Data Science
Playbook

In our view, the main reason for such a high failure rate, is that often one – if not several of the three core dimensions of data science is not properly addressed:

• Business and Operations – the business case is not convincing because of a lack of strategic impact and the inability to answer critical needs;
• Data Management – the solution developed is not industrialized and therefore not scalable;
• People and Organization – the initiative was not supported with change management and therefore  not adopted by end-users.

Based on these three challenges, we developed our Data Science Playbook – a guide to properly implement your data initiatives and deliver growth for your business.

The Data Science Playbook breaks down the data journey and maximizes the value at each stage. It formalizes each critical maturity stage, allowing you to hit the necessary targets for success. We believe successful data science projects clearly validate each stage, before building towards the next.

This is the key in realizing real business gains.

From our 14-year experience in delivering more than 1,000 data science projects, we have identified the five critical maturity stages:

1.   Strategic statement
2.   Proof of value
3.   Pilot
4.   Industrialized solution
5.   Operationalized solution

Our Data Science Playbook is a journey that supports all of your data solutions, from origin to production and adoption. It will provide you with data science frameworks, methods, tools and knowledge transfer. It includes deep dives in each of  the three pillars of data science aforementioned – business and operations, data management, people and organization. As well as a detailed approach of all five critical maturity stages.

• Strategic statement: a trustworthy method to rigorously frame all aspects of your desired solution;
• Proof of value: a technical expertise to design the AI algorithms that will be at the heart of your solution;
• Pilot: a data engineering and web development know-how to transform the core AI model into a minimum viable product (MVP), that is ready to be operationally tested;
• Industrialized solution: an IT cloud-based process that will move the MVP into production by leveraging a mature data platform;
• Operationalized solution: methods, processes and tools to monitor KPIs and business gains, optimize and evolve, adapt to new use cases, etc.