Lately, estimates have valued the augmented analytics market at $6 billion to $10 billion, and they forecast it to grow to roughly 10 times that size in the next decade, driven by the increasing demand for Augmented Data Management and Analysis.
Augmented Analytics, the newest wave of BI technology, uses AI to automate analytics workflows in platforms, contextualizing user interfaces with automated insights, generative storytelling explanations, and collaborative exploration. Powered by machine learning (ML) and generative AI, augmented analytics enables natural language queries and personalized analytics catalogs, making analytics accessible to even non-technical users.

According to Gartner, 2025 will see about 35% of organizations using generative AI as part of their identity fabric functions, leading to improved user experience and efficiency of their identity and access management (IAM) controls, making augmented data management and analysis more streamlined and efficient.

Key Features

AI and ML Integration

Augmented Analytics integrates Artificial Intelligence and Machine Learning, which analyzes historical data to uncover patterns, detect anomalies, and predict trends. This is a continuous learning process that helps to generate real-time insights from both structured and unstructured data sources, enhancing the augmented data management and analysis process.

Natural Language Processing (NLP) and Natural Language Generation (NLG)

NLP is the tool to query data using simple language, while NLG converts complex data insights into easily understandable text. This is a great boon for non-technical users who can access and interact with data conversationally, benefiting from the capabilities of augmented analytics.

Smart Data Preparation with Automation

With automation, the entire data analytics lifecycle, from data preparation to model deployment, is automated with workflows and AI-driven recommendations. Advanced algorithms assist in automated cleaning, preparing, and analyzing of data, drastically reducing the time and effort needed for manual tasks in augmented data management and analysis.

Advanced-Data Visualization and Insights

Augmented analytics tools provide automated statistical analyses and recommend visualizations, making complex data insights more interpretable. It democratizes advanced analytics with augmented data ingestion, data preparation, analytics content, and DSML model development. This feature allows users to quickly grasp and act on data insights, regardless of their technical expertise.
These tools offer contextual insights and guided experiences, helping users explore data trends and anomalies more deeply. By providing a structured approach to data exploration, augmented analytics enhances users’ understanding, curbs human biases, and accelerates decision-making capabilities.
Augmented analytics integrates with digital workplace applications, encouraging collaboration and the sharing of insights. It is a key enabler of collaboration across many users, including analytics developers, business analysts, augmented consumers, and data scientists. The democratization of capabilities, including capabilities from the data science and machine learning (DSML) market, allows everyone to access analytics.
Augmented consumers want and need a proactive, push-based delivery of intelligence driven by context – such as interests, changes in KPIs, business decisions, and recommendations. The discipline around the composable enterprise will enable composable data & analysis to augment the consumer and business analyst.
Augmented Analytics enables composability, which is the essential characteristic of data and analytics offerings, allowing organizations to quickly assemble prebuilt components instead of building and maintaining their custom applications. This provides the agility to enable sufficient innovation in augmented data management and analysis.

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