Data Enablement Data Processing Machine Learning Predictive Analytics 
Sisense
OCTOBER 6, 2020
Despite its many uses, quantitative data presents two main challenges for a data-driven organization. First, data isn’t created in a uniform, consistent format. It’s generated by a host of sources in different ways. You have to be able to work with both kinds of data in order to unlock the most comprehensive insights.
Cloudera
SEPTEMBER 12, 2018
It’s a big week for us, as many Clouderans descend on New York for the Strata Data Conference. The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Enterprise Machine Learning: .
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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. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.
Smart Data Collective
AUGUST 14, 2019
The healthcare sector is heavily dependent on advances in big data. Healthcare organizations are using predictive analytics , machine learning, and AI to improve patient outcomes, yield more accurate diagnoses and find more cost-effective operating models. However, big data poses great challenges.
Andrew White
JANUARY 11, 2021
On January 4th I had the pleasure of hosting a webinar. It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. Many data science labs are set up as shared services. I didn’t mean to imply this.
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