Dashboards your leadership actually opens, built on data your team trusts.
- 60+BI projects
- Power BIPrimary platform
- 6-12 wksFirst dashboard
- AdoptionMeasured, not assumed
Eight workstreams from raw data to leadership decisions.
Decision discovery
Workshops with operators, finance, sales, and leadership. Output: the 10-20 decisions a quarter that this BI programme will inform. Everything else flows from this list.
Data warehouse design
Star schema or data lakehouse, depending on your scale. Azure SQL, Synapse, Fabric, or Snowflake. Source systems mapped, conformed dimensions, slowly-changing dimension policy.
ETL & data pipelines
Extract from ERP, CRM, finance, marketing, custom systems. Transform with documented business logic, validate, load. Daily or near-real-time refresh, depending on use case.
Dashboard development
Power BI as primary platform, Tableau or Looker if you have an existing license. Dashboards built around decisions, not metrics. Mobile, desktop, and embedded options.
Forecasting & modelling
Time-series forecasts, demand planning, churn models, anomaly detection. Built in Power BI, Python, or R, integrated into the same dashboards your team already uses.
Cloud-first architecture
Azure-native by default (Fabric, Synapse, Data Factory). Cost-aware design, auto-scaling, dev/test/prod separation. AWS or GCP available where mandated.
Governance & quality
Master-data definitions, calculation dictionary, refresh-failure alerting, row-level security. The boring foundations that determine whether anyone trusts the numbers.
Adoption & training
Office hours during rollout, recorded training per role, dashboard usage analytics. The dashboard everyone has but no one opens is a failed project.
Four reasons clients pick us for the BI programme.
60+ shipped projects
Pattern recognition matters. We have built BI for retail, healthcare, manufacturing, and professional services. The right architecture for the right shape.
Decisions before dashboards
We start with the decisions you need, not the metrics that exist. The dashboard is the last thing we build, not the first.
Adoption is measured
Dashboard usage tracked from launch. Low-adoption dashboards get a redesign or get retired, not left to wither. We measure what we ship.
Hyderabad-based team
BI engineers, data architects, and analysts based in Hyderabad. Same time zone, same business context, on-site for workshops when it matters.
BI profiles by sector.
Retail & e-commerce
Sales by store, basket analysis, inventory turn, conversion funnels. POS integration, e-commerce platform integration, daily refresh on tier-1 metrics.
Healthcare
Appointment volumes, payer mix, clinical outcomes, operational efficiency. PHI-aware design, regulatory reporting integration, role-based access.
Professional services
Utilization, realization, margin by client, pipeline health. Time-and-billing integration, partner-level reporting, project profitability.
Financial services
Portfolio analytics, risk aggregation, regulatory reporting, customer profitability. ERP and CRM integration, audit-trailed calculations.
Manufacturing & logistics
Production yield, OEE, inventory accuracy, on-time delivery. ERP and WMS integration, IoT/OT data ingestion, real-time dashboards for production floors.
Property & real estate
Occupancy, rent collection, maintenance metrics, portfolio performance. Property management system integration, leasing pipeline, tenant analytics.
Why most DIY BI projects do not stick.
| Feature | DIY dashboards Excel + ad-hoc | Engineered BI Warehouse + governance |
|---|---|---|
Single source of truth | ||
Refresh discipline Who runs the report when the analyst is on leave? | Manual, fragile | Automated, monitored |
Calculation consistency | Disputed across teams | Defined once, used everywhere |
Security & access control | File permissions | Row-level security |
Mobile and embedded | Limited | Native |
Cost over 3 years Including analyst time spent maintaining ad-hoc reports. | Higher (analyst overhead) | Lower (engineered infrastructure) |
Adoption signal | Unmeasured | Tracked, acted on |
From discovery to dashboard adoption.
- 1
Discover
2-3 weeks
Workshops with stakeholders, decision register, data source audit, calculation dictionary. Output: written design, dashboard catalog, SOW.
- 2
Build data
3-6 weeks
Data warehouse provisioned, ETL pipelines built, source systems integrated, master data conformed. First refresh validated against source.
- 3
Build dashboards
2-4 weeks
Dashboards built around the decisions identified in discovery. UAT with stakeholders, iteration on visuals and filters, accessibility checks.
- 4
Adopt
4-12 weeks
Rollout per team, recorded training, office hours during week 1-2, usage analytics from week 3 onwards. Underused dashboards reviewed and reworked.
“We had 14 different versions of "monthly revenue" across our finance and sales teams. GR IT spent the first three weeks just defining what each calculation actually meant, then built the warehouse and the dashboards on top. Six months in, leadership opens the same dashboard at every Monday meeting and finally argues about the business, not about the numbers.”
Data analytics & BI, frequently asked.
Resources for analytics leads.
Microsoft 365
The platform Power BI lives in: identity, licensing, security baseline, governance. Often paired with BI for clients standardising on the M365 stack.
Learn moreServer management
When BI runs on on-prem databases or virtualization platforms: the operational management of the underlying servers and storage.
Learn moreGet a BI proposal
Tell us your data sources, the decisions you need to inform, and your platform preferences. We send a written design and SOW within 5 business days.
Learn moreTalk to a BI specialist.
Three-minute form. Our team gets back the same business day to schedule a discovery workshop. We will tell you whether your data is ready for BI before you commit to a build.