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.
Addressing these two criteria means addressing the industrialization of the data science solution, something which still remains a major challenge for most of our clients. When faced with this challenge, leaders often struggle to reconcile the demands of their organisations for a tool that can meet the needs of multiple brands, territories and business functions with the lack of infrastructure for the creation and development of multiple use cases in a single solution. With one-size-fits-all solutions rarely viable, each new data initiative is like starting over from scratch.
Furthermore, if the process of industrialization involves a significant technical or technological component, business leaders must take into consideration two more factors in order to ensure a successful outcome. These relate to the methodology and processes in which the industrialization takes place.
First of all, it is vital to implement processes tailored to the specificities and challenges of data projects.
Our method to industrialize your data science solutions leverages three practices:
Second, but no less important, is that the project must be steered via a business-centered approach, so that any technical and implementation decisions are made in view of the expected impacts and benefits. For this we capitalize on our experience, skills and conviction in the power of your data that allows us to be, amongst other things, the best link between the various fields of expertise (data, IT, business) needed to combine for a successful deployment.
As part of the industrialization of your data science solutions we supply:
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