Snowflake in Commerce: From Data Silo to Shared Architecture
When marketing, sales and controlling each report a different revenue figure, it isn't a reporting problem. It's an architecture problem.
AI Impact Stack · Data platform
Data platform: one truth for customer, revenue and margin
For your team and your agents.
Customers, products, prices and stock live in ERP, shop, CRM and spreadsheets today, each source with its own truth. A data platform brings them together, with one definition per metric that reporting, forecasts and agents all draw on.
Data harmonization
Agents decide with full knowledge
Three departments, one number.
Marketing optimizes for gross revenue, controlling calculates with net margin, sales measures pipeline. An agent that draws on these sources decides with half the knowledge.
A data platform on Snowflake or Databricks brings ERP, shop, CRM, marketing and service to a shared state. Every metric is defined once and used identically everywhere. Every project starts with a data audit: which sources exist, how reliable they are, which metric should stand at the end, and in which definition. This alignment with your business teams matters more than any technology choice.
- Bring sources togetherERP, shop, CRM, marketing, service and external data on one platform
- One definition per metricCustomer, revenue and margin modeled once, versioned and tested with dbt
- Governance from the startData catalog, lineage and access control are built in
- Ready for predictions and agentsFeature data for forecasts, CLV models and LLM applications
What the platform delivers
Data that impacts costs and revenue
Data work pays off in two directions: on costs and on revenue. We align every use case to this.
One set of figures for everyone.
Marketing, controlling and sales work with the same verified source. New analyses are built as dashboards on modeled data.
Result: decisions based on one number and shorter alignment rounds.
Predict customer value.
Customer lifetime value, churn indicators and next best action on one platform. For HSE, we combined deterministic calculation and AI prediction of customer value in a shared Snowflake architecture.
Result: marketing budget flows to where customer value is highest.
Predictable inventory and provisions.
More precise forecasts, better-matched provisions, predictable returns and cancellations.
Result: less tied-up capital and fewer write-offs.
Productive in six to eight weeks.
Depending on the starting point, that is how long a first productive data platform with two to three source systems takes. If Snowflake is already running, a single use case is typically productive in four to six weeks.
Technology partners
Platform-agnostic, certified
We recommend the platform that fits your landscape and your teams, and we model with dbt. Agents can run directly in Snowflake or Databricks.
Further reading and listening
Data Foundation for B2B Industry: Forecasting, After-Sales, Service Data Flows
If your forecast accuracy hangs on the gut feeling of your head of sales, and your field-service technician collects a spreadsheet of wear-parts at the end of the day, you do not need the next platform — you need a foundation that brings ERP, service tickets and IoT signals onto a common state.
Podcast
Episode 640 min
Why AI Projects Fail Because of Data Architecture
Many AI pilots start out promising and end up in a drawer after a few months. Gartner expects companies to abandon around 60 percent of AI projects that are…
FAQ
What matters before building a data platform
Both work. What matters is your existing landscape, the skills of your teams and the use cases you are planning. The recommendation is settled after the data audit.
With a single use case directly on your platform, typically four to six weeks until the model is productive. Architecture and operations stay as they are unless a specific need for change arises.
When several source systems come together, several teams need the same figures, AI initiatives are meant to go into production, or data lineage and access control become a requirement. With a single source, the existing BI tool is often enough.
Yes. The platform runs in your environment, and access is governed per role and per agent. Data sovereignty stays within your company.
Directly on the platform, for example with the AI functions of Snowflake or Databricks, or via MCP servers with clear permissions and logging. Which route fits depends on the use case.
Getting started
In two weeks, you know what your data can carry
The Impact Check assesses your data along the four hurdles and shows which data your first agent needs.
Get in touch
We look forward to your enquiry.
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