Big data is a term used to refer to amounts of quantifiable information that are measured in billions of gigabytes. Big data is measured in the “3Vs” of volume, variety, and velocity, which each represent challenges for businesses, governments, healthcare or social services organizations. Approaches to big data analytics combine data from multiple sources and use ever-evolving techniques to analyze data with flexible outcomes and strategies.
History of Big Data
Information science expert Gil Press wrote in Forbes Magazine that by the 1960s, scientific knowledge had begun to grow exponentially, doubling every fifteen years. The first information census was conducted in Japan in 1975, finding that information collection was outpacing demand for the data. Digital storage became more affordable than paper storage by 1996, and information was projected to exceed two thousand petabytes by 2000. “The typical piece of information will never be looked at by a human being,” Michael Lesk wrote in 1997. By 2008, Bounie and Gille estimated that the world produced 14.7 exabytes of new information, triple the amount available just five years before.
Making Sense of Big Data
The amount of data that businesses can access today is overwhelming without analytics. Big data analysis is now tailored toward specific businesses and their needs. SAS software developers identified three benefits to specialized software aimed at specific business sectors including marketing, restaurants, and manufacturing. Big data analysis can provide insights into a business that can reduce costs through increased efficiency, enable better and quicker decision making, and help businesses develop and introduce new products to meet the needs of customers.
Business Sectors Which Use Big Data
Customer satisfaction data benefits the travel and hospitality industry by allowing a resort to identify negative experiences and quickly turn them into positive ones. The health industry uses data on population health trends as well as service usage, treatment outcomes, and care plan effectiveness. Government uses of big data are extensive and include methods to plan traffic flow, keep crime rates down, and improve environmental quality. The retail industry uses big data analysis to improve customer experiences, increase ordering efficiency, and guide in-store experiences to increase sales and customer retention.
Methods of Big Data Analysis
The definition of big data means that the information is too great in volume to be analyzed using traditional tools. Data management tools are developed by big data analysis companies to maintain standards and reliable processes for data quality. Data mining enables businesses to see patterns in large quantities of data, which in turn, can help to give actionable business intelligence. In-memory analytics is a specialized form of big data analysis that draws immediate insights from computer memory. It also enables businesses to test scenarios and build models of predictive behavior. Open source software called Hadoop supports the storage of large amounts of data and can support distributed computing that can grow along with business needs and quantity of information.
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Other applications for big data analysis include predictive analytics, which use algorithms to provide a “best guess” of future results on an individual customer, or organization-wide basis. Text mining analyzes insights from web-based comment fields, books, emails or blogs to determine trends and opinions about products and brands. Big data analytics is a complex, challenging field with solutions that vary depending upon the industry sector and use of data for strategic planning and long-term problem-solving and insight.