About Ekimetrics
Our goal: combine high impact with long-term business purpose.
Ekimetrics is one of the world’s largest independent analytics firms. We operate from Paris, London, New York, Hong Kong and Shanghai with 400+ in-house Data Scientists, Data Engineers, Data Architects, DevOps Engineers, Full Stack Developers, Product Managers, UX-UI Designers, etc. Since 2006, we have led more than a thousand data science projects in over 50 countries, generating more than €1bn in profit for our customers, and helped our customers avoiding 7000 tons of CO2 in 2022.
Redefining Performance
We help companies rethink the way they operate, so they can reconcile financial KPIs with non financial goals. We are uniquely specialized in creating scalable data and analytics solutions that drive high-impact optimizations in alignment with overarching brand strategy and sustainability goals.
In 2023, Ekimetrics has obtained the mission-driven company status, which demonstrates our strong commitment to Corporate Social Responsibility. Our mission statement: Accelerate organisations’ transformation towards sustainability, through the application of data science and artificial intelligence.
We are also Great Place to Work certified© in France, the UK, and the US, and received the ‘Best companies to Work for in Asia 2023©’ award in Hong Kong.
Check our official website to learn more about us.
Our convictions
- 🙋♂️ Human:
Agile support throughout the project that puts users at the heart of the approach. Mission-driven company, ECOVADIS and Great Place to Work© certified, and in the process of B CORP certification.
- 🌳 Ecological:
A greenIT approach with a responsible use of digital technology. AI at the service of environmental matters with commitments for all employees who are trained in these issues.
- 🔍 Transparent:
An AI mastered thanks to the interpretability of the algorithm; opening the black box to give the possibility for the human to keep control of the process and interpret results.
- ⚓ Equitable:
An understanding of biases on algorithms, datasets, performance measurement and context to ensure non-discriminatory treatment by algorithms.
- 🔑 Secure:
A controlled technical environment, carefully chosen to respect the principles of data security and sovereignty.
- 🙌 Responsible:
A responsible and respectful approach to data privacy in accordance with GDPR policy. A charter and a code of conduct imposed on Ekimetrics employees. We are Labelia certified.
Our methodologies
Industrialization by design 🦾
The operational implementation of a data science solution has two key pre-requisites for success: the robustness of the proposed tool and the scalability of the associated service. These two features provide the foundation for a stable and sustainable solution that provide for an unlimited and ever-growing number of users.
Our method to industrialize data science solutions leverages three practices:
- DevOps – for continuous and shorter analytics deployment cycles, based on a scalable and governed infrastructure which is designed for technological solutions that will evolve over time.
- DataOps – for a bolstered data production chain, from the intake of raw data all the way to the production of business value.
- MLOps – to ensure that AI’s performance and costs are under control in order to address the business objective over the long run.
Human-centered AI
AI is not the solution, it’s a feature that if well designed can add unique value to solve real problems.
Building a real data project requires interacting with experts, businesses & customers and our approach ensures that humans are at the heart of the algorithmic creation process (UX design, simulations, trust design, active learning)
At Ekimetrics we are careful to apply design thinking methodology to ensure the consistency of our product vision with end user needs.
The success of a data-driven initiative is when it impacts operational processes, aligned with the company objectives, which requires the delivered solution to address the 3Us:
Usable: integrated within the technical stack and connected with operational systems.
Useful: understood by business users who are able to interpret and act upon the results and recommendations.
Used: actually used by business users for decision making, and continuously improved to follow the business context and evolutions.
Agile Data Science Framework
Our approach in using an agile methodology allows this theory to be put into practice:
Fast iterations: To proceed through iterative cycles interspersed with phases of re-prioritization of tasks, demos and user feedback, to approach final expectations as quickly as possible.
User centric: To include end users from the very first phase of the project thanks to a design thinking approach to always identify the right problem and the right solution.
Built for and by a team: To minimize silos in agile teams by constituting multidisciplinary and complementary teams and by establishing DevOps / DataOps practices.
Prioritized by value and risk: Any task must be evaluated at each iteration on several axes: the risk incurred, the complexity, and the expected business value. Deprioritizing is not a taboo.
Continuous improvements: Each iteration must allow the project to move forward and the teams to learn, by monitoring quality KPIs and by carrying out retrospectives on what worked and what didn't.
