Analytics your executives actually use — and trust enough to act on.
Most enterprises don't lack dashboards. They lack a governed metric layer, embedded insights inside the applications people already work in, and an AI layer that explains the numbers.
The patterns we see in every stalled analytics program
If any of these sound familiar, the fix is rarely a new tool.
Every department has a different number for the same KPI
Finance, sales, and ops each pull from different sources. Executive meetings start with reconciliation, not decisions.
Hundreds of dashboards. Nobody uses them.
Reports pile up because delivery beats governance. Adoption is <20%.
Reporting lives outside the workflow
Reps log into Salesforce, then switch to Tableau to find the number, then switch back to act. Decisions leak between tabs.
Tableau + Power BI + CRM Analytics — and no strategy
Each team bought what they liked. Licenses overlap. Certifications don't. Nobody owns the semantic model.
Self-service turned into self-serve chaos
Business users build their own dashboards on ungoverned extracts. Numbers drift.
AI-augmented analytics — promised, not delivered
Tableau GPT, Cortex Analyst, Einstein Discovery are licensed but unconfigured.
We don't stop at the chart. We connect the metric to the decision.
Most enterprise analytics problems are semantic-model problems wearing a dashboard badge.
- Governed metric layer firstBefore a single dashboard ships, the KPI dictionary is agreed, versioned, and enforced in code.
- Embedded in the workflowAnalytics surface inside Salesforce, the ERP, and the tools your users already live in — not a separate portal nobody opens.
- Platform-fluent, not platform-loyalTableau, Power BI, CRM Analytics, Pulse, Looker, ThoughtSpot — we pick per audience and workload, not per vendor incentive.
- AI-augmented by defaultCortex Analyst, Einstein Discovery, Tableau GPT deployed on a semantic model that makes natural-language actually work.
The reference analytics architecture
Blueprint of a High-Impact
Analytics Transformation Journey
Every phase of your analytics transformation — vision to adoption
Four phases, one partner.
Envision
- BI platform evaluation & rationalisation
- KPI & metric framework definition
- Audience & adoption strategy
- Self-service access model
- Analytics maturity assessment
Model
- Semantic & metric layer design (dbt, Looker, Tableau)
- Data modeling for analytics consumption
- Row-level security & access controls
- Master metric dictionary & lineage
- Governance & certification workflow
Build
- Executive & operational dashboards
- Visualization layer (Tableau, Power BI, CRM Analytics)
- Embedded analytics inside apps
- Pulse & Einstein Discovery activation
- Cortex Analyst & NL-query enablement
Activate
- Reporting ops & dashboard lifecycle
- Adoption analytics & usage telemetry
- Data literacy & enablement programs
- Secure external data sharing
- Continuous dashboard enhancement
A clear analytics maturity roadmap
Benchmark where you are. Define where you're headed.
Reactive Reporting
Data pulled manually. Spreadsheets rule.
Structured Insights
Standardised dashboards track operational KPIs.
Self-Service
Business users explore certified data on a governed semantic layer.
Predictive & AI-Augmented
Forecasts, anomaly detection, and NL queries are embedded in workflows.
Embedded & Augmented
Analytics is invisible — surfaced inside every app, every workflow, every decision point.
The analytics platforms we actually deliver
Platform-agnostic in approach.
| Use case | Primary platforms | Where we use each |
|---|---|---|
| Executive & board dashboards | Tableau, Power BIPulse for AI-generated metric digests | Curated exec views with certified metrics, mobile-ready. Pulse for proactive metric monitoring. |
| CRM & in-app analytics | CRM Analytics, Tableau EmbeddedEinstein Discovery | Analytics surfaced inside Salesforce — at the opportunity, case, or account level, where reps actually decide. |
| AI-augmented & natural-language | Cortex Analyst, Tableau GPTEinstein Copilot, Power BI Copilot | NL-to-SQL and narrative generation on a governed semantic model — not a ChatGPT wrapper on raw tables. |
| Semantic & metric layer | dbt Semantic Layer, LookerTableau Data Models, MetricFlow | Versioned KPI dictionary that every BI tool, notebook, and agent reads from. Ends the "three numbers for one KPI" problem. |
| Self-service & exploration | ThoughtSpot, Tableau ExplorerPower BI Desktop, Databricks SQL | Ad-hoc exploration on certified data. Query federation on lakehouse, warehouse, and Data Cloud. |
| Operational & real-time dashboards | Databricks SQL, SnowflakePower BI DirectQuery, Tableau Live | Low-latency dashboards on streaming sources — call center, manufacturing, trading, e-commerce. |
| Data storytelling & activation | Tableau Stories, PulseEinstein Discovery, Slack / Teams delivery | Insights delivered where decisions happen — in Slack, in email, in a mobile push, not in a dashboard nobody opens. |
| External / partner analytics | Snowflake Secure Data SharingPower BI Embedded, Tableau Server | Governed data-sharing for partners, vendors, and customers — without data egress or licensing headaches. |
Your KPIs aren't generic — neither is our approach
Every build starts with your industry's metrics, regulations, and decision cadence.
Clinical, quality, and population-health analytics
Governed metrics across EHR, claims, SDOH.
- HEDIS / Stars / HCC quality dashboards
- Population health & care-gap analytics
- Clinical ops: throughput, LOS, readmissions
- Trial operations & study monitoring
- HIPAA-safe sharing with providers & payers
Risk, claims, and portfolio intelligence
Loss-ratio, fraud, and underwriting dashboards that meet NAIC, state.
