Optimally positioned: the right IT infrastructure for data-based business processes
Operational data science: how do I create the right IT infrastructure for the requirements in the age of big data?
In order to tap the potential of data science sustainably, a seamless implementation of analytical solutions in your business processes is required.
This presentation will give you valuable insights into the technical requirements of data science and the most important building blocks of your IT landscape. Furthermore, a deeper understanding for the right tool and technology decisions based on practical reference scenarios can be gained.
- Development environment: which environment is the best choice for which user?
- Source code management: How can version management help to make source code comprehensible and transparent?
- Testing: How can I increase the quality of analyses through systematic tests?
- Staging architecture: How do I create suitable environments for different levels of maturity of the analyses?
- Documentation: How does a documentation that encourages collaboration and efficiency of the analysis team looks like?
- Access management: What requirements have cross-departmental workflows on the authorization concept?
- Security: How can you reconcile analytics and IT security?
- Infrastructure sizing: What computing capacities do you need for the implementation of data science projects?
- Reporting: Whether it is static PDF, HTML documents or interactive web applications - how do I implement the optimal toolset for my reporting?
- Package management: What are the requirements for using the packages regarding the management of dependencies or the deployment of updates?