Elektron tijoratda katta ma'lumotlar tahlili: tizimli ko'rib chiqish va kelajakdagi tadqiqotlar uchun kun tartibi

Appendix 2: Nature of big data used in business analytics

Nature	Description	Example (Study)
Voluminous	Large volume of data that either consume a huge amount of storage or consist of a large number of records (Davenport et al., 2012; Russom, 2011).	Amazon introduced search-inside-the-books option consisting of 120,000 books (Davenport, 2006). Dell initiated the development of a database that includes 1.5 million records related to sales and advertisements (Davenport, 2006). Netflix analyzes customer choice and customer feedback from over one billion reviews (Davenport and Harris, 2007a). Match.com has billions of data points to analyze generated from the past 17 years (Kiron et al., 2012b). On Facebook, 30 billion pieces of content are shared every month (Manyika et al., 2011). Tesco generates more than 1.5 billion new items of data every month (Manyika et al., 2011). Wal-Mart’s data warehouse includes some 2.5 petabytes of information (Manyika et al., 2011).
Variety	Data generated from a greater variety of sources and formats, and that contain multidimensional data fields (Davenport et al., 2012; Russom, 2011).	The DDB Matrix database includes all print, radio, network TV, and cable ads of Dell, the computer manufacturer, as well as regional sales data (Davenport, 2006). Credit card companies use website click-stream data and other data formats from call center operations to customize offers (Davenport and Patil, 2012). A retailer’s analytic model may include customer profiles and purchase history, regional and seasonal buying patterns, optimizing of supply chain operations, and unstructured data from social media to customize predictions by product, store concept, and promotional campaigns, etc. (Biesdorf et al., 2013). Cross-selling uses all possible data that can be identified and stored about a customer, including the customer’s demographics, purchase history, preferences, real-time locations, and other facts to increase the average purchase size (Manyika et al., 2011). Retailers use sentiment analysis to assess the real-time response to marketing campaigns and to make

		adjustments as needed. The evolving field of social media analytics plays a key role as consumers are relying increasingly on peer sentiment and recommendations (Manyika et al., 2011). Research identified 10 distinct methods in which to interact with consumers on social media throughout the product decision-making process, which range from passive techniques (monitoring blogs and social networks for references to brands) to direct engagement in the form of targeted marketing, new-product introductions, or consumer outreach during public relations (Chandrasekaran et al., 2013).