. HR needs hierarchical reporting; Engineering needs graph relationships; Sales needs relational CRM. Forcing everyone into one database creates friction and adoption resistance.

  2. Schema Drift: Even when teams use the same database type, their schemas diverge. One team’s Employee table has different columns than another’s. Maintaining a central canonical schema requires constant synchronization that never quite works.

  3. Ownership Boundaries: Data ownership is organizational. The HR system is owned by HR; Engineering’s service graph is owned by Platform Engineering. Centralizing this data requires political capital and creates single points of failure.

  4. Secure Data Access: Each departmental system enforces its own security policies, authentication mechanisms, and authorization rules. Creating a unified view risks bypassing these controls or creating inconsistent security postures. Sensitive data—employee salaries, unreleased product features, customer contracts—must remain protected while still enabling authorized cross-domain queries.

The Solution

MemGQL provides a virtualization layer that maps each team’s native schema into a shared enterprise ontology. The underlying data stays in place; only the graph view is unified.