Dat Tran has joined DATANOMIQ as Partner and CTO.
New CTO @ DATANOMIQ
Dat Tran has joined DATANOMIQ as Partner and CTO.
Deep Learning for Attribution Modelling
AI-based Attribution Modelling in Marketing
In commerce, companies often consider cutting marketing budgets during economic uncertainty or cost-cutting efforts. However, this short-term approach can harm long-term growth and market competitiveness. Consistent marketing is vital for…
NLP for Process Mining: Transforming text data into a structured event log by using NLP text extraction.
How unstructured data can be used for Process Mining
How unstructured data can be used for Process Mining with Deep Learning for Natural Language Processing and Computer Vision.
Reduce organizational costs now with AI!
The DATANOMIQ AI Assessment is the check-up for your organization. We examine how your business organization can benefit from AI, with real results and implementation blueprints.
Why you should examine your organization for AI potential now
Why you should examine your organization for AI potential to gain process efficiency now with the DATANOMIQ AI Assessment.
Data Lakehouse by DATANOMIQ
Data Lakehouse – When does it make sense for companies?
A data lakehouse enables companies to access and process data faster and more efficiently to use data properly as a production factor.
Data Platform by DATANOMIQ
Cloud Data Platform for Shopfloor Management
Cloud Data Platform for Shopfloor Management using data from MES, ERP-Systems as well as from machine sensors directly.
Deep Learning for Car Damage Estimation - AI for recognition of damage costs by photos
Automated Car Damage Detection – Infographic
Automated Car Damage Detection – Infographic about how to use Deep Learning (AI) in order to speed up your insurance refund process.
Data Assessment
Nowadays, companies are not only evaluated according to their sales and profits, but also according to their data. Many organizations are far from satisfied with their data management, the business…
Merge of data from different systems using Fuzzy Matching
A fuzzy matching was used to combine the data from the two different sources. A selection of fuzzy string-matching algorithms was tested, for example Jaro-Winkler Distance, Levenshtein distance, Soundex or cosine similarity. The open-source algorithms can be very efficient and there is a selection to choose from depending on the use case.