Big Data Analytics is the use of specific advanced analytics techniques on big data or extremely high volume dataset to generate business insights. It starts with data collection through pre-defined templates and forms, followed by data curation, data processing, through data analysis. It allows to process structured, unstructured, and semi-structured data, join the dots to see the bigger picture, and to take data-driven, informed decisions.

Data Lakes are huge, centralized data reservoirs, which allow to store structured, unstructured, and semi-structured data at scale in As-Is format and from where different analytics programs, machine learning sequences, visualizations, and dashboards can be launched for better business decision making.

Big Data Analytics taps into huge data reservoirs to generate business insights. Visualization tools, such as TruBI, allow users to work with high data volumes and thousands of concurrent users to graphically represent data and bring out the underlying data story. The interactive visual elements allow users to understand the trends and patterns in the data and pinpoint the outliers.

Big Data and Big Data Analytics provide the vast datasets and analyses for TruAI, in specific, or AI / ML algorithms, in general, to learn from and generate sharper insights, which are not evident from slicing-dicing and analytics.

Big Data Analytics and AI/ML are symbiotic. AI/ML learns from Big Data and Big Data Analytics to get better at unsupervised learning and decision making. While Big Data Analytics leverages AI/ML to offer vivid analyses and better decision making power.

The combination of Big Data Analytics and AI/ML can be used holistically in all data-intensive sectors, such as Banking, Financial Services, and Insurance (BFSI), Healthcare, Communication, Media, and Entertainment (CME), Education Technology, Manufacturing & Supply Chain Management, Energy & Natural Resources, and Government & Public Sector Units.