Data Unification
One Truth Across Every System
When the same customer, product, or transaction lives in five different systems with five different formats, trust in data erodes. GRAVITI unifies fragmented enterprise data into a single, consistent, reliable source of truth.
Microsoft Azure
Amazon Web Services
Google Cloud
IBM Cloud
Oracle Cloud- Full flexibility in deployment options. We are not commercial partners of software vendors
Who Is It For
Data unification is essential for organizations where inconsistent data across systems creates reporting conflicts and operational friction.
- Data and analytics teams spending excessive time reconciling conflicting reports
- IT leaders managing redundant data stores across departments
- Operations teams frustrated by inconsistent customer or product records
- M&A integration teams needing to merge data estates from acquired companies
Our Approach to Data Unification
GRAVITI tackles data fragmentation at its root. We map every data source in your organization, identify overlapping entities—customers, products, transactions, employees—and design a canonical data model that serves as your enterprise standard. This is not a one-time export; it is a living system that continuously reconciles data as it flows between systems.
Our engineers implement entity resolution and master data management techniques to match, merge, and deduplicate records across systems. We handle the messy reality of enterprise data: inconsistent naming conventions, missing fields, conflicting timestamps, and duplicate entries created by years of manual processes.
The unified data layer we build integrates with your existing BI tools, applications, and workflows, so every team across the organization works from the same numbers without changing how they access data day to day.
Connecting to systems already in your organization
Our solutions include integration with popular market systems, as well as any additional system as needed
How We Deliver
- Data Inventory: Catalog all data sources, entities, and relationships across the organization
- Canonical Model Design: Define the unified schema and entity resolution rules for master data
- ETL Pipeline Build: Develop extraction, transformation, and load pipelines to populate the unified layer
- Deduplication & Matching: Apply entity resolution to merge records and eliminate duplicates
- Consumption Layer: Expose unified data through APIs, views, and BI tool connections
Expected Outcomes
- Single source of truth for customers, products, and transactions across the enterprise
- 70-90% reduction in time spent reconciling conflicting data between departments
- Improved analytics accuracy through consistent, deduplicated master data
- Faster onboarding of new data sources and acquired company datasets
Service Model
- Assessment: 2-3 week data landscape audit and unification roadmap
- Build: 8-14 week canonical model design, ETL development, and entity resolution
- Managed: Ongoing data quality monitoring, new source onboarding, and reconciliation reporting
Frequently Asked Questions
How do you handle conflicting data between systems?
We establish source-of-truth hierarchies for each data attribute based on system reliability, update frequency, and business rules. Conflict resolution rules are documented and configurable so your team maintains control over which system "wins" for each field.
Does this replace our existing systems?
No. Data unification creates a consolidated layer that sits alongside your operational systems. Each department continues using their preferred tools while the unified layer ensures consistency for analytics, reporting, and cross-system processes.
How long does a unification project take?
Scope varies with the number of source systems and data complexity. A typical engagement with 5-10 sources takes 10-14 weeks from assessment to production. Larger multi-entity programs may be phased over 3-6 months.
End the Data Conflict
Inconsistent data is undermining your decisions. Let GRAVITI build a unified data layer that gives every team in your organization the same reliable numbers.
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