Companies in banking and securities, healthcare, manufacturing, insurance, transportation and trade generate massive amounts of data. Yet deriving meaning from the data remains a challenge. Insights from data are of great value since it serves as a guiding light in the decision-making process. Data generated across these industries are collected from diverse sources, then standardized and transformed for injecting it into the Analytics workflow. Here patterns are discovered from the data which is used to generate the insights.
If data is the gas in a car, then, analytics is the car itself. Currently, there are a few trends and topics in tech without which the talk around technology and innovation is incomplete -- analytics, artificial intelligence, blockchain to name a few. Augmented analytics is an extension of analytics that focuses on three main areas -- Machine Learning, Natural language generation (NLP) and, Insight automation. The basic premise of augmented analytics is the elimination of painstaking tasks in the process of data analysis and, replacing them by automation thus, refocusing human attention on modern analytics, business process, and business value generation. As per predictions made by Gartner, over 40% of tasks involved in data science will be automated thus, increasing productivity, quickening the process, and initiating broader usage of data and analytics.
Machine learning automation is affecting all of enterprise software, but will completely transform how we build, analyze, and consume data and analytics. Tableau, Qlik, Tibco Spotfire) have disrupted the traditional BI market (e.g. Yet, as transformative as these tools have been, analytics is once again at a critical inflection point. Across the analytics stack, tools have become easier to use and more agile, enabling greater access and self-service. And yet organizations' processes for preparing data for analysis, analyzing data, building advanced analytics models, interpreting results and telling stories with data remain largely manual and prone to bias.
Global Augmented Analytics Market was valued US$ 4.6Bn in 2018 and is expected to reach US$ 20.2Bn by 2026 at a CAGR of 19.98%. This report provides a detailed analysis of the market segment based on insurance type, sales channel and region. This report also focuses on the top players in North America, Europe, Asia Pacific, Middle East & Africa, and South America. The objective of the report is to present a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, industry-validated market data and projections with a suitable set of assumptions and methodology. The report also helps in understanding the global augmented analytics market dynamics, structure by identifying and analysing the market segments and project the global market size.