Augmented Analytics

Augmented Analytics employs AI and machine learning (ML) to enrich and automate data analysis and business intelligence. By facilitating the discovery of insights, report generation, and data-driven decision-making, it extends accessibility to a broader user base, irrespective of their technical proficiency.

Augmented Analytics endeavors to span the divide separating raw data and insightful interpretations through the utilization of AI technologies. 


Augmented Analytics platforms frequently integrate natural language processing (NLP) functionalities, enabling users to engage with data through natural language inquiries. This simplifies the process for non-technical individuals to pose questions and obtain valuable insights from the data. Data preparation stands as a pivotal stage in the analytics journey. Augmented Analytics tools possess the ability to automate tasks encompassing data cleansing, transformation, and integration. As a result, the investment of time and effort to mold data into a fitting format for analysis diminishes significantly. Automated data discovery, aiding users in recognizing patterns, correlations, and outliers within their datasets. Enhanced visualization tools then construct interactive, user-friendly charts, graphs, and dashboards to vividly represent these insights. Augmented Analytics systems harness AI and ML algorithms to produce pertinent insights and recommendations derived from the dataset. This technology enables the identification of trends, anomalies, and latent opportunities that could remain overlooked through conventional methodologies. Augmented analytics offers predictive and prescriptive insights. This encompasses more than comprehending past events (descriptive); it entails foreseeing future outcomes (predictive) and proposing actions to enhance and guide those results (prescriptive). Contextual analysis substantially enhances the ability to make well-informed decisions. AI and ML models, empowering users to seamlessly embed predictive models or tailor-made algorithms into their analyses without necessitating extensive technical proficiency.

Benefits of augmented analytics:

  • Agility through pinpointing the search scope, presenting crucial data to the relevant individual at the opportune moment, and suggesting lucrative analytical pathways, AI-driven augmentation can accelerate the quest for valuable insights.
  • Accuracy due to more Complete Picture: Using of  AI and ML enable users to  make the best judgments possible based on a full investigation
  • Efficiency in applications where algorithms are fed by highly specialized, repetitive activities, machine learning and artificial intelligence have made remarkable progress
  • Confidence: augmented technologies are generally simple to use, they make working with data and gaining insights more accessible to a wider range of individuals

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