Brainforge Knowledge

omni-ramp-up-memo

14 min read

Establishing Omni: 1–2 Month Proof of Concept and Accelerator — Assessment and Recommendation

Prepared by: Brainforge Prepared for: [Client stakeholder names and titles] Date: 2026-02-28 Status: Draft

Objective

This memo helps you decide how and when to establish Omni for data exploration, visualization, and product analytics on top of your data warehouse. We recommend a phased 1–2 month proof of concept (PoC), followed by an accelerator or full implementation: connect the warehouse, build the first topics and dashboards, validate with real business questions, then expand domains and hand off to your team.

Why This Decision Matters Now

Teams migrating from legacy BI (Looker, Tableau, or spreadsheets) or adding a modern analytics layer often face scattered reporting, metric drift, and long waits for answers. When the warehouse is the source of truth but reporting lives elsewhere, duplication and inconsistency grow. Business users either wait on analysts or export CSVs to answer their own questions—and definitions vary by team.

Current state challenges:

  • BI sprawl and migration pressure — Legacy BI tools are costly to maintain or are being phased out; migrating hundreds of reports 1:1 is rarely the goal. You need a governed layer where metrics are defined once and stay consistent.
  • No semantic layer — Different teams calculate the same metric differently (e.g. contribution margin, LTV, payback). Leadership meetings surface conflicting numbers because there is no single source of truth.
  • Underused data — Marts and models exist but are not self-serve; exploration requires SQL or analyst time. Many potential users never get to ask their own questions.
  • Product and GTM misalignment — Product analytics (events, funnels, retention) often sit in a different tool from operational or revenue reporting. Connecting behavior to outcomes requires manual stitching.

Establishing Omni on top of your existing warehouse (and, where relevant, Segment or other pipelines) turns the warehouse into a place where domain experts can explore data, build dashboards, and get answers through a governed semantic layer (Topics) and optional AI-assisted exploration. Deciding the pace and scope now—starting with a 1–2 month PoC—avoids ad-hoc experiments and aligns stakeholders on a clear path.

Evaluation Criteria

We used these criteria to compare phasing and scope options:

  1. Time to first value — How quickly can a business user get a real answer or dashboard from Omni? Prefer options that deliver a working pilot within 4–6 weeks of warehouse connectivity.
  2. Fit with existing platform — How well does the path assume a warehouse (e.g. Snowflake) and optional event pipeline (e.g. Segment) are in place? Prefer options that build on current roles, schemas, and marts.
  3. Governance and semantic layer — Can analysts define metrics once (Topics) so everyone uses the same definitions? Prefer options that use the semantic layer as the boundary and avoid metric drift.
  4. Scalability — Can we add more domains (e.g. sales, product, operations) without re-architecting? Prefer options that treat “one pilot domain first, then expand” as the pattern.
  5. Handoff and adoption — Can the client own and extend topics and dashboards after Brainforge steps back? Prefer options that include documentation, training, and clear ownership.

Options Considered

Option 1: 1–2 month phased ramp-up (PoC first, then accelerator)

Establish Omni connectivity to the warehouse in the first 2–4 weeks; agree the pilot domain and source tables/views. Build the first Topics and 1–2 dashboards for that domain, validate with real questions and business owners, and iterate. In the second phase (accelerator/implementation), add more domains using the same pattern, implement event tracking or funnel definitions if needed, and document/train for client ownership. This is the recommended approach.

Option 2: Big-bang rollout (all domains at once)

Connect Omni and define Topics and dashboards for multiple business areas in parallel. Delivers breadth quickly but spreads effort thin, increases the chance of mis-scoped or unused dashboards, and makes it harder to learn from one success (e.g. date filters, cohort definitions) before scaling. We did not recommend this because it increases risk and complicates handoff.

Option 3: PoC-only (defer full implementation)

Run a short PoC (e.g. one domain, 4–6 weeks) and defer the decision to roll out more broadly. Valid for organizations that need to prove value or secure budget before committing. Compatible with the recommended path—PoC is the first phase; accelerator/implementation can follow once the pilot is validated.

Options not evaluated in depth

  • Other BI tools (Looker, Tableau, Sigma, etc.) — This memo assumes Omni is under evaluation or preferred (e.g. for semantic layer, AI-assisted exploration, or partnership fit). Tool comparison is out of scope.
  • Omni without a semantic layer — Topics (or equivalent) are the recommended way to govern metrics; ad-hoc exploration over raw tables was not evaluated for governance reasons.

