Reports scattered across systems
Sales, finance and operations figures sit in different applications and spreadsheets, so one question needs several exports.
How BI helps: Integrate sources into one model.
We connect the data in your business systems, define your KPIs once and build Power BI reporting your managers can rely on. Our team works across the whole chain: data engineering, Microsoft Fabric, semantic models, Power BI and SharePoint.
Power BI SQL & Data Engineering Microsoft Fabric SharePoint
RevenueTarget
Illustrative dashboard with sample data, not client figures.
These are the reporting problems we are most often asked to solve. Most organisations have some of them, rarely all.
Sales, finance and operations figures sit in different applications and spreadsheets, so one question needs several exports.
How BI helps: Integrate sources into one model.
Analysts spend hours copying, cleaning and formatting data before anyone can read the numbers.
How BI helps: Automate refreshes and repeatable steps.
Two teams report different revenue or margin for the same month because each calculates it its own way.
How BI helps: Define each KPI once in a shared semantic model.
Totals show that a number moved, but not which region, product or customer moved it.
How BI helps: Drill-through reports from summary to detail.
Different formats, codes and update cycles make it difficult to join data from multiple sources.
How BI helps: Data engineering and mapping tables.
Missing records, duplicates and unclear sources make managers hesitant to act on a report.
How BI helps: Data quality checks and documented logic.
Five capability areas that work together, from raw data to the report on a manager's screen. Most projects use two or three of them.
Reports and dashboards that people use to make decisions, built on a clean data model. This is the core of our PL-300-certified Power BI work.
The data foundation behind reliable reporting: getting data out of source systems, cleaning it and shaping it for analysis.
Analytics and data engineering on Microsoft Fabric, backed by DP-600 (Fabric Analytics Engineer) and DP-700 (Fabric Data Engineer) certifications in our team.
Reporting that fits how your teams already work in Microsoft 365. SharePoint is a collaboration and document layer here, not a replacement for a data platform.
Improving the BI you already have: fewer conflicting reports, clearer definitions and models that are easier to maintain.
Four sample dashboards built on one consistent set of synthetic figures for a fictional distributor. They show the kind of reporting we build; they are not client data.
One page for leadership: revenue against target, margin and order volume, with the regional split one click away.
RevenueTarget
Target achievement by region and the product mix behind it, so sales leaders can see where the gap to target sits.
ActualTarget
Gross margin trend and operating costs by category, built on the same revenue figures as the executive view.
Order volume, delivery performance and backlog, with on-time delivery compared across warehouses against a 95% target.
At or above targetBelow target95% target
Most reporting problems start before the dashboard. This is the typical flow we design; each project uses only the layers it needs.
SQL databases, business applications, Excel files, SharePoint lists and libraries, and other systems with an export or API.
Integration, ETL/ELT, transformation and data quality checks, scheduled so the data stays current.
Structured, analysis-ready tables, in a SQL database, a Fabric lakehouse or warehouse, or SharePoint for smaller file-based processes.
Business logic, relationships, shared measures and agreed KPI definitions, maintained in one place.
Power BI dashboards and reports, embedded in SharePoint or Teams where useful, with permissions applied.
Scheduled refreshes, failure notifications and checks on row counts and totals.
KPIs defined once, documented and reused across reports.
Workspace roles, row-level security and Microsoft 365 permissions.
Clear naming, documented logic and models your team can extend.
Typical scenarios, the question behind each one and the kind of solution that answers it.
Our team holds three role-based Microsoft certifications that cover preparing data, engineering it and turning it into reports. These are team certifications: they are held by members of our team, not necessarily by every team member.
Preparing, modelling, visualising and analysing data in Power BI, using Power Query and DAX, and managing and securing Power BI content.
Where it shows in our work: Power BI dashboards, semantic models, DAX measures and report security.
View the certification on Microsoft LearnDesigning, creating and managing analytical assets in Microsoft Fabric, such as semantic models, warehouses and lakehouses, and querying them with SQL, KQL and DAX.
