Let's build your data platform

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.

Common use-cases

Domain	Use Case
Artificial Intelligence	There is no AI without data Explore NLP, LLMs, or classical algorithms with proper data foundations.
Compliance	Ensure you comply with law, industry standards… Prevent money laundering, track client activity, detect anomalies.
Legal	Produce the legal documents you need Track lifecycle of a product across production steps.
Support	Help your support team get more efficient Centralize client information, reduce response time.
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.

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

Requirements

• The MVP/POC of the data platform 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

Other important tasks

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.