Comparison

Criterion1–2 month phased (PoC first)Big-bang (all domains)PoC-only (defer implementation)
Time to first valueHigh — pilot in ~4–6 weeksMedium — first answers later, many views at onceHigh — same as phased for pilot
Fit with existing platformHigh — builds on current warehouse/martsMedium — same fit but more concurrent workHigh — same as phased
Governance and semantic layerHigh — one pilot domain, clear boundaryLower — many domains at once, harder to reviewHigh — same as phased
ScalabilityHigh — repeat pattern for each new domainMedium — pattern exists but rollout was riskierMedium — depends on follow-on decision
Handoff and adoptionHigh — one success to document and train onLower — many things to hand off at onceMedium — handoff after PoC only
Implementation time~1–2 months (PoC + accelerator)~2–3 months (higher risk)~4–6 weeks (PoC only)

Our Recommendation

We recommend Option 1: a 1–2 month phased ramp-up with a single pilot domain first, then accelerator or full implementation.

Leading with one domain (e.g. sales, product funnels, or operations) proves value quickly, keeps governance simple, and gives the team a repeatable pattern. Lessons from other implementations—dashboard migration order, date and filter defaults, funnel definitions, and event implementation—are easiest to apply when one domain is in production before expanding. Once business users see consistent metrics and can build or explore in Omni, expanding to more domains and training internal owners is straightforward.

Why not big-bang (all domains at once)

Spreading effort across many Topics and dashboards from day one makes it harder to tune relationships and metrics for any one domain, increases the chance that some assets go unused, and complicates handoff. One pilot domain de-risks the rollout and creates a template for the rest.

Why not deferring Omni entirely

If the warehouse is already the source of truth and legacy BI is costly or being phased out, deferring leaves reporting in a fragile state. A 1–2 month PoC is short enough to justify starting now and aligns with implementation-partner focus on success rather than shelf-ware.

Trade-offs and Risks

  • Pilot domain choice matters — Picking a low-use or politically sensitive area can slow adoption. Mitigate by choosing a domain with clear, frequent questions and a willing business owner.
  • Date and filter defaults — Different tools (and Omni) can default to different date ranges or filters; discrepancies between “old” and “new” dashboards often come from this. Mitigate by aligning date logic and filter defaults early and documenting them.
  • Topic and dashboard maintenance — When source marts or relationships change, Topics and dashboards may need updates. Mitigate by documenting the mapping and including “Omni review” in the team’s change process.
  • Event and funnel definitions — If the pilot includes product analytics (events, funnels), lock definitions with stakeholders before building; rework is costly once dashboards are in use.

Implementation Path

High-level 1–2 month path. Exact dates depend on client calendar and warehouse readiness.

1. Weeks 1–2 — Foundation and pilot scope

Connect Omni to the data warehouse

  • Create a database connection in Omni pointing at the client’s warehouse (e.g. Snowflake, BigQuery, Databricks). Use credentials and a role with read access to the pilot schemas. Omni supports password or key-pair (e.g. RSA for Snowflake) authentication; key-pair is recommended for production.
  • Optionally restrict the connection to specific schemas so the pilot only sees the marts (or staging) in scope; this keeps the model focused and improves performance. See Restricting connection schemas.
  • Confirm RBAC: the Omni connection should use a warehouse role that can read the pilot tables/views. Document which schemas and tables are in scope for the pilot.
  • If the client uses dbt and you want two-way sync (metrics in dbt visible in Omni), connect the dbt project to the same Omni connection. See Connecting dbt to Omni and Integrating dbt.
  • Connect the Omni instance to the client’s GitHub repository so that Topics and model assets are version-controlled and the team can collaborate (e.g. PRs, history). This supports a consistent workflow with dbt when the dbt project lives in the same org/repo and enables tooling (e.g. MCP) that expects Omni + GitHub.

External reference: Connecting Snowflake to Omni (Snowflake); Connect data (overview), Connect a database (all warehouses); Setting up the git integration (GitHub) (version control).

Lock pilot domain and definitions

  • Agree the pilot domain (e.g. sales, operations, product funnels) and the tables/views that will feed the first Topics. Align Topic design to any existing data dictionary or metric definitions so labels and filters stay consistent. If event or funnel definitions are in scope, align with stakeholders (e.g. funnel stages, cohort definitions) before building.

Rough effort: 2–3 weeks for Brainforge + client alignment.

2. Weeks 3–6 — First value (PoC)

Build the first Topics

  • Topics in Omni are curated datasets built on top of your connection’s views/tables. Each Topic has a base view, optional joins, and can define default filters, descriptions, and which fields are exposed. For the pilot, create one or two Topics that map to the pilot domain and agreed metrics.
  • Use Topic parameters to set default filters (e.g. date range) and descriptions so business users see consistent behavior. Document any default or always-applied filters so they match the client’s definitions (e.g. order date vs ship date).
  • Follow Topic best practices: build for your audience (e.g. sales vs finance), keep topics focused, and add sample queries to help users get started. See Curating datasets with topics and Topic best practices.

