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What Is Information Harvesting?

By Sandi Johnson
Updated May 16, 2024
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Information harvesting, better known in certain industries as data mining, is a method of gathering and qualifying large amounts of both hard copy and digitized information. A large part of information harvesting rests in the analysis of data. By collecting, categorizing, and summarizing information, users can determine relationships and trends that might otherwise go unnoticed. Such information is useful to marketing professionals, researchers, retail store owners, market analysts, accounting professionals, and numerous other professionals, business entities, and statisticians.

Computers are central to information harvesting. Mining large volumes of data from customer records, online databases, or international retail records could take years to collect, summarize, and categorize on paper. Data mining software products and other methods of incorporating automated sorting via a computer allow for faster and more accurate processing of large volumes of information. For example, automated information harvesting can track, sort, and summarize the shopping habits of hundreds of thousands of customers at various national retail establishments in a matter of moments.

Economic studies and developing marketing strategies are the two most common uses of information harvesting technology. A university or government agency, for example, might collect and aggregate thousands of bits of information regarding specific industries, such as manufacturing. Using information harvesting technology, research groups can spot economic trends such as average raw material prices, upswings in production of certain products, historical data on manufacturing times, or even trends in imports and exports of specific goods.

Marketing professionals and retailers use data mining and other information harvesting methods to spot trends in shopping habits, cost of goods sold, and inventory levels, to name just a few uses. Specific information, such as what day of the week most men shop, or the number of times the average family patronizes a local grocery store can provide valuable information to store owners and marketing professionals. Based on this type of information, sales events, customer reward programs, and pricing strategies can be developed and planned to maximize effectiveness and success.

Identifying trends and patterns within large volumes of information, as well as establishing relationships among different data, assists with compiling meaningful historical data for use in predicting future performance. Transactional data, such as computerized sales records or accounting information, is one type of data commonly used for information harvesting when applied to a single business. Industry data, such as industry-wide sales, local market forecasts, and raw goods purchases are all types of data used for large-scale information harvesting. Typically, large-scale data mining projects are performed by financial or economic analysts to spot industrial or national trends.

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