Decision Foundry delivers expert platform services across Snowflake, Databricks, Power BI, Tableau, and MCI — from architecture and implementation to migration and managed services.
The Reality
Your teams aren't failing because they lack talent. They're failing because the platform landscape underneath them has grown organically — without architecture, without governance, and without a coherent strategy.
Five teams using five different tools with no shared governance, no common data models, and no way to compare numbers across business units.
You went all-in on one platform years ago. Now requirements have changed, costs have ballooned, and switching feels impossible without a migration partner.
Your data warehouse, BI tools, and AI platform don't talk to each other. Analystss spend most of their time wrangling data instead of generating insights.
You can't hire enough Snowflake architects, Databricks engineers, and Tableau developers. Projects stall because the right expertise isn't available when you need it.
Compute credits, user licences, and storage costs grow 30–meaningfully year-over-year. Nobody can attribute costs to teams, projects, or business outcomes.
Data access policies are defined differently in each platform. There's no single view of who can access what, and compliance audits are a quarterly scramble.
Legacy on-prem warehouses need to move to the cloud, but the migration risk feels too high. Months of planning produce no forward movement.
You're paying for enterprise licences across multiple platforms, but adoption is low. Features go unused, and ROI remains theoretical, not measurable.
Our Platforms
We don't just know these platforms — we've deployed them at scale for hundreds of organisations. Click through to explore our deep-dive service pages for each platform.
Architect and deploy Snowflake for secure data sharing, cost-optimised compute, and governed analytics — from single-cloud to cross-cloud deployments.
Build unified lakehouse architectures with Delta Lake, MLflow, Unity Catalog, and generative AI — turning your data platform into an AI engine.
Deploy enterprise-grade Power BI with semantic models, Microsoft Fabric integration, embedded analytics, and DAX-optimised reporting at scale.
Implement Tableau for interactive dashboards, Tableau Pulse AI insights, embedded analytics, and deep Salesforce CRM integration.
Unify marketing data with MCI (formerly Datorama) — connecting campaigns, spend, and performance across every channel into a single source of truth.
AI Across Platforms
From Databricks generative AI and Snowflake ML Functions to Power BI Copilot and Tableau Pulse — AI capabilities are native to every modern data platform. We help you activate these capabilities so your teams move from descriptive reporting to predictive and prescriptive intelligence.
Explore AI CapabilitiesLeverage built-in AI capabilities in each platform — from Databricks generative AI to Tableau Pulse and Power BI Copilot — to surface insights faster than manual analysis.
Build, train, and deploy predictive models using Databricks MLflow and Snowflake ML Functions, then visualise predictions directly in Power BI and Tableau.
Govern AI assets, data lineage, and access policies across all five platforms using Unity Catalog, Snowflake Horizon, and platform-native security controls.
Automate data flows between platforms with event-driven pipelines — from Snowflake Streams and Tasks to Databricks Workflows and Fabric Dataflows.
Platform Ecosystem
No platform operates in isolation. We integrate your entire data ecosystem — from ingestion and transformation to storage, analytics, AI, and activation — so every layer works together seamlessly.
+ native connectors to data sources across all five platforms
Our Process
We audit your current platform landscape — licences, usage patterns, skill gaps, and integration points — and deliver a prioritised modernisation roadmap.
We design your target-state architecture: platform selection, data flows, governance policies, security model, and compute strategy — tailored to your workloads.
Our certified engineers build the platform — provisioning environments, configuring governance, deploying data models, and establishing CI/CD pipelines.
We connect your platforms to upstream sources and downstream consumers, migrate legacy workloads, and validate data quality end-to-end.
Post-launch, we tune performance, optimise costs, upskill your team, and provide ongoing managed services to keep your platforms running at peak efficiency.
Case Study
A global media company was running Snowflake, Databricks, Power BI, Tableau, and MCI as disconnected silos. We designed a unified architecture with shared governance, migrated legacy workloads, and integrated all five platforms — reducing reporting time from days to hours.
80%
Faster reporting
5→1
Unified governance
45%
Cost reduction
3x
Faster adoption
Faster reporting across all platforms
Unified · Governed · Integrated
How We Work
Pick the model that fits where you are. All engagements include a dedicated platform lead and a clear outcome definition.
Ideal for: Teams evaluating platform options or needing an expert audit of their current setup
A 2-week deep dive into your platform landscape — licences, usage, architecture, skill gaps, and cost efficiency — with a prioritised improvement roadmap.
Ideal for: Organisations deploying, migrating, or scaling a data platform
A full platform build — from architecture and migration through to governance, integration, and go-live — delivered in 8–12 weeks with a dedicated certified team.
Ideal for: Teams that want expert-managed platform operations across all tools
We manage your platforms end-to-end — monitoring, optimisation, incident response, and a dedicated engineering partner on call across all five platforms.
Common Questions
A data platform is the technology stack where your data lives, is processed, and is queried — typically a cloud data warehouse or lakehouse (Snowflake, Databricks, BigQuery, Redshift), plus the BI tools (Tableau, Power BI, MCI) that consume it. Decision Foundry's data platform practice spans the design, deployment, optimisation, and ongoing operation of all of the above.
Platform selection and architecture (which warehouse, which lakehouse pattern, which BI tool, which AI activation); implementation across Snowflake, Databricks, Power BI, Tableau, and MCI / Datorama; cross-platform integration; cost and performance optimisation; AI activation across the stack (Cortex, Mosaic, Einstein, Tableau Pulse); and the governance layer that holds it all together.
A data warehouse is one component (structured data, SQL-queryable); a data lake is another (raw, schema-on-read). Modern lakehouses (Databricks, Snowflake's hybrid features) combine them. A data platform is the whole stack — storage + compute + governance + access + BI + AI. Most enterprises don't pick one technology; they assemble a platform from several. We help design which combination matches your scale and use cases.
A focused single-platform stand-up (e.g., new Snowflake or Databricks environment + initial data load + basic governance) runs 12–16 weeks. A full enterprise platform build (multi-warehouse architecture, governance, observability, BI activation, AI integration) runs 6–12 months. Cost varies with data volume, source-system count, and integration depth. Every engagement starts with a discovery call.
It depends on workload mix. Snowflake leads on SQL-heavy analytics, ease of use, and Salesforce integration via zero-copy. Databricks leads on data science, ML, streaming, and engineering-heavy workloads. Many enterprises run both — Snowflake for the BI and warehouse layer, Databricks for engineering and ML. We're a Select Partner of one and a Premier Partner of the other, so we'll give you a vendor-neutral recommendation based on your workloads.
We're certified across the full enterprise data stack — Salesforce SELECT Partner, Snowflake Select Partner, Databricks Premier Partner, plus deep Tableau, Power BI, and MCI expertise. 200+ data projects delivered. Most consultancies specialise in one or two of these; we genuinely operate across all of them, which means we can architect the right platform mix rather than recommending whichever vendor we sell most of.