Artificial Intelligence Digital transformation Government Retail 
erwin
OCTOBER 16, 2020
Without automation and the development of a governed data pipeline, you’ll never have enough data scientists in the front office to put the data to work. What processes should governance use? Data Intelligence Provides an EDGE. There’s a common denominator in what they’re all missing, and that is data intelligence.
Info Source
JUNE 20, 2018
Irvine, CA, June 20, 2018 – Kofax ® , a leading supplier of software to automate and digitally transform information intensive processes, today announced the addition of artificial intelligence (AI), machine learning and other capabilities in its Kapow 10.3 Robotic Process Automation (RPA) software. And Kapow 10.3
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Info Source
OCTOBER 6, 2020
But to be more impactful on business process automation, intelligent Capture 2.0, based on artificial intelligence and invoking machine learning, is critical. The pandemic has accelerated the digital transformation. into their systems. Capture 2.0
erwin
JUNE 11, 2020
Transforming Industries with Data Intelligence. Data intelligence has provided useful and insightful information to numerous markets and industries. Businesses are then introduced to modern data science and data intelligence tools to enhance and fine-tune their products and processes. Data quality management.
IBM Big Data Hub
OCTOBER 20, 2023
Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. Improved governance AI governance seeks to guarantee the ethical, responsible and compliant development and implementation of an organization’s AI and ML models.
Reltio
JULY 2, 2019
It’s the classic garbage in garbage out, but it is even more important now as we move into the real drive towards machine learning and artificial intelligence. Particularly the data governance side is beginning to be seen as really important.
Reltio
MARCH 20, 2023
As the world embraces data and the technology that brings it to life, we see four key themes emerging in data management: Artificial intelligence (AI), data virtualization, real-time integration, and multi-cloud strategies. For example, AI can classify and label data, identify patterns and trends, and monitor data quality.
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