infotechData integration
Bring information from relevant sources into a planned data workflow. Define refresh needs, transformations and how failed or incomplete loads are handled.
Microsoft Fabric
One data foundation. A clearer business picture.
Connect your analytics landscape and turn enterprise data into useful insight.
Built forData leaders, engineers, analysts and business stakeholders
The focusFrom source information to shared analytical insight
Our delivery approach The business perspective
Technology witha clear purpose.
Siloed data slows reporting and limits visibility. A unified approach helps teams move from repeated preparation toward meaningful analysis.
A data platform should make information easier to reuse, understand and act on. Fabric brings data workloads into a connected platform, but the business value depends on the sources, definitions and decisions you prioritise. We help shape a foundation around a useful analytical outcome, then design the ingestion, transformation and reporting needed to support it. That creates a practical path from a first use case to a broader data capability.
Explore the possibilities
Built for the workthat matters.
A closer look at the capabilities and decisions that shape a useful solution.
OneLake foundation
Use a shared logical data lake within the Fabric architecture. Organise data for reuse with clear ownership and access boundaries.
Engineering and warehousing
Select the lakehouse, warehouse and engineering patterns appropriate to the workload. Align the design with the skills and needs of the delivery team.
Real-time intelligence
Assess event and streaming scenarios where timely operational information changes a decision. Distinguish those needs from reporting that can refresh on a schedule.
Power BI analytics
Connect prepared data to reusable semantic models and reports. Keep business definitions visible from the platform through to the information people consume.
Data science and AI
Prepare suitable data for analytical and AI use cases. Review quality, access and evaluation needs alongside the capabilities available in the platform.
From capability to everyday value
See itin practice.
Explore possible starting points for your organisation. Each scenario connects a business need with work to consider and measures to review.
Enterprise reporting · Illustrative scenario
Build one dependable reporting foundation.
Departments prepare similar extracts and reconcile different results. Choose a priority reporting area, agree the measures and connect the relevant sources. Establish a repeatable data workflow that analysts can reuse instead of rebuilding preparation for every dashboard.
What to measureData reuse and reporting preparationAgree a baseline and targets with your business owners.
Operational insight · Illustrative scenario
Match data freshness to the decision.
A team needs to respond to an operational event sooner than the next daily report. Define the response window and the action the information should trigger. Compare streaming and scheduled approaches against that requirement before designing the platform.
What to measureInformation latency and response completionAgree a baseline and targets with your business owners.
AI readiness · Illustrative scenario
Prepare the information behind a useful use case.
An AI initiative depends on records whose quality and ownership are unclear. Identify the required sources and definitions, resolve critical gaps and establish an access model. Treat the first AI use case as a test of the data foundation as well as the model.
What to measureData-quality exceptions and evaluation readinessAgree a baseline and targets with your business owners.
The Defacto approach
Clarity at every step.Ownership beyond launch.
We assess your sources, reporting priorities and governance needs, then design the data platform and deliver valuable reporting use cases in stages.
Explore our approach- 01
Define the first analytical outcome
Agree the decision, users and source systems. Review the existing data estate and select a scope that can demonstrate a complete information journey.
- 02
Build the connected foundation
Design ingestion, transformation, storage and semantic models. Validate quality, access and performance with the teams who create and consume the information.
- 03
Plan sustainable operation
Review capacity, monitoring and ownership. Establish standards for adding sources and workloads so the platform can grow without losing consistency.
A useful first conversation
Bring the context.
We’ll help shape the next step.
A few examples of your current work will make the discussion more specific.
- Priority analytical questions and data sources
- Refresh, volume and performance expectations
- Data definitions, quality and ownership
- Capacity, access and operating responsibilities
Experience you can explore
See the workbehind the words.
Customer experienceLudhiana Beverages Pvt. Ltd.
Turning enterprise data into operational insight.
LBPL modernised its analytics landscape by eliminating data silos, unifying enterprise data, and enabling real-time decision-making across production and distribution.
Explore the customer storyMake an informed decision
Your questions.Considered answers.
The right scope starts with a clear understanding of your business, the platform and the work ahead.
Does Fabric replace Power BI?
Power BI is part of the Fabric experience for business intelligence. Fabric also provides broader data capabilities. We assess whether your need is primarily reporting or whether ingestion, engineering, shared storage and other workloads should be included.
Do we need to migrate every dataset into Fabric?
No. Start with the information needed for a defined outcome and assess supported access patterns. The architecture should account for existing investments, source ownership and practical integration requirements rather than assuming a complete migration from the outset.
How do we plan capacity and cost?
Identify the workloads, expected data volumes, refresh patterns and user demand. Validate assumptions with a representative workload and review utilisation during operation. Capacity planning should evolve with observed use rather than rely on a fixed estimate made before the design is understood.
Explore Microsoft guidance
Your next chapter starts here
What’s your next?
A challenge to solve. An idea to explore. Let’s talk.