Build your end-to-end Data platform
Why?
You have multiple data sources in various formats coming from different applications specific to each business activity within your company (logs, insights, third party, etc.).
So how can you obtain a comprehensive view of all your data? How do you build a platform that gathers the relevant data for your company?
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Challenges
According to a study by Nucleus Research, companies can achieve a 188% return on investment by using a data platform.
This is due to cost reduction in data collection and management, as well as improved productivity and operational efficiency.
In summary, building a data platform can enhance your company's efficiency by accelerating data processing, increasing data accuracy, improving worker productivity, and reducing costs associated with data collection and management.
According to a study by Nucleus Research, companies can achieve a 188% return on investment by using a data platform.
This is due to cost reduction in data collection and management, as well as improved productivity and operational efficiency.
In summary, building a data platform can enhance your company's efficiency by accelerating data processing, increasing data accuracy, improving worker productivity, and reducing costs associated with data collection and management.
The expert
Vincent D.
Lead Data Engineer
With its expertise in Data Engineering and strategic partnerships (Snowflake, Databricks, Google Cloud), we are committed to guiding you in building the most relevant data platform that ensures the quality, performance, and scalability of your business.
Our customizable product
Technical challenge
During the technical challenge, we identify business challenges and potential solutions for the design of your data strategy. Then, we perform an analysis of your existing infrastructure to verify technical feasibility.
ETL Creation
During the creation of your ETL, we are committed to building an efficient system for data extraction, transformation, and storage. With the objective to:
Streamline, synchronize, and format data flows
Aggregate and migrate data from multiple systems
Automate manual data collection
Lay the foundation for Data Science and visualization projects
Data product discovery
The data product discovery phase aims to mitigate risks related to value, business viability, usability, and technical feasibility of a data product in the most cost-effective manner possible.
This approach is based on hands-on experience to overcome client biases and focuses on the data product strategy by:
Utilizing a multidisciplinary team to address all risks.
Employing an iterative approach to deliver value from the first release.
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48 Bd des Batignolles, 75017 Paris