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

Velocity

Frequency of data generation and/or frequency of data delivery (Russom, 2011).

Amazon manages a constant flow of new products, suppliers, customers, and promotions without compromising its promised delivery dates (Davenport, 2006). Consumer sentiment analysis through social media analytics requires real-time monitoring of the environment. The frequent flow of new data makes the decision role frequently obsolete. Trends in customer sentiments about products, brands, and companies are of high velocity with a larger volume (Davenport, 2012). At Twitter, even at 140 characters per tweet, the data volumes are estimated at eight terabytes per day due only to the high velocity/frequency (Dijcks, 2012). Retailers can now track the individual customer’s data, including click-stream data from the web, and can leverage based on their behavioral analysis. Moreover, retailers are now capable of updating such increasingly granular data in near real time to track changes in customer behavior (Manyika et al., 2011). eBay Inc. conducts thousands of experiments with different aspects of its website to determine optimal layout and other features ranging from navigation to the size of its photos (Manyika et al., 2011).

Veracity

Generating authenticated and relevant data with the capability of screening out bad data (Beulke, 2011).

eBay Inc. faced enormous data replication problems, with between 20-fold and 50-fold versions of the same data scattered throughout its various data marts. Later, eBay Inc. developed an internal website (a data hub) that enabled managers to filter data replication (Davenport et al., 2012). Using data fusion, an organization can combine multiple less reliable sources to create a more accurate and useful data point, such as social comments affixed to geospatial location information (Schroeck et al., 2012). ‘Black swans’ (the disproportionate quality of high-impact, hard-to-predict, rare events that generally people do not expect to happen, which are also said to be extreme outliers) are outside the realm of detection—by quantitative, traditional analytics. Montage Analytics has developed a tool that can predict areas vulnerable to ‘black swans’ within organizations, and other types of risk involving the impact of human behavior and motivations (Ferguson, 2012).