How to manage data science as a process
of continuous improvement?

Prof. Dr. Diego Kuonen; CStat PStat

It is increasingly clear that organizations do not know how to manage data science at the enterprise level. As such, this presentation addresses the issues associated with managing the process at an enterprise level by illustrating our “data science management process”

Analytics purpose: Generate value for many –
Analytics needs: Access to Data, Quality of Data,
Speed to Results

Cosimo Caforio, General Manager StatSoft Europe

“Digitalization and transformation” is all about data. Most of the new value in digitalization projects comes from data. Data is important and when it’s generated, data must be fit for analytical usage and purpose.

Simplified robust Data Architectures models
to drive analytical success

Erik Fransen, General Manager Connected Data Group

The foundation of any digital transformation is connecting to data, unifying data, and providing data in the right format to the right users, for the right analytical purpose. Simplification, robustness, and manageability of your data architecture are key in creating that foundation and achieve analytical excellence.

Best practices

Analytics Use case by StatSoft
CSL Behring is a global pharmaceutical company. Their use case shows how to implement analytics that are generating value for many.

Data Use case by Connected Data Group
The use case of a leading financial services organization shows how a Data & Analytics Platform, fueled by Data Virtualization, accelerates innovation with data and enables users to have easy, quick and integrated access to any data.

Q & A

Attendees can share examples and questions relevant to enable continuous improvement.