Big Data is the Next BIG Thing...

Systems that process and stock big data have become a common constituent of data management architectures in organizations. Big data is often considered by the 3Vs: the large volume of data in many environments, the wide variety of data types stored in big data systems and the velocity at which the data is generated, collected, and processed. These characteristics were first identified by Doug Laney, then an analyst at Meta Group Inc., in 2001; Gartner further popularized them after it acquired Meta Group in 2005. Several other Vs have been added to different descriptions of big data, including veracity, value and variability. While big data doesn't connect to any specific volume of data, big data deployments often involve terabytes, petabytes and even exabytes of data captured over time.

Importance of Big Data: Companies use the big data collected in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer likings and, ultimately, increase viability. Businesses that utilize big data hold a possible competitive advantage over those that don't since they're able to make faster and more informed business decisions, provided they use the data effectively. For example, big data can deliver companies with valued insights into their customers that can be used to improve marketing campaigns and techniques in order to increase customer engagement and adaptation rates. Furthermore, utilizing big data allows companies to become progressively customer centric. Historical and real-time data can be used to assess the evolving preferences of consumers, consequently enabling businesses to update and improve their marketing strategies and become more responsive to customer desires and needs.

Big data is also used by medical researchers to identify disease risk factors and by doctors to help diagnose illnesses and conditions in individual patients. In addition, data resulting from electronic health records (EHRs), social media, the web and other sources provides healthcare organizations and government agencies with up-to-the-minute information on infectious disease threats or outbreaks. In the energy industry, big data helps oil and gas companies recognize potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids. Financial services firms use big data systems for risk management and real-time analysis of market data. Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes. Other government uses include emergency response, crime prevention and smart city initiatives.

Examples of Big Data: Big data comes from innumerable different sources, such as business operation systems, customer databases, medical records, internet clickstream logs, mobile applications, social networks, scientific research repositories, machine-generated data and real-time data sensors used in internet of things environments. The data may be left in its raw form in big data systems or preprocessed using data mining tools or data preparation software so it's ready for analytics uses. Using customer data as an example, the different branches of analytics that can be done with the information found in sets of big data include the following:

Comparative analysis: This includes the examination of user behavior metrics and the observation of real-time customer engagement in order to compare one company's products, services and brand authority with those of its competition.

Social media listening: This is information about what people are saying on social media about a specific business or product that goes beyond what can be delivered in a poll or survey. This data can be used to help identify target audiences for marketing campaigns by observing the activity surrounding specific topics across various sources.

Marketing analysis: This includes information that can be used to make the promotion of new products, services and initiatives more informed and innovative.

Customer satisfaction and sentiment analysis: All the data gathered can disclose how customers are feeling about a company or brand, if any possible issues may arise, how brand loyalty might be conserved and how customer service efforts might be enhanced.

Breaking down the Vs of big data: Volume is the most cited characteristic of big data. A big data environment doesn't have to comprise a large amount of data, but most do because of the nature of the data being collected and stored in them. Clickstreams, system logs and stream processing systems are among the sources that typically produce enormous volumes of big data on an ongoing basis.

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