Data made Agile
Let’s build your data platform
We’re here to help you, design, setup and build your next data platform. 3 key points to our approach.
📌 Cost effective
📌 Business oriented
📌 Vendor agnostic
1️⃣ Co-Create & Design
We discuss your need
Find some solutions
Define a proof of concept or the mininum viable product
📅 Around 2 weeks / part time
🎯 Define the "WHY" & the "HOW"
📦 Detailed architecture & design documentation
2️⃣ Setup & Deploy
We start deploying the data platform
You start experimenting with it
Confirm that we solve an impacting pain-point
📅 Between 1 and 3 months / full time or part time
🎯 Build the platform & confirm the need
📦 A working POC/MVP of your data platform
3️⃣ Make it your own
We hire your team members
We work on improving ease of maintenance
We ensure that everything is running as expected
We work on the first new features
📅 Whatever is necessary / part time
🎯 Make the product reliable and the team ready
📦 A well sized and autonomous team
*️⃣ Other related services
Data retention policy with an attorney
Data gouvernance
Integration with other softwares
CTO for data related topics
1️⃣ Co-Create & Design
We discuss your need
Find some solutions
Define a proof of concept or the mininum viable product
📅 Around 2 weeks / part time
🎯 Define the "WHY" & the "HOW"
📦 Detailed architecture & design documentation
What's our goal ?
Discuss your pain-points, find some problems we could solve using data solutions. Define the scope of the minimum viable product and work on the timeline to build it.In the end we should have:
A detailed architecture & design documentation
Be really excited to work on the MVP
Why build a data platform ?
What's a data platform ?
A data platform is a set of software components used to gather, store, organise and use data. Two main approach are existing the one-stop shop were a single software provider is offering all the different tools or an approach were you combine yourself existing tools, see it like a cursor and you can choose where you want your company to be.
Common use-cases
| Domain | Use Case |
|---|---|
| Artificial Intelligence | There is no AI without data Building a data platform is one of the first steps to explore all the features offered by AI like natural language processing (nlp), large language models (llm) or more classical algorithms. |
| Compliance | Ensure you comply with law, industry standards… Companies in the finance sectors must ensure that all their clients are not doing money laundering or financing terrorism. A data platform can help have a global view of a client activity and also detect abnormal behavior. |
| Legal | Produce the legal documents you need using the data platform In industry sector you can track and display the lifecycle of a product through the different stages of production. |
| Support | Help your support team get more efficient by providing a single view were all the clients information are centralised for a quicker response time and more satisfied clients. |
| Analytics | Make the most of your marketing campaigns Centralise, store and keep most of the data points of all your marketing campaigns and take decision backed by data analisys. |
| Business Intelligence | Dashboards and KPI Become a data-centric or data-oriented company and take decision based on the data you have. |
| Fraud | Find fraudster and make your business more secure Using your data platform detect abnormal behaviour or strange patterns/change in the data like an increase in refunds. |
| Data brokerage | Find new ways to make the most of your business Sell the data you manage to other companies. |
A data platform can help in all the division of a company, the key to a successful data initiative is to not get lost and stay focus on the defined goal to ensure a return on investment.
2️⃣ Setup & Deploy
We start deploying the data platform
You start experimenting with it
Confirm that we solve an impacting pain-point
📅 Between 1 and 3 months / full time or part time
🎯 Build the platform & confirm the need
📦 A working POC/MVP of your data platform
Requirements
At least one well defined and business impacting use-case
A first architecture design, can be partial
Some questions that we need to answer
What's our goal ?
Confirm that the problem we’re solving is really impacting and can be solved by a data related solution.In the end we should have:
Deployed the most important components of platform
Found answers to the design choices we were ensure about
Be confident that we’re building something that matters
3️⃣ Make it your own
We hire your team members
We work on improving ease of maintenance
We ensure that everything is running as expected
We work on the first new features
📅 Whatever is necessary / part time
🎯 Make the product reliable and the team ready
📦 A well sized and autonomous team
Requirements
The MVP/POC of the data paltform was successfully deployed
What's our goal ?
Now that we know the impact of our work and that the platform is up and running, in this last step we want to transfer the ownership of the platform to your team, and if needed build it.In the end we should have:
A team confident and ready to run your platform
And a partner to help your in the long run, us
What are the titles in a data team ?
Usually a data team is composed of some:
Data Engineers in charge of managing the ETL pipeline, ensuring that the automated tasks are running successfully, fixing eventual bugs and optimisation.
Data Ops (Data Dev Ops): usually in charge of the setup of the platform, they are also responsible for the network configuration, the IaC, security, the CI/CD…
Data Analysts explore the data, create new gold layers tables, propose some data visualizations
Data Stewards works on data governance, ensuring consistency between all the data sources, keeping a data glossary up-to-date, and ensuring a logical data lineage.
Data Architect define, and design the major components of the data platform, ensuring business needs are met.
Data scientists explore the data, working on models and statistics related to the data.
Product Manager Data product manager role with a major focus on data related projects
Some other tasks that needs to be done but can be assigned to different roles are:
FinOps, ensuring that the costs of running stays within budget
Quality assurance
Project management
Access management
Building a Data retention policy
Security can also be the responsibility of a DevSecOps
Usually the biggest part of the data team is composed of Data Engineers and Data Analysts / Scientists.
⚠️ Note: A common mistake is to overhire Data Scientists. A common ratio is 2 to 3 DE for every DS (Source) In our experience we can’t go with less than 1DE for every DA or DS involved in the project.
References
MaatPharma
| Client | MaatPharma | Description | French medtech |
| Division | R&D Data | Details | Building a data platform in the cloud, to explore data and IA role in increasing efficiency. |
| Part | Details on the work done |
|---|---|
| 1️⃣ Co-Create & Design | Design cloud data platform (Databricks, AWS…) |
| 2️⃣ Setup & Deploy | Deploying the data platform |
| 3️⃣ Make it your own | Knowledge transfers |
Mangopay
| Client | Mangopay | Description | European Fintech |
| Division | Compliance Fraud Data | Details | Building the data platform Building tools for compliance and fraud monitoring Migrating to the cloud |
| Part | Details on the work done |
|---|---|
| 1️⃣ Co-Create & Design | Design cloud data platform (Snowflake, AWS…) |
| 2️⃣ Setup & Deploy | Migrating the data platform to the cloud |
| 3️⃣ Make it your own | Hiring the team Knowledge transfer Maintenance Long term architecture topics |
Electricité de France
| Client | EDF | Description | France national energy provider |
| Division | Production | Details | A data platform to help manage energy production, increasing energy output and improving maintenance quality |
| Part | Details on the work done |
|---|---|
| 1️⃣ Co-Create & Design | Not applicable for this client |
| 2️⃣ Setup & Deploy | Hadoop, Spark, Elasticsearch |
| 3️⃣ Make it your own | Adding new features Growing the team, tech mentorship Knowledge transfers |