First pattern
dataproduct

Define your data strategy and vision

Design useful Data products

Let's work together !

Why ?

Develop a Data-Driven culture

You struggle to unleash the power of data in your organization because most POC/MVP initiatives have not delivered the expected value. One of the major challenges of data-driven projects in enterprises is the adoption by end users.

According to a 2021 NewVantage study, for 92% of Fortune 100 companies, the biggest obstacle to becoming a data-driven organization remains the culture.

I want to develop a data driven culture !
datadriven

Challenges

How to prioritize and address user needs and issues during the project scoping phases in data projects?

Antoine Gri.

The expert

Antoine Grivot

Product expert

Designing a data product around user needs is a critical challenge in generating adoption of the developed product. It provides a fresh perspective on understanding users in data projects, emphasizing the context and critical performance expected when using a product. Developing data products that gain real traction among end users is crucial to reducing the overall cost of data-driven initiatives and minimizing wastage of time and resources.

Designing a data product around user needs is a critical challenge in generating adoption of the developed product. It provides a fresh perspective on understanding users in data projects, emphasizing the context and critical performance expected when using a product. Developing data products that gain real traction among end users is crucial to reducing the overall cost of data-driven initiatives and minimizing wastage of time and resources.

Antoine Gri.

The expert

Antoine Grivot

Product expert

Customized support

Our Agile and Lean approach enables teams to work in short cycles, deliver features quickly, and rapidly detect defects and quality issues. This allows us to deliver an excellent final product.

Our customizable products :

Dataengineers-talking

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.

This approach is based on practical experience to overcome client biases and focuses on the data product strategy through :

 

  • A multidisciplinary team to address all risks.

  • An iterative approach to deliver value from the first release.

 

Dataengineers-talking
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First pattern

Our business cases

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Product

3 months to help sales teams capitalize on the increased face value of meal vouchers

Discover the business case