Proven Impact of3RIVE TECHNOLOGIES

The Challenge

Scale creates data fragmentation the way it creates everything else: a system at a time, each justified on its own, each owned by a different part of the business. Over many years this corporation has built up more than 20 data sources across product lines, regions and sub-organisations, with no unified way to see across them.

The cost landed in two places. Leadership could not get timely insight into performance across the business, at exactly the scale where decisions are expensive to get wrong. And reporting teams spent a disproportionate share of their time reconciling data from incompatible systems before any analysis could start. A question from an executive became a request, a queue, an analyst, a bespoke report, and a wait.

Replacing the underlying systems in a multi-year big-bang programme was not on the table. The corporation needed a way to consolidate data from systems that were never designed to work together into something leadership across a dozen sub-organisations could use directly, and it needed the first useful output in months, not years.

The Solution

3Rive built an enterprise data warehouse consolidating product, customer and market data from more than 20 source systems spanning multiple regions and product lines, and a layer of AI-powered analytics on top of it.

1. 1

   The Source Systems Stayed Where They Were

   The warehouse ingests them as they exist. Pipelines handle cleansing, deduplication and field matching across systems with different structures, different definitions and different owners. That decision is the reason the programme delivered at all: rewriting 20 systems first would have pushed the first useful output out by years.

2. 2

   Definitions Reconciled Once

   Where sub-organisations counted the same thing differently, the warehouse resolves it in one governed model, so a figure means the same thing in every dashboard, for every region.

3. 3

   Natural Language Is on Top

   On the unified layer, 3Rive delivered Power BI dashboards with AI-powered natural-language questions and answers. Executives and managers ask the business a question in plain English and get an answer, instead of waiting on an analyst team to build a report. A large share of day-to-day reporting moved from request-and-wait to self-service, which is where the 85% reduction in critical report generation time comes from.

4. 4

   Built to Extend

   The pipeline architecture was designed so that additional sub-organisations and sources onboard as they are identified, not only the systems live at launch. Coverage has kept growing across the business without a re-architecture each time a new source arrives.

5. 5

   Embedded Engineers, Ongoing

   3Rive's senior data engineers and BI engineers work inside the corporation's analytics team on a continuing basis, extending the warehouse, building new dashboards and analytics, and onboarding new sources as the business changes. The platform is not a handover; it is a capability the corporation keeps drawing on.

The Approach

• Inventory the sources. Catalogue data across product lines, regions and sub-organisations, and find where definitions diverge.
• Engineer the pipelines. Cleansing, deduplication and field matching across heterogeneous systems, without changing the systems themselves.
• Build the warehouse. One queryable, governed layer for product, customer and market data.
• Put natural language on top. AI-powered dashboards, so executives query the business directly.
• Design for the next source. Pipelines that onboard new sub-organisations and systems without re-architecture.
• Stay embedded. Data and BI engineers inside the analytics team, extending the platform as the business moves.

Systems and Stack

Systems integrated

Credentials

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