Organizations now have access to more data than ever before, but unless companies decipher how to bring this data to work it proves to be absolutely useless. Understanding the benefits of big data is not as simple as putting all the data in one place. Their real value can be harnessed only through specific big data use cases and applications. Those applications tend to vary widely across departments and industries. While there are several technical challenges associated with integrating and managing all of this data, organizations should first take the time to identify and crystallize the right benefits of big data for their business. Let’s delve into some of the key big data use cases for modern businesses that we have identified with several successful customer deployments:
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Top Big Data Use Cases for Business
eCommerce Personalization
With hundreds and thousands of search queries and customer databases, it often becomes difficult for eCommerce companies to optimize their customer’s shopping experience. This daunting tasks of collecting, processing, and analyzing shoppers’ behavior and their transaction data open up a plethora of opportunities for big data in e-commerce. Using a powerful search and analytics platform e-commerce companies can clean and enrich product data for a better search experience on both desktops and mobile devices. They can also use predictive analytics and machine learning to predict user preferences through log data, and then personalize products in a most-likely-to-buy order. This would ensure maximum conversion. Quantzig’ s customer analytics solutions provide customer insights to drive sales and improve revenues through acquisition, growth, and retention.
Fraud Detection
Fraud has been a major concern especially for companies that deal in enormous amounts of financial data. This has been putting such companies in a quest to identify the most effective approaches to fight fraud. In traditional fraud detection models sometimes causes lengthy delays in legal fraud cases, thus, huge losses for the business. With big data use cases in fraud detection, large amounts of billing and claim records can be processed and pulled into a search engine. This helps investigators to analyze individual records by performing intuitive searches on a graphical interface. Using predictive analytics and machine learning capabilities enables a big data platform for fraud detection to signal automatic red flag alerts as soon as it recognizes a pattern that matches a previously known fraud scheme.
Log Analytics
Log data forms a fundamental foundation of several big data use cases for business. Though log management and analysis tools have been in existence prior to big data, the exponential growth in business transactions can make it difficult for storing, processing, and presenting log data in an effective manner. However, the synergy between log search capabilities and big data analytics has enabled organizations to discover insights for more agile operations. Big data log analytics applications provide a variety of benefits including improving IT system security and network performance, to market trends and e-commerce personalization.
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Recruitment and Candidate Selection
Recruiters often face a lack of effective tools to place candidates as quickly as possible in a competitive environment. Today, merely matching resumes with the desired keywords no longer seem to provide the desired results. However, big data use cases in recruiting has allowed to speed up and automate the hiring process like never before. A big data recruitment platform has the ability to mine data from internal databases and provide a 360-degree view of a candidate. This includes their education, experience, skill sets, job titles, certifications, and other relevant competencies. This data is then compared to the company’s past hiring experience, salaries, and previously successful candidates to identify the best match for a particular job role. Quantzig’ s HR analytics solutions have helped several top clients across various industries to make better decisions about their workforce and improve overall operational performance.
