- Only 20% of all AI solutions make into production, because of technical integrations problems or lack of trust by the decision-makers.
- A successful data, analytics & AI strategy identifies and prioritizes those use cases with a high probability of success for deployment.
- This means, focusing on those AI products with measurable impact and high user acceptance.
- The method of Data Strategy Design and the free (open source) tools from the Data Strategy Designkit help you to design and implement value-oriented and user-centric data strategies for your industrial company.
- At the example of a fictive but typical company, you will see the canvas tools in action – and if you want, you can help draft the data strategy in Miro.
Martin Szugat
Managing Director at Datentreiber
With his strategy consultancy Datentreiber, Martin Szugat supports companies in their digital transformation to data-driven business models and processes with strategy workshops, consulting and seminars. His approach to data strategy design is applied by a wide range of companies across industries and disciplines to design analytical solutions for their own business or their clients and to develop successful data strategies.
Prior to Datentreiber, Martin Szugat was a partner and managing director of SnipClip, an agency for social media marketing & analytics solutions. The bioinformatics graduate has researched machine learning and data mining and worked as a freelance specialist author and IT consultant. Since 2014, he has served as program director for the Predictive Analytics World & Deep Learning World conferences in Germany. He has been lecturing on Data Thinking & AI Product Management at the education provider StackFuel and at the Hochschule für Wirtschaft in Zurich since 2020. He is also on the advisory board for media & IT at DDG AG and a sought-after keynote speaker at international and national conferences and events.