Data governance
Who is responsible for what, under which rules, from one team to the whole group
- Data office and governance committees
- Data owners and data stewards
- Catalog and business glossary
- Policies, master data and compliance
The Data Consulting Company That Creates Value
AI is only as reliable as your data
Insightovalue supports every kind of company, from SMEs to international groups, that wants strong data governance, powerful quality rules across every business domain and data that is ready before any AI project
We also industrialise AI-driven process automation, so your teams gain productivity and refocus on their core business
More than 10 years of experience, forged in industry where demanding processes taught us a great deal, and brought to large groups in luxury, retail and many other sectors
Close to your business teams
Data governance is only adopted when it speaks your teams’ language: that is where we start, from the shop floor to the executive committee
10+years
supporting large groups and multinationals
Supply chain
Forecasts, inventory and flows reconciled from one site, country and ERP to the next
Manufacturing
Production, OEE and quality data aligned with the ERP and the MES
Finance
One version of the figures, from management control to group reporting
Procurement
Clean supplier and item master data, so spend can finally be analyzed
Sectors and contexts
Industry
Production sites, logistics chains and technical data to make reliable
Luxury
High traceability standards, with high-value product and customer data
Multinational groups
Several countries, entities and ERPs brought under one shared governance
Three stages from data to value
Each stage builds on the one before, and AI comes last, once the data is ready
Build
A reliable data foundation: well built, well governed, well controlled
Illuminate
Dashboards that everyone understands and trusts
Unlock value
AI use cases and industrialised automations chosen for their value: your teams refocus on their core business
The foundation first, everything else after
Three offers build the foundation, our migrations modernize it, and two more draw value from it once the data is ready
BuildThe base of every data project, from one team to the whole group
Data quality
Accurate data, measured and monitored
- Quality rules
- Measurement and monitoring
- Remediation of discrepancies
- Trust indicators
Data infrastructure
A modern architecture, connected to your sources
- Databricks, Fabric, Snowflake
- SAP and SharePoint integration
- SQL modeling
BuildIlluminate
Data platform and BI tool migrations
We support your data platform and BI tool migrations, and the change management that comes with them: inventory of what exists, conversion of models and reports, figures reconciled before cutover, and users trained and supported so everyone migrates smoothly
- On-premise data platforms to the cloud (Databricks, Fabric, Snowflake…)
- BI tools, whatever the vendor you leave or adopt
- Report rationalization before migrating
- Parallel run and reconciliation of the figures
- Change management, training and user support
Once the data is ready
BI reporting
IlluminateClear dashboards in your BI tool, on data you already trust
Learn more about BI reportingAI use cases and automation
Unlock valueValue and feasibility workshops, a roadmap, then industrialised process automation
Learn more about AI and automationWhat sets us apart
The first case studies are coming soon: until then, here is how we work
Governance before AI
We secure the data before plugging models into it, so your AI projects hold up in production
The language of your business
Supply chain, production, finance: we start from your processes and your indicators to design the data solution
Multi-platform expertise
Data platforms and BI tools, from cloud to on-premise: we recommend the solutions that fit your context and know how to migrate from one to another
Senior support, all the way to autonomy
An experienced counterpart from scoping to production, and teams trained to keep control afterwards
Is your data ready for AI?
Have your data infrastructure diagnosed
We assess your infrastructure, governance, data quality and AI use cases, then build a concrete, effective action plan with you
Book a call: a first conversation is enough to scope the diagnostic
Sample result
Illustrative valuesOverall score52/100
Fragile
Infrastructure76/100
Reliable
Governance58/100
Fragile
Quality34/100
Insufficient
AI use cases41/100
Fragile
Read this before starting an AI project
Checklist
Ready for AI? 10 points to check
Ten checks to run on your data foundation before starting an AI project
- Your data sources are inventoried
- Every critical dataset has a data owner
- Your key metrics have a shared definition
- Quality rules are measured on your critical data
6 more points in the full checklist
Article
Data owner, data steward: who does what?
Two roles that are often confused, and how to assign them from a single team to the whole group
Article
Migrating BI tools without losing your users’ trust
Inventory, conversion, parallel run and change management: the steps that prevent conflicting figures and bring every user along
Frequently asked questions
What we do, and the terms you will hear during a data project
Why start with data rather than AI?
An AI model reproduces the flaws of the data it is fed, so without governance and quality an AI project cannot be industrialized
Do you work with large groups and multinationals?
Yes, Insightovalue works in multi-country, multi-entity and multi-ERP contexts, alongside your data, IT and business teams
Where do you work?
Mainly in Paris and Cannes, as well as in major French cities such as Lyon, Marseille or Aix-en-Provence, and across Europe or internationally when needed: hybrid or fully remote
Can you migrate our reports from one BI tool to another?
Yes, we inventory what exists, convert models and reports, reconcile the figures of both tools before cutover and support your users through the change
What is a data catalog?
The inventory of your data: what it means, where it comes from, who is responsible for it
What is master data?
The data shared across the whole company: customers, products, suppliers
What is the difference between a data owner and a data steward?
The data owner is accountable for the quality and use of a dataset, while the data steward applies the rules day to day and fixes discrepancies
What does data quality cover?
Data that is accurate, complete, up to date and consistent from one team to the next
Trusted data before AI
Describe your context and Insightovalue will get back to you as soon as possible
- contact@insightovalue.com
- Service area
- Mainly Paris and Cannes, as well as major French cities (Lyon, Marseille, Aix-en-Provence…), and across Europe or internationally when needed, hybrid or fully remote
Would you rather start by measuring your data maturity?
Take the data maturity diagnosticWrite to Insightovalue
All fields are required unless marked optional