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Big data analytics can be explained as the complex process of examining a combination of unstructured, semi-structured and structured data to discover market trends, correlations, hidden patterns, and customer preferences.

Using predictive analysis, big data analytics offer organizations customized suggestions and recommendations which allows them to launch new products per customer preferences and futuristic needs. This information can help businesses make informed decisions and growth strategies to increase their revenues.

Both business analytics and data analytics is based on extracting insights from data to enhance business performance. Business analytics targets major business implications of data to evaluate actions that should benefit the business. These may include deciding whether a company should prioritize a distinct project or whether a business should launch a new product line to the target market. Essentially, business analytics can be described as a combination of data tools, skills and apps that enable an organization to improve the efficiency of core business functions such as customer service, sales, marketing, IT, etc.

Data analytics involves analyzing raw data for hidden trends and valuable insights. Businesses generate volumes of data concerning sales, logistics, market research, or transactions. Data analysts comb through datasets to find patterns, metrics, risks, opportunities, draw conclusions about hypotheses, and develop data-based insights to support business decisions.

Data Never Sleeps 6.0 says that nearly 1.7MB of data is being generated every second for every person on the Earth which has led the company to predict 180 zettabytes of data being consumed globally by 2025. This enormous amount of data is being fed into advanced analytics and machine learning solutions that give retailers the ability to foresee imminent customer actions.

Data scientists in the retail industry work diligently to uncover hidden trends, relationships, risks and opportunities. The derived insights allow retail businesses to understand customer demand, predict market trends, create omnichannel shopping experiences and make smarter pricing decisions. Statista forecasts worldwide revenues from the big data and business analytics market to increase from 168.8 billion in 2018 to a staggering 274.3 billion in 2022. That’s a 63% growth in just 4 years.

Data ingestion is the transfer of unstructured data from its point of origination to a system or medium where it can be stored for analysis and future access. If we talk about the architectural pattern of big data, then data ingestion becomes the first stage of the journey. It is also regarded as the beginning of Data Pipeline as this is the point where data is imported for immediate use. In some instances, data is streamed in real-time, but usually it is ingested in batches. In short, data ingestion can be referred to as the process of procuring data and adding it into the Data Processing system.

The graphical representation of information and data is regarded as data visualization. The intelligent usage of visual elements such as graphs, charts, and maps, offer an accessible way for users to see and understand patterns, trends, and outliers in data. In the Big Data industry, data visualization technologies are key to evaluate information and execute data-driven decisions.