- Loss-ratio & combined-ratio tracking
- Fraud & SIU analytics on streaming claims
- Portfolio risk & concentration dashboards
- Agent & broker performance analytics
- Regulatory & audit-ready reporting
Revenue, product, and customer analytics
Unified revenue, product telemetry, and CS signals in one metric layer.
- Pipeline, forecast, and NRR dashboards
- Product telemetry & feature adoption
- PQL & expansion-signal reporting
- Churn & renewal-risk dashboards
- Embedded analytics for your own customers
We Blend Our Data Expertise With Other Services
To Transform Your Vision Into Reality
Pre-built IP that compresses months into weeks
Reusable assets that shorten every engagement.
Analytics Maturity Assessment
2-week benchmark that scores current maturity, audits BI sprawl.
KPI Dictionary Starter Pack
Pre-built industry metric catalogues for HCLS, Insurance, and SaaS — finance, revenue, operations.
Executive Dashboard Library
Tableau and Power BI templates for CFO, CRO, COO.
CRM Analytics Fast-Start
Production-ready Sales, Service, and Revenue Cloud analytics apps live in 6.
Embedded Analytics Framework
Reference patterns for embedding Tableau, Power BI, and CRM Analytics inside Salesforce, custom apps.
NL-Query Enablement Kit
Semantic-layer standards plus Cortex Analyst / Tableau GPT configuration so natural-language queries return the.
Credentialed across every platform we deliver on
Partnerships are how we get roadmap access, pre-release enablement, and senior platform engineering on your program.
- Salesforce Summit Consulting Partner
- 1000+ Customer Success Stories
- 200+ Salesforce Certified Experts
- 100+ Industry Accelerators
- Clauude Partner Network
- Claude-powered agents in production across regulated industries
- 40+ enterprise Agentforce deployments grounded on Claude
- Teqfocus runs on Claude — we are our own first client
- AWS Advanced Consulting Partner
- 150+ Active AWS Engagements
- 50+ Skilled Practitioners & Cloud Engineers
- Data & Analytics Competency + 6 Designations
- Snowflake Services Partner
- Expertise in Data Lake to Snowflake
- 50+ Customers across key industries
- 20+ SnowPro Certified Experts
- Certified Databricks Consulting Partner
- 20+ AI & Data Workloads delivered
- 20+ Databricks-Certified Experts
- Expanding focus in Healthcare & Financial Services
Why analytics leaders choose us over the Big 4
Depth, accountability, skin in the game.
Dual-stack practice
Tableau and Power BI, CRM Analytics and Databricks SQL — credentialed depth in both worlds means.
Semantic layer first
Most shops start with a dashboard.
Embedded where work happens
Analytics inside Salesforce, the ERP, the support console — so reps.
AI-augmented, not AI-hyped
Cortex Analyst, Tableau GPT, Einstein Discovery — deployed on a semantic layer that.
Adoption accountability
We measure usage, not deliverables.
Operate past go-live
Reporting ops covers dashboard lifecycle, certification, metric governance.
Ready to make analytics a decision system?
Start with a 2-week Analytics Maturity Assessment.
Questions leaders ask before kicking off analytics work
Usually — but not always to one tool. Rationalisation isn't about consolidating to a single BI tool. It's about agreeing which tool serves which audience (exec, operator, rep, customer-facing) and ending the overlap that causes duplicated dashboards and cert sprawl. A typical engagement cuts platform spend 20–40% while improving adoption.
They work when the semantic layer does. Natural-language analytics returns junk on raw tables. On a governed semantic layer with business-friendly names, synonyms, and certified metrics, accuracy jumps dramatically. We deploy the semantic layer first — then turn on NL features. Sequencing matters more than tool choice.
With a versioned metric dictionary, not a meeting. We build a single source of certified metrics — definition, owner, formula, refresh cadence, lineage — expressed in dbt or Looker so every BI tool reads from it. Change requests go through a pull request, not a steering committee. The KPI stops drifting because it has one canonical definition in code.
Most internal BI teams are strong on reporting but stretched on semantic modeling, governance, and embedded analytics. We accelerate the gap — semantic layer, metric dictionary, AI-augmented query, Salesforce embeds — and leave behind patterns your team can extend. We're accelerators, not replacements.
First governed executive dashboard suite live in 8–12 weeks. Broader analytics transformation 6–12 months. Sequence: 2 weeks for maturity assessment and KPI dictionary → 4–6 weeks for semantic layer and first exec dashboards → rolling waves for domain-specific analytics, embeds, and AI augmentation.
Adoption telemetry and a monthly review. Every dashboard is tagged with an owner and a certification. Usage, view frequency, and query performance go into an adoption dashboard reviewed monthly. Dashboards under threshold get retired, redesigned, or re-enabled. Zombie content doesn't survive the cadence.
Yes — and it's the same team that delivered the build. Reporting ops covers dashboard lifecycle, metric governance, change requests, certification, and adoption enablement. No knowledge transfer gap, no finger-pointing. Contracts typically 12–36 months.
Start with an Analytics Maturity Assessment. 2 weeks. You leave with a maturity score, an audit of current BI sprawl, a rationalisation recommendation, a KPI dictionary starter pack for your industry, and a 90-day execution plan — boardroom-ready whether you continue with Teqfocus or not. Book the assessment.
