SAP Big Data and analytics combined with SAP provide the flexibility to use technologies. Such as big data warehousing, predictive analytics and in-memory computing. Which are to manage and visualize data and generate insights at the right time and from the right side to gain time. Here is another use of SAP Big Data.
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SAP Big Data Use
Log Analysis
Log data is a fundamental foundation of many commercial big data applications. This log management and analysis tools existed long before big data. But with the exponential growth of business activity and transactions, storing, processing, and presenting log data in the most efficient and cost-effective way can become an enormous problem.
Many open source and commercial log analysis tools can give you the ability , collect, process, and analyze massive log data without having to dump the data into relational databases and retrieve it via SQL queries. The synergy between log search capabilities and big data analytics has enabled organizations to gain insights for more agile operations. Big data log analysis applications are now widely used for various business objectives, from IT system security and network performance to market trends and e-commerce personalization.
Ecommerce Personalization
Remember when you were lazy surfing online shopping sites to find the perfect gift for a friend or family member? How often does you type in the search box, click on the navigation bar, expand product descriptions, or add a product to your shopping cart? If you were an ecommerce business, each of these actions can become key to optimizing the overall shopping experience. And so, the daunting tasks of collecting, processing, and analyzing shopper behavior and transactional data open up enormous possibilities for big data in e-commerce.
A powerful search and analysis platform for large amounts of data enables retail companies to send e-mails clean and enrich product data for a better search experience on both desktop and mobile; and use predictive analytics and machine learning to predict user preferences using log data, then match products into a most likely purchase order that maximizes conversion. There has also been a new movement toward real-time e-commerce personalization, enabled by the massive processing power of Big Data.
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Detection of Insurance Fraud
Organizations that handle large numbers of financial transactions continue to look for more innovative and effective approaches to combating fraud. Health insurance companies are no exception, as fraud can cost the industry up to $5 billion a year. In the traditional fraud detection model, fraud investigators must work with BI analysts to run complex SQL queries against invoice and claim data, and then wait weeks or months for the results. This process sometimes leads to long delays in legal fraud cases and thus to enormous losses for companies.
Big data technologies can process billions of billing and claims records and extract them into a search engine, allowing investigators to analyze individual records by performing intuitive searches through a graphical user interface. Predictive analytics and machine learning capabilities enable a big data fraud detection platform to provide automatic alerts once it detects a pattern that matches a previously known fraud scheme.
Recommendation Engines
If you use online streaming If you’ve browsed media, you may have noticed these “recommended for you” videos, movies, or music. Doesn’t it feel great to have a custom selection just for you? It’s easy. Save time. Overall a satisfying user experience, right? Also, did you notice that the more videos and movies you watched, the better the recommendations got? As the media and entertainment space is full of strong competitors, the ability to provide the best user experience will be the deciding factor.
Big Data, with its scalability and power to process large amounts of both structured and unstructured data can allow businesses to analyze billions of clicks and viewing data from you and other users like you to get the best recommendations . Over time, machine learning and predictive analytics will make the recommendations more tailored to the user’s tastes.
Automated Recruitment Candidate Placement
Recruiters often feel they do not have the career tools . Which to place candidates as quickly as possible in a competitive environment. As matching resume keywords to job descriptions no longer produces the desired results. New approaches to leveraging big data for recruiting have enabled recruiters to speed up. And automate the recruitment process like never before.
A Big data recruitment platform can pull from internal databases and provide a 360-degree view of a candidate. Such as education, experience, skills, job titles, certifications, geography, and anything else recruiters can think of. And then compare them to previous hiring experience and company salaries, previously screened candidates and others. Which that to identify the “best match.” These platforms can even go beyond matching to anticipate recruiting needs. And then suggest candidates before positions are posted. They also allow recruiters to be more proactive. It will come as a competitive advantage over their peers.