When Forecasts and Network Metrics Don’t Match

The issue isn’t models. It’s the data underneath.

DataOS fixes the data underneath demand forecasting and dealer analytics. By standardizing definitions, adding lineage, and delivering trusted data, teams can scale outcomes with confidence.

Talk to Us About Your Forecasting Data

Works on top of your existing data stack

Learn more about DataOS

What Teams Are Seeing on the Ground

Across retail, manufacturing, aviation, QSR, and fintech:
  • Forecasts change depending on the source system
  • Dealer and partner metrics don’t reconcile
  • Analysts spend more time fixing data than analysing it
  • AI experiments look promising but break in real decisions
These aren’t forecasting problems. They’re data foundation problems.
What customers achieve with DataOS

“It previously took a couple of months to execute one campaign. It now takes 1-2 hours to execute the same campaign with DataOS.”

Director of Digital Innovation, Global Consumer Goods Distributor
Backup IMg

Why These Use Cases Break at Scale

Forecasting and dealer analytics depend on:

  • Shared definitions across systems
  • Clean joins between operational, sales, and external data
  • Clear lineage to explain numbers
  • Trust that metrics won’t change tomorrow

Most enterprise stacks weren’t designed for this level of reuse and consistency

Talk to Us About Usable Data

How Enterprises Fix This Without Building Point Solutions with DataOS

DataOS treats forecasting and dealer analytics as outcomes, not standalone projects. It provides:

  • A semantic layer so demand, inventory, and performance metrics mean the samething everywhere
  • Reusable data products analysts can apply across forecasts and networks
  • Lineage and quality built in, so numbers can be explained
  • Access across BI, APIs, and AI tools without rebuilding pipelines

All on top of your existing data platforms

Talk to Us About Semantic Layer

What Improves Once the Foundation Is Right

Teams that use DataOS typically see:

  • More stable and explainable forecasts
  • Cleaner dealer and network reporting
  • Faster scenario analysis
  • Less manual reconciliation
  • Higher confidence in AI-driven recommendations

Forecasts stop being debated and start being used

Talk to Us About Your AI Readiness

How Enterprises Fix This Without Rebuilding Their Stack

You Keep

  • ▪   Snowflake or Databricks
  • ▪   Existing BI and analytics tools
  • ▪   Current source systems

DataOS Adds

  • ▪   A semantic layer that standardizes business meaning
  • ▪   Data products analysts can reuse across AI and BI
  • ▪   Inline lineage, quality, and access control
  • ▪   Native access for dashboards, APIs, and AI apps

No rip-and-replace. No new silos. No disruption to existing teams.

Who DataOS is Built For

Global CPG Distributor

Heads of Digital Transformation and Business Analytics

Industrial Manufacturer

AI and Innovation
leaders accountable for outcomes

Global Architectural Firm

CIO, CTO, CDO balancing speed with trust

Leading Device Manufacturer

Enterprises where analysts drive decisions, not just reports

Start With a Real Business Use Case.
Don’t start with just a demo.

A real forecasting,
analytics, or AI 
use case

Implementation in
your environment

A working outcome
your analysts
can validate

A clear path
to production

Learn more about DataOS

DataOS is recognized by the experts.
Proven by results.

Talk to us about making your data usable for AI

Start with a discovery conversation that leads to a Proof of Value.
Get Started

FAQs

Is this just a governance or catalog tool?

No. Governance and lineage are built into how data is delivered and consumed, not managed separately.

Will this increase process overhead for analysts ?

No. Analysts get faster access because quality, lineage, and access controls are already in place.

Do we need to standardize everything upfront ?

No. Semantics evolve incrementally as new use cases are added.

Can this work with our existing BI and analytics tools ?

Yes. DataOS works with the tools your teams already use.

How quickly can we see results ?

Most teams see a working, trusted use case in 4 to 8 weeks.