Service
Enterprise Data Strategy & Analytics
Most enterprises sit on data they've never used. We turn it into decisions in weeks, not quarters.
Your data estate is expensive because nobody trusts it.
Most enterprise teams have the same pattern. Customer records disagree across CRM and ERP. Product data breaks search, personalization, and reporting. Executives still ask for spreadsheet reconciliations before they approve a decision.
That is not a tooling issue. It is an operating model issue. Object Edge helps you fix the data layer underneath AI, commerce, service, and finance so teams can ship decisions and systems with confidence.
We do that with senior engineers, clear sequencing, and AI-native delivery. The mandate is not “build a data program.” The mandate is to make revenue, service, and operational systems work better in 8-12 week increments.
What changes when the foundation is right
- Leaders stop debating whose dashboard is correct and start acting on shared numbers.
- Commerce, CRM, and service systems run against cleaner product, customer, and pricing records.
- AI use cases move out of pilot mode because the underlying data is governed and observable.
- Teams reduce manual reconciliation, exception handling, and reporting churn.
How we approach the work
We start with the business pressure, not the platform. For one client the constraint is a broken catalog. For another it is quote latency, poor account hierarchy data, or analytics nobody believes. We map the few domains that matter most, then build the architecture, governance, and implementation path around them.
That usually means combining strategy, governance, integration, and operating support in the same program. Clients like Specialized, TYR, Calix, and American Apparel did not need a generic data exercise. They needed a cleaner system that improved buying, service, and decision speed.
Core service paths
- Data strategy and consulting for roadmap, domain prioritization, and architecture decisions.
- Enterprise data governance for quality controls, lineage, ownership, and policy enforcement.
- Data readiness and experience design for search, personalization, and UX tied to trusted data.
- Systems integration and platform implementation for connecting ERP, CRM, commerce, PIM, and analytics layers.
- Data managed services for ongoing monitoring, issue response, and optimization after launch.
Best fit
This service group is a strong fit when your team already knows the pain. Reports disagree. Launches slip because data cleanup starts too late. AI pilots cannot graduate because nobody owns the source data. That is the right moment to fix the operating system behind the business.