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Augmented analytics describes the process of using advanced technologies such as artificial intelligence (AI), machine learning (ML) and natural language generation (NLG) to transform how analytics can be built, consumed and shared with everyone. It concentrates on automating analysis processes that are a part of the data preparation, discovery and insight generation lifecycle, and it makes tools previously limited to specialized data science and machine learning (DSML) solutions more accessible and streamlined for the larger business population.
Augmented analytics encompasses several emerging, evolving and established analytics techniques such as machine-assisted insights which automatically generate visualizations and calculation creation triggered by the action of a user asking a question from their analytics tool, and automated analytics which runs always-on analysis to surface relevant changes in data without the need for manual exploration. It is beneficial for large, complex datasets with high dimensionality and significantly increases the efficiency and effectiveness of analytical efforts.
Augmented analytics is used by a variety of organizations today to identify, manage and understand business data faster and easier through the use of automation, algorithms and natural language capabilities that enhance the analytics life cycle – data preparation, data discovery, insight generation and insight explanation.
Independent Software Vendors (ISVs) and enterprise organizations can instantly surface opportunities and risk, and add capability and value that complement traditional dashboards and reporting tools, increasing avenues for insights and reducing the workload for their teams. Augmented analytics is particularly beneficial for industries where data and variables are highly complex or too vast in volume for users to reliably perform analysis with only manual BI tools.
The automatically generated alerts and machine-assisted insights augmented analytics provides can help end-users realize more efficient and precise data monitoring and analysis, reduce analytical bias by being able to access findings from other areas they may not have known or thought to look, and accelerate the data preparation and discovery process for all.