Augmented Analytics

Introduction

Augmented Analytics is the use of statistical and Artificial Intelligence technology to improve the data management performance , from assisting with data preparation, and drawing insights with Data Analytics, and explaining the insights or patterns using Business Intelligence tools. Data Analytics software with Augmented Analytics use Machine Learning and NLP to understand and interact with data and produce analytical results.

The Process

The Analysis process starts with data collection from multiple sources (internal databases or external data sources) and perform data transformation which is traditionally called ETL process. After the data is transformed and brought into centralized system, Predictive or Descriptive analysis is performed in order to extract actionable insights. After obtaining the desired results, these are shared with action plan to make critical business decisions and recommendations. The complete process is made in real time and saves huge amount of time for both the Analyst and Data Scientist.

Methods in Analytics

1. Descriptive Analytics : Descriptive Analytics is a statistical method that is used to search and summarize historical data in order to identify patterns or meaning.
2. Diagnostic Analytics : Diagnostic analytics is a type of advanced investigation which analyses content or data to respond to the inquiry “Why did it happen?” and is described by procedures, for example, data mining, drill-down, data discovery and correlations. Diagnostic analytics delves down deep into analyzing data to comprehend the reasons for trend.
3. Predictive Analytics : Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.
4. Prescriptive Analytics : provides entities with recommendations around optimal actions to achieve business objectives and improve the business process.

Gartner Analytics Model

Advantages:

1. Augmented analytics helps companies become more agile and helps to reduce the size of Data Scientist thus, it is less error prone and saves time in data cleaning and preparing.
2. Augmented analytics speeds up decision making and lets users make better data driven decisions.
3. Simplified process, No human Bias and Time saver for the analyst