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Data

Data & Analytics

Governance, architecture, pipelines, data products: we structure your data so it informs your business decisions and feeds your AI use cases. Not just your reporting.

Duration

3–6 months

Team

2–3 consultants

Indicative budget

€80–200K

The Nobori Method applied to data (start from decisions, structure, industrialize, transfer) helps our clients make better decisions faster. Data is no longer just an IT issue. It is a business asset their teams have control over.

€2.5M

savings delivered (BNP Paribas CIB)

8–12 wks

from audit to first data products in production

600 tx/s

real-time analysis cross-referenced against 6 TB of history (Intermarché)

THE PROBLEM

Your data exists. Its value does not, yet

Data is everywhere in your organization. But between silos, uncertain quality, makeshift pipelines and dashboards nobody looks at, it does not serve the people who need it. The executive committee wants data-driven decisions and AI everywhere, but without reliable, governed data, no model holds up. It is not a volume problem. It is a structure problem.

Our approach

Step 1

Start from decisions, not data

Which decisions do your teams make on gut feeling for lack of reliable data? That is where we start, not with an exhaustive inventory of your sources.

Step 2

Build a roadmap, not a target blueprint

Governance, quality, architecture: we build step by step, based on your actual maturity. Every sprint delivers value, not just plumbing.

Step 3

Industrialize so it holds

Tested, orchestrated, monitored pipelines. We deliver data flows that run in production, not prototypes that break at the first load spike.

Step 4

Put your teams in control of their data

We do not build dependency. We deliver useful data products, train your teams, and leave once they are autonomous.

How we fit into your organization →

Technologies & tools

DatabricksDatabricks
SnowflakeSnowflake
DBT LabsDBT Labs
Apache KafkaApache Kafka
Apache SparkApache Spark
AirflowAirflow
Power BIPower BI
ElasticsearchElasticsearch
PostgreSQLPostgreSQL
BigQueryBigQuery
RedshiftRedshift
TableauTableau

Team certifications

Databricks Certified Data Engineer AssociateSnowflake SnowPro Core CertificationGoogle Cloud Professional Data EngineerAWS Data Analytics Specialty

Case studies

Concrete results, not promises

Retail·Multi-year

Intermarché

Data Factory: from architecture to ML in production

15+

data initiatives delivered

3

domains: ML/AI, BI, streaming

Before data can drive every aisle, warehouse and recommendation, someone has to build the foundations

Read the story
Finance·Multi-year

BNPPARIBAS

Asset repository and IT services marketplace

€2.5M

saved

15

reference data repositories synchronized

When the success of a marketplace creates more chaos than the failure it was meant to prevent

Read the story
Finance·4 months

Blank (Crédit Agricole)

Self-service analytics for a neobank serving freelancers

1 FTE

saved on back-office processing

Real-time

extraction of operational data

When your database handles the load but blocks your decisions

Read the story

Complementary expertise

Combine our areas of expertise for greater impact

AI & GenAI Consulting

Your structured data becomes the fuel for your AI and GenAI use cases.

Discover

IT Architecture & Modernization

Integrate the data platform into your overall IT roadmap.

Discover

Our commitments on this offering

Business impact every sprint

Every sprint produces a result the business can see, not just one more pipeline in the technical backlog.

Non-negotiable quality

Automated tests, monitoring and alerting from the very first data flow. No data in production without a safety net.

AI data readiness

We structure your data so your future AI use cases can use it, not just for today's reporting.

What our clients say

We grew from 12 to 95 people in two years. In that hypergrowth, we had to structure our data as fast as the organization, or every decision would remain a gut call. Nobori helped us refine our processes, define our KPIs and deploy, end to end, the data platform that took us from ad hoc spreadsheets to data-driven management. Today, every team makes decisions based on shared, reliable figures, and our data is ready to feed the company's AI use cases.
A

Anonymous

Chief Data Officer

Producer responsibility organization (PRO)

Frequently asked questions

Where do you start when you do not know how to tackle data?
With the business decisions that lack reliable data, not with an exhaustive inventory of your sources. Our 2-to-4-week scoping identifies 3 to 5 high-value use cases, the actual availability of the data, and the most cost-effective roadmap. You walk away with a prioritized, costed plan, not an architecture diagram that sits on a shelf.
How long before we see concrete value?
Our clients get a first data product usable in production in 8 to 12 weeks. We deliver in sprints of 2 to 3 weeks, each ending with a result the business can see, not just technical plumbing. The goal: demonstrate value before industrializing.
How do you work with our internal teams?
We build with your data engineers and data analysts, not in their place. Knowledge transfer starts in the first sprint: pairing, code reviews, living documentation. By the end of the engagement, your teams run the platform on their own. That is a success criterion, not an option.
How do you prioritize data use cases when everyone wants one?
We use an impact/effort matrix for each use case, combining estimated business value, technical complexity and data availability. The first sprints target high-impact cases on data that is already available, to demonstrate the value of the platform quickly and unlock budget decisions.
Are you tied to a data software vendor (Databricks, Snowflake, etc.)?
No. Our consultants are certified on the main platforms, but we are independent: no resale, no commissions. We recommend the architecture that fits your context, your volumes and your in-house skills. We systematically favor open formats (Delta Lake, Iceberg, Parquet) to avoid any lock-in.
How much does it cost to build a data platform for a mid-market company?
The typical budget is €80–200K for an operational data platform, over 3 to 6 months with 2 to 3 consultants. That covers the architecture, the first pipelines in production, governance and knowledge transfer. We always size the work to the expected business value, not to the technical complexity.
What is the difference between Nobori and an IT consulting firm or a large advisory firm on data?
We do not do staff augmentation. Every engagement is led end to end by a senior lead with 15+ years of hands-on experience: no team rotation, no juniors supervised remotely. We commit to deliverables and to your teams' autonomy at the end of the engagement, not to a volume of billed days.

Let's talk about your data project

Our team works with you from vision to delivery

Book a discovery call

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