External reference: Curating datasets with topics, Topic best practices, Topic file parameters, Topic design inspiration.

Context Engineering / Semantic Engineering

  • Treat Topic design as context (semantic) engineering: descriptions, default filters, and sample queries define the semantic layer so that Blobby and other consumers get consistent, interpretable answers. Invest in clear, well-scoped Topics and metadata during the pilot so the layer is well-governed from day one.

Enable Blobby for customer-facing use

  • Blobby is Omni’s AI assistant—the main way business users ask questions and get answers in natural language (and create content from prompts). Blobby works out of the box when Topics exist; there is no separate setup step. Once the first Topics are built and published, Blobby is available for questions and for creating dashboards or analyses.
  • Mention Blobby explicitly in customer- and stakeholder-facing documentation and training (e.g. “Ask Blobby” for self-serve, where to find it in the UI). See Dashboard Assistant (Omni’s doc name for this capability).

Build 1–2 dashboards

  • In Omni, dashboards are built from workbooks: select a Topic, add dimensions and measures, and create charts/tables. Save the workbook as a dashboard and publish it so the business owner can view and filter. Use the same date and filter defaults as in the Topics to avoid discrepancies. Users can also ask Blobby questions or ask it to create content on top of the same Topic (see Blobby subsection above).

Validate and iterate

  • Validate with real business questions; iterate on relationships, metrics, and filters (including date defaults). Run at least one session with the business owner to confirm numbers and usability. Document any date/filter or definition choices so they can be reused.

Rough effort: 2–3 weeks.

3. Month 2 (or follow-on) — Accelerator and handoff

Expand domains

  • Add one or two more domains using the same pattern: new Topics from the relevant marts, default filters and descriptions aligned to the client’s definitions. Implement event tracking or funnel definitions if part of scope.

Permissions and training

Ongoing maintenance

Rough effort: 2–3 weeks.

Decision Points for Leadership

  • Which domain should be the pilot? — This is a business and adoption decision. The pilot should have frequent, concrete questions and a stakeholder who will use the dashboards and give feedback. Options might be sales, operations, product funnels, or revenue—depending on your priorities.
  • Who owns Omni licensing and usage? — Someone on the client side should confirm Omni licensing, seat count, and any usage or cost implications. Brainforge can outline what to check but cannot approve spend or contracts.
  • Who gets training first? — Decide which analysts or business users will be trained to create or update Topics and dashboards. Training is most effective when tied to the pilot domain.
  1. Confirm pilot domain and stakeholder — [Client] to choose the first business area and a business owner who will use Omni. Brainforge to align on the source tables/schemas (e.g. from dbt marts or existing warehouse views).
  2. Confirm warehouse and Omni access — [Client or IT] to confirm warehouse connectivity, roles, and Omni account/access. Brainforge to use this in Week 1 planning.
  3. Kick off foundation — Brainforge to connect Omni to the warehouse and lock pilot domain and sources; schedule PoC kickoff once access and scope are confirmed.

References

Internal (vault and playbook)

  • Omni partnership and positioning: knowledge/sales/sales/partners/omni/transcripts/2026-01-27_uttam_kumaran_and_greg_doherty_omni_d1d2e846.md (retainer, implementation success); knowledge/clients/unassigned/transcripts/2025-09-30_brainforge_x_omni_partnership_discussion_4298a902.md (Omni vs Looker/Tableau, data models).
  • Omni demo (Edge attribution recovery): knowledge/sales/campaign-launch/demos/omni-edge-attribution-recovery-demo-transcript.md.
  • Eden/Default learnings (dashboard migration, funnel definitions): knowledge/clients/unassigned/transcripts/2026-02-19_omni_dashboard_migration_strategy_sync_7e56f32c.md; knowledge/clients/unassigned/transcripts/2026-02-23_omni_eden_review_d785d910.md; knowledge/clients/unassigned/transcripts/2026-02-25_default_dashboards_project_check-in_9c1c0578.md; knowledge/clients/default/transcripts/default_omni_demo.md; knowledge/clients/unassigned/transcripts/2026-02-17_omni_case_study_stand-up_c6f69dd8.md.
  • Strategy and implementation partner role: knowledge/clients/unassigned/transcripts/2026-02-19_brainforge_x_omni_partner_strategy_sync_9897684b.md.
  • Service catalog: Data Tool Implementation, Analysis Layer — knowledge/sales/pricing/SERVICE_CATALOG.md.

External (Omni documentation and resources)

Getting started and setup

Modeling and Topics (semantic layer)

dbt integration

Workbooks, dashboards, and content

Permissions and administration

Onboarding and training

AI and product

Graph View