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CASE STUDY
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The revenue numbers your team
actually trusts.

We build the reporting layer that ends the three-different-revenue-numbers meeting, pipeline and forecast dashboards, rep scorecards and board packs, reconciled across CRM, billing and support. And we fix the fields and stage definitions underneath, because a dashboard on dirty data is just a faster way to be wrong.
150+
dashboards in daily executive use
10 days
median time to first trusted dashboard
92%
of our dashboards still in use a year later
Revenue overview
one board · every system
REAL-TIME
$4.8M▲ 23% vs last quarter
SOURCESZohoHubSpotQuickBooksStripe
31%
win rate
18
boards, one source
0
manual reports
Teams reporting with Encloud
NORTHBRIDGEmeridianVANTAGECaremontorbita

Why nobody in the room
trusts the dashboard.

When the CEO gets three revenue numbers from three tools, the problem is rarely the charting, it starts further down. Four patterns we find in almost every reporting audit.
01TRUTH
Three tools, three revenue numbers
The CRM says one number, billing says another, and the board deck, assembled by hand on Sunday night, says a third. Every meeting starts with reconciliation instead of decisions.
02FIELDS
Reports built on fields nobody fills
Close dates set to the last day of the quarter, blank amount fields, deals that jump from stage one to closed-won. Every chart downstream inherits the gaps, and everyone quietly knows it.
03DEFINITIONS
"Qualified" means five different things
Sales, marketing and finance never agreed what a stage, an SQL or ARR actually means. So the forecast roll-up is an argument, not a number, and no dashboard can fix a definition problem.
04SPRAWL
Fifty dashboards, zero answers
Every question spawned another report, nobody retired the old ones, and now nobody knows which view is current. So the team exports to spreadsheets, and the CRM stops being the source of truth.

Our fix: treat reporting as a data product.

A dashboard is the last mile. What makes it trustworthy is everything behind it, agreed definitions, fields people actually fill, one reconciled pipeline of record, and an owner. We build all four layers, then the charts.
Reconcile your numbers →
01
Agree what the words mean first
Before a single chart, we run the definitions workshop: what a stage means, when a deal counts, how ARR is computed. The output is a metric dictionary the whole company signs, every report traces back to it.
Metric dictionaryStage definitionsOne owner per metric
02
Repair the fields the reports stand on
Field-fill audits, validation rules, required-at-stage fields and dedup passes, so the data flowing into every chart is complete enough to bet a forecast on. Hygiene first, visuals second.
Field-fill auditValidation rulesDedup passes
03
One pipeline of record, reconciled to finance
We tie CRM revenue to Stripe and QuickBooks line by line, and pull support signals alongside. Native reporting where it holds; a BigQuery or Snowflake warehouse with dbt models when it runs out.
CRM-to-finance tie-outZoho Analytics / HubSpotWarehouse when needed
04
Fewer dashboards, each one owned
A small role-based set, rep scorecard, manager pipeline view, executive board pack, each with a named owner and a scheduled digest. We retire the graveyard so the current view is never in doubt.
Role-based setScheduled digestsReport retirement

Reporting services, source to boardroom

All CRM engineering services
01
Pipeline & Forecast Dashboards
Stage conversionWeighted forecastSlippage trackingCoverage ratios
02
Rep & Team Scorecards
Win rates by repActivity vs outcomeRamp trackingQuota attainment
03
Executive & Board Reporting
Board packARR & churn viewsScheduled exportsVariance notes
04
Revenue Reconciliation
CRM-to-finance tie-outInvoice matchingVariance reports
05
Data Quality & Field Hygiene
Field-fill auditRequired-at-stageValidation rulesDedup & backfill
06
Zoho Analytics & HubSpot Reporting
Cross-module reportsCustom datasetsCalculated fieldsReport training
07
Warehouse & Custom BI
BigQuery / Snowflakedbt semantic modelsGrafana boardsNightly syncs
08
Scheduled Digests & Alerts
Monday forecast emailThreshold alertsAnomaly flags

How trustworthy reporting ships

Five stages, each with named deliverables. Hover a stage to see what you get.
01
/ 05
Audit
01Trace the three numbers
We take your CRM number, your billing number and last month's board deck and reconcile them line by line, then audit field fill rates and the report sprawl. You see exactly where the numbers diverge and why.
Variance reportField-fill auditReport inventory
02Agree what the words mean
One workshop with sales, marketing and finance to fix stage definitions, metric formulas and ownership. Contentious on Tuesday, settled by Friday, and written down so it stays settled.
Metric dictionaryStage definitionsKPI owner map
03Fix the data underneath
Validation rules, required-at-stage fields, dedup passes and a targeted backfill of the fields your key reports depend on. The CRM becomes worth reporting on before we report on it.
Field cleanup logValidation rulesTargeted backfill
04Ship the dashboard set
The role-based set built in Zoho Analytics, HubSpot reporting or a warehouse with dbt models, whichever your questions actually require. You review live dashboards weekly, not mockups.
Role-based dashboardsdbt / dataset modelsRollout by role
05Keep the reports honest
Fields change, pipelines get restructured, and unmaintained reports silently rot. Change control on the metric layer, scheduled digests, and a quarterly metric review keep the numbers current.
Scheduled digestsMetric change logQuarterly review

