The AI-Ready Semantic Layer practice.
Five productized engagements for mid-market multi-unit operators on Snowflake or Databricks — designed, deployed, and handed off to your team.
What we do, and what we don’t
DirectionalData is the AI-Ready Semantic Layer practice for mid-market multi-unit operators on Snowflake or Databricks. We turn fragmented warehouses and inconsistent BI metrics into a governed semantic layer that Tableau, Power BI, Cortex Analyst, Databricks Genie, and your AI agents all use as one source of truth — designed, deployed, and handed off to your team in 60 to 120 days. We work with both the open OSI standard and proprietary platform-native semantic models (Tableau Published Data Sources, Power BI Semantic Models). We do not staff-augment, we do not resell tools, and we do not chase Fortune-100 engagements that need staffing a single principal cannot deliver.
Three productized core engagements
Each is scoped from a complimentary 60-minute discovery conversation.
Two larger engagements when the scope demands it
Semantic Layer Diagnostic
Who this is for
- A mid-market operator whose dashboards disagree on basic numbers and whose CFO is tired of “whose number is right” meetings.
- A company that has just funded an AI assistant pilot and wants to de-risk the metric layer before the rollout.
- A PE operating partner who needs an independent, vendor-neutral assessment of a portfolio company’s analytics maturity.
- An analytics leader who knows the metric layer is the next problem but needs an outside-in audit to make the case.
What we deliver
- Metric inventory across Power BI, Tableau, AI assistants, and Excel pulls — with a numbered conflict-and-drift report.
- Five to seven structured stakeholder interviews surfacing what numbers people actually trust, and why.
- Target-architecture recommendation: which platform’s semantic layer (Snowflake Semantic Views, Databricks Metric Views, Power BI Tabular on Direct Lake, or a hybrid) and which 25–50 metrics to build first.
- ROI calculator pre-filled with your data: avoided rebuilds, BI license consolidation, analyst time saved.
Investment
Duration: 4 weeks
Frequently bought as a small SOW under a master agreement. Also listed on Snowflake Marketplace and Databricks Marketplace as a packaged offer.
Foundational Semantic Layer Build
Who this is for
- A $250M–$3B mid-market operator on Snowflake or Databricks ready to fund the metric truth plane as a real program, not a side project.
- A company that has just rebuilt its data warehouse and needs the semantic layer designed correctly on top from the start.
- A team that tried to build a semantic layer in-house, stalled, and wants senior outside help to finish the job.
- A PE-backed portfolio company where the deal thesis depends on analytics capability being usable inside the first year of hold.
What we deliver
- The full 90-day arc: stabilize the conversation (weeks 1–2), pick the platform and define the first ten metrics (weeks 3–6), first wave of BI and AI consumers (weeks 7–11), stewardship live and backlog expanded (weeks 12–13).
- Implementation in the chosen platform: Snowflake Semantic Views + Cortex Analyst, Databricks Metric Views + Genie, or Power BI Tabular Semantic Model on Direct Lake.
- Migration of the highest-pain BI dashboards to consume the semantic layer instead of their own definitions — in Tableau (Published Data Sources), Power BI, or both.
- Metric stewardship forum stood up; CFO or deputy chairs; we sit in for the first three months and then hand off.
- OSI-compatible export of the metric definitions so they stay portable.
Investment
Duration: 60–120 days (most clients finish in 90–100 days)
Payment structure: three milestones (kickoff · first ten metrics live · stewardship transfer) or flat monthly during the engagement.
Pricing is indicative. Final scope determined after a complimentary 60-min discovery call.
AI-Readiness Semantic Layer Sprint
Who this is for
- A company that has launched (or is about to launch) an AI assistant on top of its data warehouse and is hitting accuracy problems.
- A CFO funding a Copilot for finance or operations who wants the underlying metric layer audited before the assistant ships.
- A team that has a working semantic layer already but whose AI agent returns inconsistent numbers and they don’t know whether the layer or the model is at fault.
What we deliver
- A 100-question benchmark in your domain (restaurant ops, retail merchandising, distribution, insurance, mortgage, ad-tech) that exercises the AI agent at the questions executives actually ask.
- Pre-sprint accuracy measurement on raw warehouse access (typical: 30–55%).
- The top 20–30 metric definitions the agent needs, pre-built in your platform’s semantic layer.
- Post-sprint accuracy measurement on the same benchmark (typical: 88–95%).
- Hand-off runbook so the team can extend metric coverage as new questions emerge.
Investment
Duration: 6 weeks
Sprint can run standalone or as a follow-on to the Foundational Build.
Standards-based and proprietary semantic models
We work with both. The right answer depends on your stack, your AI agents, and how much platform-portability you need to preserve.
OSI — Open Semantic Interchange
The vendor-neutral standard finalized in January 2026. Backed by Snowflake, Salesforce, dbt Labs, BlackRock, RelationalAI, Atlan, Alation, ThoughtSpot, Sigma, Cube, and others — with Amazon, Google, and DataHub in the working group. Use this when you want metric definitions that survive a future platform change. Our default for clients running both Snowflake and Databricks.
Tableau Published Data Sources
Tableau’s native semantic model. Every Pulse digest, Tableau Cloud workbook, and Agentforce agent reads from the same Published Data Source. Strongest fit for Tableau-first stacks where the semantic layer is unlikely to feed non-Tableau consumers.
Power BI Semantic Models
The Power BI tabular model on Direct Lake over OneLake. The reference enterprise implementation, especially in Microsoft Fabric environments. Strongest fit when Power BI is the primary BI tool and Copilot is the primary AI surface.
For most mid-market clients we build a hybrid: the canonical metric definitions live in the OSI-compatible platform layer (Snowflake Semantic Views or Databricks Metric Views), and the proprietary BI semantic models (Tableau Published Data Sources, Power BI Tabular) consume those definitions instead of holding their own.
Engagement comparison
| Engagement | Best for | Duration | Investment |
|---|---|---|---|
| 1. Semantic Layer Diagnostic | Conflicting dashboards; AI pilot launching soon; PE portfolio assessment. | 4 weeks (fixed) | $15K–$25K |
| 2. Foundational Semantic Layer Build | Full semantic layer program for a mid-market operator on Snowflake or Databricks. | 60–120 days | $85K–$165K |
| 3. AI-Readiness Semantic Layer Sprint | AI assistant returning wrong numbers; Copilot rollout that needs the metric layer audited first. | 6 weeks (fixed) | $35K–$75K |
| 4. BI Migration with Semantic Layer | Legacy BI estate (Sisense, Looker, MicroStrategy, Cognos, SSAS) moving to Tableau or Power BI. | 14–20 weeks | $250K–$400K |
| 5. Fractional Semantic Layer Architect | Ongoing governance after any core engagement; CFO needs a senior advisor on retainer. | Monthly (90-day min) | $12K–$18K/mo |
Not sure which fits?
Book a 60-minute discovery call. We listen first, ask what we need to understand the situation, and recommend an engagement honestly — even if it isn’t one of ours.