Augmented Analytics

Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paperhttps://www.gartner.com/en/information-technology/glossary/augmented-analytics. 
Augmented analytics is based on business intelligence which is realized with OLAP tools. In the graph extraction step, data from different sources are investigated.
==Defining augmented analytics==
* Machine Learning - a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules.
* Natural Language Generation (NLG) - a software capability that takes unstructured data and translates it into plain-English, readable, language.
* Automating Insights - using machine learning algorithms to automate data analysis processes.
==Data democratization==
Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented alongside a method for users to make sense of the data. This process is used in hopes of speeding up company decision making and uncovering opportunities hidden in data.
==Use cases==
Agriculture - Farmers collect data on water use, soil temperature, moisture content and crop growth, augmented analytics can be used to make sense of this data and possibly identify insights that the user can then use to make business decisions.
Smart Cities - Many cities across the United States, known as Smart Cities collect large amounts of data on a daily basis. Augmented analytics can be used to simplify this data in order to increase effectiveness in city management (transportation, natural disasters, etc.).
Analytic Dashboards Augmented analytics has the ability to take large data sets and create highly interactive and informative analytical dashboards that assist in many organizational decisions.
Augmented Data Discovery - Using an augmented analytics process can assist organizations in automatically finding, visualizing and narrating potentially important data correlations and trends.
Data Preparation - Augmented analytics platforms have the ability to take large amounts of data and organize and "clean" the data in order for it to be usable for future analyses.
Business - Businesses collect large amounts of data, daily. Some examples of types of data collected in business operations include; sales data, consumer behavior data, distribution data. An augmented analytics platform provides access to analysis of this data, which could be used in making business decisions. 
==In the media==
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==References==
==External links==
* Augmented Analytics - Gartner
* What is augmented analytics? - Definition from WhatIs.com
* Augmented Analytics: the Future of Business Intelligence
* Augmented Analytics Demystified - Analytics for Humans
* What is Augmented Analytics in less than 3 minutes
* Augmented Analytics 101 - A Starter's Guide - SelectHub
* What Is Augmented Analytics? A Simple Explanation For Anyone
* What is Augmented Analytics and Why Does it Matter?
* Augmented Analytics: What it is, why it matters, and best practices
Category:Data analysis
Category:Data analysis software
Category:Machine learning
Category:Natural language processing
Category:Data scientists
Category:Automation
Category:Democratization