Data Science End-to-End

The first part of the workshop provides basic information on AI & Machine Learning (ML). This way, you will understand the tools needed to identify challenges and opportunities in your company that can be solved and implemented using AI & ML tools.

We explain effort drivers, lifecycle, project cycles and other general knowledge about the practical use of the technologies. Likewise, we show the limitations of the methods and which problems should be solved much more by other means.

• Machine Learning & AI - at a glance
• What do use cases look like, for example?
• What characterizes projects in this environment?
• How can projects be managed in this environment?
• How can use cases be captured and evaluated?

Based on the newly gained or deepened knowledge of AI & ML, we discuss your concrete challenges and ideas for process optimization along your entire value chain in 2 heterogeneous groups. Alternatively, we can also present use cases from our catalog in this phase to provide impulses for your different departments and record suitable cases for you in interview format.

Ideally, you outline your end-to-end processes in advance and bring this information to the workshop. This will allow us to dive in specifically and collect suitable use cases.

• Discussion rounds in 2 groups (4h)
• Big Picture for all workshop participants (2h)

Your identified use cases are prioritized jointly by the entire group. For this purpose, we use the knowledge imparted at the beginning to evaluate the effort drivers and impacts of the use cases. Prioritization is done using a four-quadrant model (similar to a SWOT analysis) with the axes 'impact' and 'feasibility'.

Finally, two to three selected use cases are specified in our Data Science Model Canvas (analogous to the Business Model Canvas). The listed dimensions summarize the respective use case, so that these requirements serve you concretely as a basis for the conception of a project or Proof of Concents.

• Prioritization of Use Cases from the previous Big Picture (2h)
• Specification of 2-3 prioritized use cases (4h)

A possible next step is the concrete testing of one of your prioritized use cases with our out-of-the-box ML services in the context of a Proof of Concepts (PoC).

We apply our services directly to your company data. This gives you direct feedback on the suitability of your data and its impact on feasibility and impact in the context of the use case with minimal project effort. In this way, we transform the use case into a tangible business case with real added value.