We have invented a method for automatic text analysis.
The datanizing text analytics engine turns your text amounts into interpretable results. We use an analysis pipeline based on multiple machine learning technologies like NLP, feature extraction, topic modelling, embeddings together with supervised and unsupervised machine learning. It's automated and no human supervision is needed.
This can save you a lot of time and enable you to sell new data-driven services to your customer. The NIZE technology is already used by partners specializing in market research, requirements analysis and document management.
NIZE is the Text Analytics Service for all kinds of text you can think of.
To create data-driven services for your customers, it is not enough to do manual data science. You have to use the latest ML technology in an automated way. This is what datanizing does.
- B.Sc in business administration
- Product Owner for Machine Learning projects
- Background in Change Management
- Part of Plattform Lernende Systeme committee.
- Ph.D. in theoretical physics
- Data Scientist and Machine Learning Architect
- Many years experience in designing scalable systems
Conference contributions & trainings
- Heise Live webinar: Introduction into text analytics
- ML Summit 2018 in Berlin: Text Analytics for Managers
- Servus KI! Festival in Nuremberg: Challenge Accepted! Machine Learning Live-Experiment
- Strata Data 2018 in New York: From chaos to insight: Automatically derive value from your user-generated content
- MCube 2018 in London: From chaos to structure: How to derive insights from your user-generated content
- COI Forum 2018 in Nuremberg: 4 examples for efficient handling of document archives
- Hackday 2018 Tech Incubators Zollhof in Nürnberg: 1 day Machine Learning Prototyping Workshop
- BayStartUp Startup Demo Night 2018 in Nürnberg: Contributor
- Minds Mastering Machines Conference 2018 in Köln: Influencer and Trends – a network analysis of user-generated content
- World Information Architecture Day WIAD 2018 in Munich: IA for Good
- Bitkom Big Data AI Summit 2018 in Hanau: Community Analytics - How to find engagement clusters zusammen mit Consorsbank
- data2day 2017 in Heidelberg: Workshop analysis and classification of large amounts of text
- Solutions.Hamburg 2017: Articial Intelligence – comfort or loss of control (Pecha Kucha session)
- The Whitepaper Festival 2017 in Dachau: Datenspinnerei
- DataWorks Summit 2017 in München: Classifying unstructured text – a hybrid deterministic/ML approach
- Bitkom Big Data Summit 2017 in Hanau: Classification of unstructured texts - determinstic and ML approach
- Apache Big Data Konferenz 2016 in Sevilla: Classifying unstructured text: Deterministic and Machine learning approaches
- data2day 2016 in Karlsruhe: Using realtime context for optimizing website search results
- Big Data Konferenz auf der TDWI 2016 in München: Big Data between technology and organization culture
- Startplatz Köln, Big Data Konferenz #3 2016: Head or toe? Big Data between technology and organization culture
- Apache Big Data Konferenz 2016 in Vancouver: Data Science with news headlines
- Interview about Text Analytics Status Quo: Alexa, what does Text Mining mean?
- iX kompakt 2018: Three-part tutorial about text analytics with machine learning
- iX 2018: Three-part tutorial about text analysis with Machine Learning: Part 1, Part 2, Part 3
- iX Juni 2017: Apache Big Data Landscape
- Heise Developer 2017: Realtime context for optimizing website search results
- iX Dezember 2016: Report about Apache Big Data Conference 2016 in Sevilla
- Heise Developer 2016: Report about Apache Big Data Conference in Vancouver
- OBJEKTspektrum 2016: Six steps to a big data prototype
- Informatik Aktuell 2015: Anatomy of a big data project - from requirements to prototype