Augmented Analytics

By: Editor IJSMI

Narrated by: Virtual Voice

Analytics is the systematic use of data, statistical analysis, and computational methods to discover patterns, generate insights and make informed decisions. It involves Descriptive, Diagnostic, Predictive Prescriptive tools and methods. Augmented Analytics is the use of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate manual data preparation, analyzing the data and draw inferences and conclusion for faster decision making. According to Gartner Augmented analytics is defined as a tool which automates insight generation using AI and ML to transform how analytics content is developed, consumed, and shared.

Need for Augmented Analytics starts from the fact that the manual data cleaning involves the tedious process of identifying and correcting errors, inconsistencies, and missing values within a dataset. Also solving complex statistical modeling requires deep mathematical nuances of probability distributions and hypothesis testing. It involves selecting the correct tests, ensuring assumptions (like normality) were met, and interpreting results without bias. Augmented analytics tools are beginning to perform these kinds of routine tasks.

Standard business dashboards have long been the primary way to visualize statistical trends, but building these time-consuming to build and maintain. Traditional dashboards are "static" in nature; they require manual updates, constant troubleshooting of data connections, and significant design time to ensure they are readable. Augmented Analytics tools helps us to automate with this dashboard and make it real time dashboards.

Traditional statistical methods often struggle with scalability, particularly when moving from controlled experimental data to the massive scale data environments. Many classical algorithms were designed to run on a single machine's memory and cannot easily be distributed across cloud clusters. Additionally, as the number of variables (dimensions) in a dataset increases, traditional models can suffer from the vast amount of dimensionality. These shortcomings are of scalability well handled by Augmented analytics in the areas such as frequency trading
This book provides an overview of Augmented Analytics, its tools, areas of application and implementation of Augmented Analytics tools in the real time environment with help of Python Programming.