Reporting outcomes in spotlight

All case studies
CRM, billing and the board deck finally agree
B2B SaaSRevenue Reconciliation
VANTAGE
±1%CRM-to-finance variance, down from 14%
Client portrait
We used to spend the first twenty minutes of every board meeting explaining why the numbers differed. Now there is one number.
Priya Raman
CEO, Vantage
A weighted forecast the CFO signs without edits
LogisticsForecast Dashboards
NORTHBRIDGE
93%forecast accuracy over four quarters
Client portrait
Stage definitions did more for our forecast than any dashboard ever did. Encloud fixed those first, then the dashboard was easy to trust.
Tomas Lindqvist
VP Sales, Northbridge Freight
The board pack that used to eat a week builds itself
HealthcareExecutive Reporting
Caremont
4 dayssaved per month on reporting prep
Client portrait
The pack lands in my inbox on the first of the month, reconciled and annotated. My analysts do analysis now instead of assembly.
Elena Vasquez
CFO, Caremont Health
Rep scorecards made the CRM worth updating
ManufacturingRep Scorecards
meridian
2.4×more deal updates per rep per week
Client portrait
Once reps saw their own scorecard pulled straight from the CRM, the data cleaned itself up. Nobody wants a blank chart with their name on it.
Greg Sorensen
Sales Director, Meridian Manufacturing
Native reporting hit its ceiling; the warehouse answered
RetailWarehouse & BI
orbita
14data sources unified in BigQuery
Client portrait
Store, ecommerce, CRM and support data in one model, with dbt tests catching problems before we see them in a chart.
Nadia Osei
Head of Analytics, Orbita

Put a senior data pod on your reporting, not a dashboard factory.

A data engineer, a CRM engineer and a delivery lead who understand pipelines and P&Ls, building the metric layer, the dashboards and the habits that keep them accurate.
150+
Dashboards in daily use across client teams
10 days
Median time from kickoff to first trusted dashboard
92%
Of our dashboards still in use a year later

The stack behind the numbers

Native reporting pushed to its real limit, and a proper warehouse stack ready for the day your questions outgrow it.
CRM & pipeline sources
Billing & support systems
Warehouse & modeling
Dashboards & delivery
The systems of record we report from, and clean up so they deserve the title.
ZohoZoho CRM & Analytics
HubSpotHubSpot
SSugarCRM
AirtableAirtable

Book a reporting audit, not a sales call.

45 minutes with a data engineer. Bring your CRM number, your billing number and last month's board deck, leave knowing exactly where they diverge, which fields are causing it, and whether native reporting or a warehouse is the right fix.
No obligation, no prepared pitch
NDA on request before you share any numbers
Honest read on native reporting vs warehouse, no over-build agenda
4.9 / 5average across 90+ reporting engagements
The audit found our 14% CRM-to-finance gap in the first session, three field fixes and a definitions workshop closed it.
Priya Raman
CEO, Vantage
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Frequently asked questions

Deciding between fixing native reporting and standing up a warehouse, or just tired of numbers that never match? Bring the question to a reporting audit and get an answer backed by your own data.
Talk to a data engineer →
Why do our CRM reports disagree with finance?+
Usually three causes stacked together: deals counted at different moments (closed-won vs invoiced vs paid), currency and discount handling that differs between systems, and CRM fields that were never filled consistently. A reporting audit reconciles the two numbers line by line and tells you which cause dominates, the fix is often field rules and definitions, not new software.
Native CRM reporting vs a BI warehouse, which do we need?+
Native reporting, Zoho Analytics, HubSpot reports, SugarCRM dashboards, covers most teams if the data model is clean, and it is dramatically cheaper to maintain. You outgrow it when you need to join CRM data with billing, product or support data at scale, keep history the CRM overwrites, or answer questions across more than a couple of systems. We tell you honestly which side of that line you are on.
How long until we have a dashboard we can actually trust?+
The first trusted dashboard typically ships in about ten days, after the variance audit and definitions workshop, on top of targeted field fixes. A full role-based reporting layer with reconciliation and scheduled digests usually runs four to eight weeks depending on how many systems feed it.
How do reports stay accurate when we change fields or pipeline stages?+
This is where most reporting quietly dies. We put the metric layer under change control: every report traces to the metric dictionary, so a field or stage change triggers a known list of reports to update, with dbt tests or dataset checks catching breaks before your executives do. Restructure the pipeline and the reports move with it.
Who maintains the dashboards after launch?+
Your choice. Everything we build is documented and owned by you, many clients run it with their own admin after handover training. Most keep a light monthly retainer: we handle metric changes, quarterly reviews and new report requests, and you can stop anytime.
Our CRM data is a mess. Can you still build reporting on it?+
Yes, messy data is the normal starting point, not a blocker. The audit shows which fields your key reports depend on; we repair and backfill those specifically rather than boiling the ocean. Rep scorecards then keep the data clean, because nobody wants a blank chart with their name on it. For deeper cleanup we pair this with our CRM migration and data service.
Do you work with Zoho Analytics, HubSpot reporting or custom BI?+
All three. We are a Zoho Analytics consultant and HubSpot reporting shop first, native tools done properly beat a premature warehouse. When you genuinely need more, we build on BigQuery or Snowflake with dbt models and Grafana or sheet-based delivery, and you own the whole stack.
What does a reporting engagement cost?+
The reporting audit is fixed-price. Dashboard builds are quoted as a fixed project after the audit, native-tool engagements land well below warehouse builds, which is one reason we do not push warehouses you do not need. Ongoing maintenance runs as a small monthly retainer you can stop anytime.

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