Modern analytics technologies
The era of artificial intelligence (AI) and machine learning has arrived, thanks to
virtually limitless data storage and lightning-fast processing speeds. Analytics are being
“augmented” by these developments, making them infinitely more efficient than before.
AI and machine learning analytics can identify trends, locate outliers, and make
correlations in Big Data much more quickly and accurately than ever before. They can use the
cloud to access more data from more sources, such as social media and Internet of Things
sensors, to uncover insights, opportunities, and threats that would otherwise go unnoticed.
Machine learning algorithms can even simplify some of the most difficult steps in the
analytics process, allowing even non-data scientists – not just data scientists – to use
advanced and predictive analytics.
All of this, of course, is accessible through mobile devices, allowing users to get answers
to ad hoc questions no matter where they are.
What is Advanced analytics?
Advanced analytics refers to a form of analytics that employs sophisticated tools and
techniques to explore data autonomously (or semi-autonomously). Predictive modelling, data
and text mining, sentiment analysis, machine learning, neural networks, statistical
algorithms, and complex event processing are examples of technologies and techniques that go
beyond conventional BI capabilities.
What is Big Data analytics?
Big Data analytics is a form of advanced analytics that analyses very large datasets from a
variety of sources, including structured, semi-structured, and unstructured data. Big Data
analytics can uncover hidden patterns, unknown correlations, and other meaningful insights
in datasets using complex tools and techniques like predictive modelling, what-if analysis,
and machine learning algorithms.
What is Augmented analytics?
These powerful AI-driven analytics democratise advanced analytics by automating complex
processes and allowing users to ask questions and understand answers with minimal training.