Custom Business Intelligence Solutions
Data Warehousing, BI Dashboards, and Analytical Reporting That Drive Smarter, Faster Business Decisions
We provide custom Business Intelligence Solutions that help organisations collect, consolidate, transform, and visualise data from across their business into clear, actionable intelligence. Our BI solutions build the data infrastructure — data warehouses, ETL pipelines, semantic layers, and interactive dashboards — that gives decision-makers at every level a comprehensive, accurate view of business performance.
Is your business data scattered across multiple systems with no single source of truth? Are your management reports time-consuming to produce, inconsistent between teams, or too historical to support timely decisions? Techmits IT Solutions builds BI solutions that consolidate your data, automate reporting, and deliver the business intelligence your team needs — in real time, on demand, and in the format that is most useful for each audience.
We deliver business intelligence solutions for organisations across India, the UK, Australia, the USA, Canada, UAE, and the Middle East — building BI platforms for retail, manufacturing, financial services, SaaS, logistics, and professional services organisations. Every BI solution we build is designed around the specific reporting needs and data landscape of the business it serves.
Why Choose Techmits for Business Intelligence?
Business intelligence is only valuable when it is trusted, timely, and acted upon. At Techmits IT Solutions, we build BI solutions with data quality, performance, and usability as the primary design criteria — creating BI platforms that business users actually rely on for decisions, not just technical demonstrations of what is possible.
Data Warehouse Design
We design and build data warehouses that consolidate data from all your source systems into a clean, well-structured analytical layer — the single source of truth that makes consistent reporting possible.
ETL Pipeline Development
We build ETL and ELT data pipelines that extract data from source systems, transform it to the required structure and quality, and load it into your data warehouse on automated schedules.
BI Dashboard Development
We develop interactive BI dashboards using leading tools — Power BI, Tableau, Looker, or custom-built — delivering the visual analytics that make data insights accessible to business users.
Self-Service Analytics
We build self-service analytics capabilities that allow business users to explore data, create their own reports, and answer ad-hoc questions without relying on the data team for every query.
Data Quality Management
We implement data quality frameworks — validation rules, reconciliation processes, and data quality monitoring — ensuring business users can trust the data driving their decisions.
BI Governance
We implement BI governance — metric definitions, data dictionaries, access controls, and change management — ensuring consistent, authoritative business intelligence across the organisation.
How We Build BI Solutions
Our Business Intelligence Development Process
Requirements & Data Discovery
We understand the business decisions your BI solution needs to support, map data sources, assess data quality, and define the metrics and reports that matter most.
BI Architecture Design
We design the BI architecture — data warehouse structure, ETL approach, semantic layer design, dashboard hierarchy, and governance model.
Data Warehouse Build
We build the data warehouse — dimensional model, tables, indexes, and the structural foundation for analytical queries.
ETL Pipeline Development
We build automated data pipelines that extract from source systems, transform to warehouse structure, and load on defined schedules with monitoring and error alerting.
Dashboard Development
We develop BI dashboards — executive summaries, operational views, and analytical deep-dives — with the visualisation quality and interactivity your users need.
Data Validation
We validate BI data against source systems — reconciling figures to confirm accuracy and building user trust in the BI platform.
User Rollout
We roll out the BI platform with user training, adoption support, and the documentation that helps business users get maximum value from day one.
Ongoing Development
We continue developing the BI platform — adding reports, refining metrics, improving performance, and evolving the solution as business needs change.
Everything You Need to Know About Business Intelligence Solutions
Get answers to questions about BI architecture, data warehousing, ETL pipelines, BI tools, self-service analytics, data quality, and how to build a BI solution that business users will trust and use.
What is the difference between a data warehouse and a database?
An operational database (transactional database) is optimised for recording and retrieving individual records quickly — processing customer orders, updating inventory, recording transactions. A data warehouse is optimised for analytical queries across large volumes of historical data — calculating aggregates, comparing periods, and joining data from multiple source systems. Data warehouses use dimensional modelling (star schema, snowflake schema) that makes analytical queries fast and intuitive for BI tools to query.
Which BI tools do you work with?
We work with all major BI platforms: Microsoft Power BI (excellent for Microsoft-ecosystem organisations, strong self-service capability), Tableau (powerful visualisation, large user community), Looker (code-based semantic layer, excellent for data teams), Google Looker Studio (free, good for smaller deployments), and Apache Superset (open-source, cost-effective for technical teams). We also build custom BI dashboards using React and charting libraries for organisations that need BI embedded in custom applications. We recommend the right tool for your team's technical capability and budget.
How long does it take to build a business intelligence solution?
A focused BI solution — consolidating 2–4 data sources into a data warehouse with a suite of standard dashboards — typically takes 2–4 months. A comprehensive enterprise BI platform covering many source systems, complex data transformations, a full semantic layer, and extensive dashboard development takes longer. We deliver in phases — providing initial value through the most important reports quickly while building out broader coverage over subsequent months.
How do you ensure the data in our BI system is accurate?
Data accuracy is the most critical requirement for BI adoption — users who encounter incorrect figures stop trusting and using the system. We implement multi-layer data quality: source-level validation during ETL (rejecting or flagging records that fail quality rules), reconciliation reports comparing BI figures to source system totals, data quality monitoring dashboards, and a formal process for investigating and correcting data discrepancies. We treat reconciliation testing as a primary delivery milestone before business user rollout.
Can you integrate our BI solution with all our current systems?
Yes. We build ETL pipelines that extract data from any source that provides access — databases (MySQL, PostgreSQL, SQL Server, Oracle), cloud applications via API (Salesforce, HubSpot, Shopify), files (CSV, Excel), and data streams. We handle the complexity of varied source formats, incremental extraction, change detection, and the transformation logic needed to make diverse source data consistent in the warehouse. We assess your specific source systems during the discovery phase and design appropriate integration approaches.
What is self-service analytics and how do we enable it?
Self-service analytics enables business users to create their own reports and explore data without relying on the data team or developers. It requires: a clean, well-documented semantic layer that presents business-friendly metric names and relationships (hiding complex SQL); a BI tool with an intuitive drag-and-drop report builder; appropriate data access controls (users see only data they are authorised for); and user training. We design and implement the semantic layer and governance that makes self-service analytics practical — not just technically possible but genuinely usable by non-technical business users.
How do you handle slowly changing data and historical analysis?
BI solutions must preserve historical snapshots of data that changes over time — customer status, product pricing, employee department. We implement slowly changing dimension (SCD) techniques in the data warehouse to maintain full historical context, enabling analysis questions like "what was this customer's segment when they placed this order?" or "compare sales performance by region using the organisation structure that was in place at the time." This historical accuracy is essential for meaningful time-series analysis.