Augmented analytics combines sophisticated machine learning (ML) and natural language processing (NLP) to enable users to extract intelligence more quickly and simply. In addition, this kind of automation guides the user toward new insights, through built-in trend pattern recognition and pattern recognition tools.
Augmented analytics share the following characteristics:
- Smart data discovery tools assisting business professionals to leverage sophisticated analytical techniques without guidance from a technical professional or analyst.
- Self-Service business intelligence platforms that make strategic, operational, and tactical plans for the business.
- Cognitive Search, where advanced search methodology of diverse data sources delivers more ingestible, comprehendible, and organized content.
- Automated data preparation that cleanses data more quickly.
Augmented analytics tools are new in adoption and maturity. Gartner estimates that augmented analytics will grow at twice the rate of non-augmented and will deliver twice the business value.
Beware of using augmented analytics on poor-quality or biased inputs, as this will skew analysis, leading to incorrect conclusions.
Other Definitions of Augmented Analytics Include:
- Using “machine-learning automation to supplement human intelligence across the entire analytics life-cycle.” (Jen Underwood, InformationWeek)
- “An approach that automates insights using machine learning and natural-language generation.” (Gartner IT Glossary)
- The “combination of machine learning to automate data preparation, insight discovery and insight sharing as an area of growing strategic importance.” (Peter High, Forbes Magazine).
Businesses Use Augmented Analytics to:
- Provide critical data faster.
- Better understand the report.
- Save time and money by making better use of data scientist expertise, instead of creating an automated report.
- Handle massive amounts of big data, as 80 percent of this remains unused or underutilized.
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