Refresh latency disguised as “real-time”
Direct-query modes buckle under real joins. Teams fall back to extracts and scheduled refreshes — then decisions lag.
For business users + data teamsSelf-service with guardrails2026
Analytics
Stop arguing over which dashboard is right.
Keep metrics, source evidence, decisions, and next actions connected so business users and data teams can discuss the same number.
Voice of customer
What teams praise (in their own words)
The emotional “yes” behind modern BI: self-service, trust, and speed.
“It has been the easiest for my business users to adopt and actually unlock self-service. Feels like Excel on steroids.”
Reddit · r/BusinessIntelligence ↗
“A fantastic BI tool for data governance, data democracy, consistent metrics, and single-use code.”
Reddit · r/BusinessIntelligence ↗
“Once I had a report set up for automatic refreshes I could put my feet up. It was the first app I could deploy to genuine non-tech business users and they got real value from it.”
Reddit · r/PowerBI ↗
Why analytics stacks break at scale
The technical issues that show up in every 2026 review.
Recurring failure modes from G2, Reddit, and operator forums.
Metric drift (semantic layer fragmentation)
Definitions split across tools, query languages, and notebooks. Same KPI, different number. Endless reconciliation.
Slow at scale
Large models, complex row-level security, and heavy calculations turn filters into waiting — killing exploration.
Brittle connectors
SaaS APIs change. Rate limits hit. Schemas shift. "Plug-and-play" becomes ongoing engineering maintenance.
Governance that blocks self-service
The security model is either too loose (shadow dashboards) or too strict (ticket queues). Both create workarounds.
DevEx gaps (versioning, CI/CD)
Binary report files and ad-hoc logic make reviews, testing, and safe rollbacks hard. Changes ship slowly and break often.
The real cost of legacy BI
What it feels like when dashboards do not agree.
The pain points business and data teams describe most often.
Dashboard sprawl
Hundreds of near-duplicates, no clear ownership, weak discovery — driving people back to spreadsheets.
“Export to Excel” is still the default
Users leave the BI tool to answer simple follow-ups or adjust assumptions, so the final decision happens in a spreadsheet.
Steep learning curve
Business users face query languages, modeling concepts, brittle filters — so self-service stalls without analysts.
Trust is fragile
Sampling, inconsistent totals across pages, and missing source evidence make teams second-guess dashboards in meetings.
Collaboration is clunky
Comments, approvals, and "why did this change?" threads live outside the tool — disconnected from the chart.
Pricing surprises
Costs jump with viewers, capacity, or data volume. Adoption becomes a budget negotiation, not a product win.
What teams actually need
The job your analytics stack should have been doing all along.
Proactive insights (push, not pull)
Alerts that explain what changed, why it matters, and what to do — without hunting across 12 dashboards.
A universal metrics layer
One definition per KPI, reusable across BI, spreadsheets, and apps, with tests and change history.
Writeback & scenario planning
Plan and forecast directly where the metrics live. No offline spreadsheets and manual reconciliation.
Fast self-service with guardrails
Business users can explore safely, while governance stays intact (schemas, contracts, source evidence, approvals).
AI that cites its work
Natural-language questions return answers with sources, SQL, and drill paths, so teams can validate before acting.
Embedded analytics that feels native
Insights inside the workflow (CRM, ERP, support) with shared permissions and the same metric definitions everywhere.
Pilot access
Ready to clean up the dashboard debate?
Bring your warehouse, your top three KPIs, and one dashboard people do not trust. We'll map the metric, source evidence, owners, and decision workflow.