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Augmented analytics is the use of machine learning (ML) and natural language processing (NLP) to enhance data analytics, data sharing and business intelligence. The concept of augmented intelligence, an overarching concept to augmented analytics, was introduced by the research firm Gartner, in their 2017 edition of the "Hype Cycle for Emerging Technologies."
Data analytics software can integrate augmented analytics tools to handle large data sets. Organizations can enter in raw data source information to these platforms that will then scrub, parse and return key data for analysis. The use of machine learning and NLP gives augmented analytics tools the ability to understand and interact with data organically as well as notice valuable or unusual trends.
The data analytics field is complex and generally requires a data scientist to extract any value from big data. This complexity is in part due to the fact that data must be gathered from a number of disparate sources, such as web analytics, marketing releases and social media posts. Collecting the data is just the first step, it also has to be prepared for analysis by being organized and refined before the analyst or data scientist can glean useful insights. The results must then be communicated to the organization along with action plans to capitalize on these insights.
Due to the manual effort required for these tasks, data scientists are currently in high demand and can be impractically expensive for some businesses. It is estimated that a data scientist can spend as much as 80% of their time gathering, preparing and cleaning up data. This is where augmented analytics can be implemented. With the addition of machine learning to data analytics, many of the time-consuming tasks of data collection and preparation can be done quickly, automatically and with fewer errors. As a result, data scientists could spend more time searching for actionable insights.