What is Big Data Analytics? Importance, Types & Applications
We live in a time when technology has achieved its peak and entirely taken over our lives. We use and generate new data on a massive scale every day, and machines feed on this data to give businesses an insight into the latest trends. Machines have become partially intelligent, and the volume of data shared among devices is enormous. These high volumes of data collection are called big data.
Big data is like oxygen to businesses that rely on it to compete in the dynamic marketplace. We generate nearly 2.5 quintillion bytes of data every day. Since conventional computing cannot handle such large volumes of data, businesses use specialized tools to extract meaningful information from this data through a process called big data analytics.
What is Big Data Analytics?
Big data analytics is the process of extracting meaningful insights from raw data and using this information to help in decision-making, prevent frauds, sense market trends and customer preferences, correlate data sets, and find hidden patterns. Businesses and corporations leverage various sophisticated analytical tools and AI-powered algorithms to evaluate the data and connect the dots to deliver actionable results to businesses.
Importance and Application of Big Data Analytics
Big data analytics means separating large amounts of data and finding useful clues. What makes big analytics interesting is the ability of intelligent tools to determine data usefulness and futility and convert useful data into valuable business insights. Businesses and commercial applications use data to sense customer preferences. This is why each time you open Netflix, YouTube, or Shopify, you get to see personalized views based on your browsing history and personal inclinations.
Businesses use big data analytics to identify patterns and interpret data to find the latest market trends and customer choices. The conclusions drawn from this data help businesses improve customer experience, develop effective marketing and sales strategies, forecast demand, and help decision-making related to price optimization, product development, customer retention, etc. Logistics and supply chain organizations use big data to increase the agility of the whole system, quick inventory replenishment, estimate delivery delays, and optimize routes.