Augmented Analytics
Make Informed Decisions Faster with Augmented Analytics
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.
What Augmented Analytics Unlocks for Your Business
Contextual and Guided Analysis
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.
Enables Collaboration
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.
ROLES WORKING TOGETHER
Data & Analysis Adoption for the Augmented Consumer
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.
Boosts Innovation
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.