Where it shows in our work: Fabric analytics solutions and governed semantic models.
View the certification on Microsoft LearnIngesting and transforming data, and securing, monitoring and optimising analytics solutions in Microsoft Fabric, using SQL, PySpark and KQL.
Where it shows in our work: Data pipelines, data loading patterns and orchestration in Fabric.
View the certification on Microsoft LearnTogether they cover an end-to-end engagement: DP-700 for getting data in and transformed, DP-600 for analytical models and Fabric assets, and PL-300 for the Power BI reports people use. Certification shows tested knowledge; it does not by itself guarantee a project result.
Five stages, scaled to the size of the project. Scope, timelines and deliverables depend on your environment and requirements, and we agree them after the assessment.
Understand your objectives, stakeholders, decisions and KPIs, and the reporting problems you want to solve.
Review data sources, data quality, current architecture, access and integration requirements.
Build the data foundation, the semantic model and the reports, reviewing them with your team as we go.
Test calculations against known figures, check permissions, usability and performance, then publish.
Monitor use, gather feedback and improve the solution over time, with ongoing support if you need it.
Two recent projects, written up in detail with the architecture, the data model and the automation behind them. Client names are withheld for confidentiality.
Challenge: customer forecasts arrived in different layouts and part numbers, and planners compared versions by hand.
Approach: Python standardisation into SharePoint, a Power BI model that compares forecast versions over 30, 90 and 120 days, and Power Automate alert emails.
Reported outcome: a forecast cycle went from almost five days to less than one day, as reported by the project team.
Power BIPower AutomateSharePointPythonDAX
Read the case studyChallenge: sensor data was downloaded and processed by hand, and every vendor needed a separate daily report.
Approach: Power Automate collects the data into SharePoint, Power BI calculates hours per shift, and a paginated report emails each vendor only its own machines.
Outcome: the daily reporting cycle runs with no manual steps.
Power BIReport BuilderPower AutomateSharePoint
Read the case studyBusiness intelligence (BI) brings data from your business systems together, applies agreed definitions and presents it in reports and dashboards. It helps managers see performance, compare it with plans and find the cause of changes without assembling the figures by hand.
We design and build Power BI dashboards and reports, data models and DAX measures, semantic models, row-level security, and report performance improvements. We also review existing Power BI solutions and provide ongoing support.
Yes. Power BI can use SQL databases and SharePoint lists or files as data sources, and Power BI reports can be embedded in SharePoint and Teams. The right setup depends on your data volumes, refresh needs and security requirements.
Yes. Our team includes DP-600 (Fabric Analytics Engineer Associate) and DP-700 (Fabric Data Engineer Associate) certified members, and we work on Fabric data pipelines, lakehouses, warehouses and semantic models. Fabric is not necessary for every project, and we will tell you if a simpler setup fits better.
In many cases, yes. We automate data collection, refreshes and repetitive transformation steps, and can use Power Automate to deliver reports or alerts. How much can be automated depends on whether your source systems provide exports, APIs or database access.
Yes. We review existing reports and models, consolidate duplicated logic into shared semantic models, standardise KPI definitions and improve performance, keeping what already works.
We follow the principle of least privilege: workspace roles, row-level security where needed, and access managed through your Microsoft 365 accounts. We document data sources and KPI logic so that ownership is clear.
A typical project moves through discovery, assessment, design and build, validation and deployment, and ongoing improvement. Scope, timelines and deliverables depend on your data sources, the number of reports and your internal processes, so we agree them after the assessment.
Yes. We can support and maintain your reports, models and data pipelines after go-live, including changes, new requirements and troubleshooting.
Whether you are building a new Power BI solution, modernising existing reports or improving your data engineering foundation, let's discuss your requirements and identify the right next steps.
Microsoft-certified team: PL-300 · DP-600 · DP-700. Serving businesses since 2020 from our India-based team.