Business Analytics Machine Learning Social Technology 
CIO
JUNE 7, 2022
The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.
Datavail
MARCH 3, 2022
Today’s advanced technologies provide data analytics programming to understand, learn from, and harness the values hidden deep in those data center depths. Data Science = Business Intelligence. The process is more of an approach to computing than an individual technology. Contact an Expert ».
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Apiumhub
MARCH 18, 2021
This information might include mobile app usage, digital clicks, interactions on social media and more, all contributing to a data fingerprint that is completely unique to its owner. More data is available to businesses than ever, which is why business analytics is a growing field. What is Business Analytics?
O'Reilly Media - Ideas
MARCH 1, 2023
million users are actually working on and what they’re learning day-to-day. That’s a better measure of technology trends than anything that happens among the Twitterati. But other technology topics (including some favorites) are hitting plateaus or even declining. Business (13%), security (8%), and web and mobile (6%) come next.
Cloudera
MAY 27, 2021
An authoritarian regime is manipulating an artificial intelligence (AI) system to spy on technology users. No matter how good the intentions behind the development of a technology, someone is bound to corrupt and manipulate it. Social media platforms have struggled with this. It’s not the machine’s fault.
Sunflower Lab
JULY 5, 2023
This has become true with the addition of Artificial Intelligence (AI), Machine Learning (ML) and Robotic Process Automation (RPA) in businesses. This blog explores how the implementation of Intelligent Process Automation leads to growth and productivity of business and its operations.
Hu's Place - HitachiVantara
MAY 10, 2019
Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.
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