Business Intelligence
Turn your data from a liability into a decision-making advantage.
Most businesses are swimming in data but starving for insight. We build BI systems — from data warehouse architecture and ETL pipelines to executive dashboards and KPI frameworks — that give decision-makers reliable, real-time visibility into the metrics that actually drive their business outcomes.
What you get
What's included in our
Business Intelligence engagement
Data Warehouse and ETL Pipeline Architecture
A properly modelled data warehouse that consolidates data from your operational systems — CRM, ERP, e-commerce, marketing platforms, financial systems — into a single source of truth with consistent definitions, reliable refresh cycles, and documented data lineage.
Executive and Operational Dashboards
Role-specific dashboards that show each audience exactly what they need: executive-level KPI scorecards for the board, operational dashboards for managers, and self-service reporting for analysts. Built in Metabase, Tableau, Looker, or a custom React front end depending on your requirements.
KPI Framework and Metric Definitions
Before a dashboard is built, we facilitate a KPI definition process that aligns on which metrics matter, how they're calculated, who owns them, and what good/bad performance looks like. This eliminates the "why does my report say X but the finance team says Y" problem that plagues most BI implementations.
Our process
How we deliver Business Intelligence
Data Audit and Requirements Gathering
We audit all of your data sources, document their schema, data quality, and refresh characteristics, and gather reporting requirements from each stakeholder group. We identify gaps between what data exists and what decisions need to be informed — and propose a plan to close them.
Data Warehouse Design and Modelling
We design the data warehouse schema using dimensional modelling (star or snowflake schema) — building fact tables, dimension tables, and aggregate layers that enable fast, flexible querying. Data transformations are implemented in dbt with version control and automated testing.
Pipeline Development and Dashboard Creation
We build ETL/ELT pipelines connecting each source system to the warehouse, schedule automated refresh, and build the dashboard layer. Dashboards are developed iteratively with stakeholder review at each stage — not delivered as a final product at the end.
Training, Documentation, and Handover
We train your team on self-service reporting, document the data model and transformation logic, and produce a data dictionary that your business can maintain as new data sources are added. BI value compounds when users can explore independently — we invest in making that possible.
Stack
Technologies we use
Why Palsoro for Business Intelligence
We Define the Metrics Before We Build the Dashboards
Most BI projects fail because they build dashboards before agreeing on what should be in them. We run a structured KPI alignment workshop with your leadership team before any technical build begins — producing metric definitions that everyone agrees on and trusts.
Data Quality as a First-Class Concern
We build data quality tests into every pipeline — checking for null values, duplicate records, value range violations, and referential integrity — and create a data quality dashboard your team can monitor. Dashboards built on unreliable data are worse than no dashboards.
Self-Service Focus — Not Dependency Creation
We build BI infrastructure that your team can operate and extend independently. Documented models, version-controlled dbt transformations, and trained power users mean you're not locked into a perpetual consulting dependency to add a new chart.
We work with Metabase, Tableau, Looker, Power BI, and Superset, and build custom BI applications when off-the-shelf tools don't meet the requirements. We recommend the right tool based on your team's technical capability, budget, and the type of analysis your decision-makers need to perform.
This is the central challenge in most BI projects. We facilitate a metric definition workshop that resolves conflicting definitions at the business level before touching the data, then implement the agreed definition consistently in the warehouse transformation layer. Every metric has a single authoritative calculation.
Yes. We build streaming data pipelines using Kafka, Kinesis, or real-time database change data capture (CDC) for use cases that require near-real-time data freshness. Most reporting use cases are adequately served by hourly or daily batch refresh, but where operational decisions require live data, we design for it.
A focused first-sprint BI implementation — connecting your top 3 data sources, building core warehouse models, and delivering your most critical executive dashboard — typically takes 6–8 weeks. A full enterprise BI platform covering all data sources and stakeholder groups is typically a 4–6 month programme delivered in phases.