article thumbnail

Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

article thumbnail

Accelerating Insight and Uptime: Predictive Maintenance

Cloudera

While both are far superior to traditional Corrective maintenance (action only after a piece of equipment fails), Predictive is by far the most effective. In fact, McKinsey points to a 50% reduction in downtime and a 40% reduction in maintenance costs when using IoT and data analytics to predict and prevent breakdowns.

IoT 98
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

What is data science? Transforming data into value

CIO Business Intelligence

What is data science? Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. Data science gives the data collected by an organization a purpose. Data science vs. data analytics.

article thumbnail

The most valuable AI use cases for business

IBM Big Data Hub

Energy Companies in the energy sector can increase their cost competitiveness by harnessing AI and data analytics for demand forecasting, energy conservation, optimization of renewables and smart grid management. Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions.

article thumbnail

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

article thumbnail

A Few Proven Suggestions for Handling Large Data Sets

Smart Data Collective

Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated.

article thumbnail

Top 10 Analytics And Business Intelligence Buzzwords For 2020

datapine

Moreover, as most predictive analytics capabilities available today are in their infancy — they have simply not been used for long enough by enough companies on enough sources of data – so the material to build predictive models on was quite scarce. Augmented Analytics.