Connect the sources
Link ERP, CRM, applications and other sources, defining collection and refresh rules.
Our service
BI & Data: Reliable Data for Your Business Decisions
A BI & data project gives your teams metrics they can rely on. We build the data flows, data models and dashboards that let you run the business, and we prepare clean, documented data for your AI use cases.
For
Leadership, finance, operations and product teams whose decisions rely on disputed figures
What you get
Defined metrics, controlled pipelines, documented dashboards and data ready for AI
Technologies
Power BI, Looker, Tableau, Metabase, BigQuery, Snowflake, dbt, Airflow, on AWS, Azure or GCP
Getting started
A first dashboard limited to priority metrics, validated against known cases
What we build
A scope tied to your business.
A dashboard is only useful if its metrics rest on data that is understood and kept up to date. When the ERP, CRM and spreadsheets produce different numbers, we start with the business definitions and the sources. The goal is to build a data pipeline whose results your teams can explain.
Make the data reliable
Model the metrics, check the quality of data flows and document shared definitions.
Present & operate
Build useful dashboards, organize access rights and monitor processing jobs.
What it is for
When should you start a BI & data project?
A BI & data project makes sense when decisions rely on disputed figures, manual exports or spreadsheets that only one person knows how to update.
Manage with reliable metrics
Leadership, finance, operations or product share the same KPIs, calculated the same way, with no debate about the source.
Centralize scattered data
ERP, CRM, website, application, marketing and support tools are consolidated into a single source of truth.
Reduce manual reporting
Data flows are industrialized; Excel reports rebuilt every month give way to automated, controlled processing.
Set up data governance
Rules, definitions, roles and owners are formalized so that data remains a managed asset.
Prepare your AI use cases
Clean, structured and accessible data is a prerequisite for a useful AI assistant or feature.
Use cases
A few BI & data use cases.
Performance management
Tracking revenue, margin, customer value or acquisition cost.
Finance and cash flow
A view of business activity, forecasts and costs to plan ahead.
Operational monitoring
Service levels, backlog, operations or support, in real time or near real time when needed.
Multi-source consolidation
Data from the ERP, CRM, web and marketing tools brought together in a single model.
Product analytics
Activation, retention and user journeys in an application or SaaS product.
From work to deliverables
How the engagement unfolds.
We identify the source of each piece of information, the required refresh frequency and the quality checks. Data flows collect and transform the data before it is presented. We document calculation rules, prepare recovery after errors and make incomplete processing visible. Users validate the metrics on known cases before the scope is extended.
Definitions and priorities
Which KPIs, which calculation rules, which reference source. Review of existing sources and their quality.
Architecture
A simple, scalable architecture, chosen according to data volume, expected latency, budget and the organization’s maturity.
Pipelines
Connection to sources, transformation, historization, documentation, monitoring and alerts.
Dashboards
Decision-oriented, readable reports, with the ability to drill down into details.
Governance and operations
Roles, access, catalog, quality, change procedure, then maintenance and addition of new sources.
What your team receives
- Metric definitions
- pipelines and controls
- dashboards and documentation
Our BI & data expertise
The BI & data technologies we work with.
Tools do not make the project, but they must fit your sources, your cloud and your teams’ skills. We work with the main building blocks on the market.
BI & visualization
Power BI, Looker, Tableau and Metabase, depending on reporting needs and the level of autonomy expected from users.
Data warehouses and platforms
BigQuery, Snowflake, Redshift and Databricks to store and analyze data at scale.
Pipelines
dbt for modeling and transformation, Airflow for orchestration, Fivetran or Stitch for ingestion from your business tools.
Governance & cloud
Data quality tests, catalog and lineage, fine-grained access management, deployment on AWS, Azure or GCP.
BI, data and AI
Data your AI features can use.
An assistant that answers questions about your figures, a search across your documents or an agent that prepares a summary is only as reliable as the data it draws on. BI & data work (shared definitions, reference sources, access rights, historization) directly prepares these use cases.
We can then build the AI feature itself, connected to your data with the same access rules. See our enterprise AI solutions and our AI agents and chatbots. For an example of a data platform we built, see the real estate data platform.
The choices that matter
The points to decide with your team.
You need to distinguish between missing data, late data and a value that is genuinely zero. History, changes in definitions and access rights are handled in the model. A first release can cover a few reliable metrics rather than many charts whose origin no one controls.
How we work together
Preparing the first conversation.
You can walk us through how things work today, the users involved and the difficulties you face. The documents, examples and access required are specified afterwards, depending on the agreed scope. The first goal is to understand the work to be done and the dependencies that may affect how it unfolds.
Frequently asked questions
BI & data: your questions.
How long does it take to deliver a first reliable dashboard?
We first aim for a dashboard limited to the priority metrics, validated by users on known cases, then we industrialize and extend the scope. The timeline depends mainly on the number of sources and their quality.
Can you take over an existing setup (Power BI, Excel, scripts)?
Yes. We audit what exists, make the calculations reliable, streamline the sources and put a sustainable architecture in place, without necessarily rebuilding everything.
How do you avoid discrepancies in figures between teams?
By setting shared KPI definitions (a glossary), one reference source per piece of information, and documented, tested transformation rules.
How do you ensure data security?
Through fine-grained, role-based access management, separate environments, the best practices of the chosen cloud and alignment with your compliance requirements.
What makes a good BI & data stack?
A stack suited to your use cases, your data volume and your maturity. It favors data reliability, readable metrics and simple operations over piling up tools.
Do you need a data warehouse to do BI?
Not always. With few sources and low volumes, a BI tool connected directly can be enough to start. A warehouse becomes useful as soon as you need to consolidate several sources, keep history and guarantee identical calculations for everyone.
Can BI data be used by an AI assistant?
Yes, as long as it is well defined, reliable and protected by access rights. We can build an assistant or AI feature that relies on this data, with evaluations and monitoring in production.
Do you provide maintenance after implementation?
Yes. Maintenance covers monitoring data flows, fixes, cost optimization and adding new sources, as a fixed-price project or with a dedicated team.
Let’s talk about your data and your metrics.
Tell us about your sources, the decisions you need to inform and the difficulties you face. Together, we will define the first scope to explore.