Augmented analytics is an approach to data analytics that employs Machine learning and natural language processing to automate analysis processes normally performed by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. The approach builds on business intelligence and analytics, with the goal of making data analysis more accessible and efficient.
In augmented analytics, machine learning algorithms sift through data to identify relationships, trends, and patterns, dynamically learning from data rather than relying on a fixed set of programmed rules. Natural language generation translates unstructured data into plain-English readable language, while natural language query allows users to query data using business terms typed into a search box or spoken aloud. Automating insights uses machine learning algorithms to automate data analysis processes, reducing the need for manual intervention.
Data Democratization
Data democratization is a key goal of augmented analytics, aiming to relieve data congestion and eliminate data gatekeepers. This process must be implemented alongside methods for users to make sense of the data, with the hope of speeding up company decision making and uncovering opportunities hidden in data. There are three aspects to democratizing data: data parameterisation and characterisation, data decentralisation using an OS of blockchain and DLT technologies with an independently governed secure data exchange, and consent market-driven data monetisation.
Decentralized identity management and business data object monetization of data ownership are two features that accelerate adoption of data democratisation. These enable individuals and organizations to identify, authenticate, and authorize participants across multiple networks and use cases, empowering users with personalized self-service digital onboarding without relying on central administration.
Use Cases
Augmented analytics has applications across various domains. In agriculture, farmers collect data on water use, soil temperature, moisture content, and crop growth; augmented analytics can make sense of this data and identify insights for business decisions. In smart cities, many cities across the United States collect large amounts of data daily, and augmented analytics simplifies this data to increase effectiveness in city management, including transportation and natural disaster response.
For analytic dashboards, augmented analytics takes large data sets and creates highly interactive and informative dashboards that assist in organizational decisions. Augmented data discovery helps organizations automatically find, visualize, and narrate potentially important data correlations and trends. Data preparation platforms can organize and clean large amounts of data for future analyses. In business, augmented analytics provides access to analysis of sales data, consumer behavior data, and distribution data, supporting decision making.
Technical Foundations
The technical foundations of augmented analytics draw from advances in Artificial intelligence and Deep learning. Machine learning models, including Neural network architectures, enable pattern recognition and predictive analytics. Natural language processing capabilities, often powered by Large language models, allow users to interact with data using conversational queries. Techniques such as Data Augmentation and Model Pruning improve model efficiency and robustness.
These systems often rely on cloud infrastructure from providers like Amazon Web Services, Microsoft Azure, and Google Cloud, which offer scalable computing resources. Specialized hardware, including AWS Trainium chips and Graphcore processors, accelerates training and inference. Research institutions such as MIT CSAIL, Stanford AI Lab, and BAIR (Berkeley AI Research) contribute to advancing the underlying algorithms.
Evolution and Adoption
Since its introduction in 2017, augmented analytics has evolved from a niche concept to a mainstream feature in business intelligence platforms. Gartner predicted that by 2020, augmented analytics would become a dominant driver of new purchases in analytics and business intelligence solutions. The approach addresses the shortage of data science talent by enabling business users to perform sophisticated analyses without specialized training.
Adoption has been accelerated by the proliferation of data sources and the need for faster decision making. Organizations across industries, from agriculture to urban planning, have integrated augmented analytics into their workflows. The technology continues to evolve with advancements in Generative AI and Transformer (architecture) architectures, which enhance natural language understanding and generation capabilities.
Challenges and Considerations
Despite its benefits, augmented analytics faces challenges. Data quality and integration remain critical, as the accuracy of insights depends on the underlying data. Organizations must implement robust data governance to ensure trust and compliance. The democratization of data access also raises concerns about security and privacy, requiring decentralized identity management and secure data exchange mechanisms.
Additionally, while augmented analytics automates many processes, human oversight is still necessary to validate findings and make final decisions. The technology is not a replacement for domain expertise but rather a tool to augment human capabilities. As of the early 2020s, ongoing research focuses on improving interpretability and reducing bias in automated insights.