Augmented Analytics: Analytics Acquires Machine Learning and Natural Language Processing
By: Wade Curtis
Small to medium businesses recognize the importance of data analytics but have some barriers preventing them from participating. It can be difficult to collect meaningful data and analyzing and interpreting the data requires a skill set that an SMB would more than likely need to hire a consultant. Many times, these consultants are not a financially viable option. Even larger businesses that can afford a consultant or dedicated data scientist may not have their time used efficiently as much of their time is spent cleaning up the data and other pre-analysis tasks.
Enter Augmented Analytics. In mid-2017, Gartner stated that Augmented Analytics would be an maturing technology in the next two to five years and by 2020 Augmented Analytics will be the main driver of new purchases of business intelligence and other data analysis platforms. Augmented Analytics is the use of machine learning and natural language processing in the data analysis and presentation.
Machine learning is an process where an algorithm can develop data models by exposing itself to new data. Capabilities of Cloud processing and large volumes of data are aiding machine learning in the development of new algorithms where proven algorithms were previously used.
Natural language processing allows an application to read and interpret human language. It allows an application to interpret a question, spoken or written, get the requested data and speak or write the answer to the user in their language.
These technologies in Augmented Analytics help remove some of the barriers that SMB may have when approaching analytics. The machine learning provides continued analysis of the data without the need to hire a consultant or dedicated data scientist. Natural language processing allows users to ask a question and have the application answer without the skills needed to interpret the data. Augmented Analytics also would have the ability to present insights that it found in the data.
Technical users, like data scientists, can also benefit from Augmented Analytics. Basic queries and reports are can be handled automatically by the application. This gives the data scientist the ability to put their time into more advance problems or development making better use of the company’s investment.
Now two years after Gartner’s prediction we are seeing signs of Augmented Analytics. In a recent article about 2019 trends, Gartner predicts that 40% of data science tasks will be automated by 2020. This can include regularly generated reports but also pattern and data set identification.
Companies are starting to include Augmented Analytics in their data analysis platforms they publish. Microsoft’s Power BI has been rated the top leader in Gartner’s 2019 Magic Quadrant for Analytics and BI platforms. One of the features in Power BI is the “Quick Insight” function. It can analyze a data set and apply an algorithm to automatically generate insights. Microsoft’s voice assistant, Cortana, can also be used to get information from reports and dashboards.
Tableau and Qlik are following behind Microsoft but are still leaders in the market. Their platforms are showing features of Augmented Analytics by incorporating machine learning and natural language processing.
Augmented Analytics is developing now and will begin to make its way to other platforms in the future. It’s ability to process large amounts of data and present it in a format the users of all different technical abilities can understand make it valuable tool for any business looking to analyze their data.
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