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Today’s organizations have amounts of data from all aspects of their operations. The power of big data during morning coffee break. But how can big data provide business intelligence, unlike other data mining techniques?. It is different from running SQL queries or navigating your Excel spreadsheets.
1: Log Analytics
A Log data is a fundamental foundation of many business big data applications. The Log management and analysis tools have been around long before big data. But with the exponential growth of business activities and transactions. Log data a stored, processed and presented in the most efficient, cost-effective manner.
It can provide the ability collect, process, and analyze massive log data. The dump data into the relational database and retrieving through SQL queries. The synergy between log search capabilities and big data analytics. It organizes big data log analytics applications used for various business goals. Like IT system security and network performance, to market trends and e-commerce personalization.
2: E-Commerce Personalization
When you were browsing Amazon.com and Ebay to find that perfect gift for others. The search boxes, click on the navigation bar, expand product descriptions, or add a product to your cart. An e-commerce company actions key to optimizing the entire shopping experience. The tasks of collecting, processing, and transaction data for big data in e-commerce.
A powerful search and big data analytics platform allow e-commerce companies. To clean and product data for a better search experience on both desktops and mobile devices. Predictive analytics and machine learning to predict user preferences through log data. Then personalize products in a most-likely-to-buy order that maximizes conversion. A new movement towards real-time e-commerce personalization big data's massive processing power.
3: Recommendation Engines
YouTube, Netflix, Spotify online media services noticed recommended for videos, movies, or music. Doesn’t it feel great to have a selection personalized only for you? It’s easy. It’s time-saving. A satisfying user experience, right. That the more videos and movies you watched, the better those recommendations became. As the media and entertainment space is strong competitors. The ability to deliver the top user experience will be the winning factor.
The scalability and power to process amounts of both structured and unstructured data. Companies to analyze billions of clicks and viewing data from recommendations. The machine learning and predictive analytics are recommendations the user’s taste.
4: Automated Candidate Placement in Recruiting
The race to place candidates as possible in a competitive environment. As matching resume keywords with job descriptions no longer provide the desired results. Big data for recruiting speed up and automate the placement process.
Big data recruitment platforms databases and provide the view of a candidate. Such as education, experience, skill sets, job titles, certifications, geography, and anything else. Then compare to the company’s past hiring experience, salaries, before successful candidates. These platforms matching to expect recruiting needs and suggest candidates before positions posted. It recruiters to be more proactive – a competitive edge against their competitors.
5: Insurance Fraud Detection
Organizations that handle many financial transactions. To continue searching for more innovative, effective approaches to fighting fraud. Medical insurance agencies are no exception, as fraud can cost the industry up to $5. Fraud investigators need to work with BI analysts. To run complex SQL queries from the bill and claim data. Then wait weeks or months to get the results back. This process lengthy delay in legal fraud cases, thus, huge losses for the business.
It used into billions of billing and claim records pulled into a search engine. So that investigators can analyze individual records by searches on a graphical interface. Predictive analytics and machine learning capabilities big data automatic red flag alerts. It recognizes a pattern that matches a before known fraud scheme.
6: Relevancy and Retention Boost for Online Publishing
The research publishing companies giving their online subscribers. They want to build authority, expand subscriber base, and boost the bottom line. To investing in great SEO effort to make the publishing site searchable, strategizing.
First, a powerful search engine helps clean and enrich research documents’ metadata. The most relevant content and explore related content. Then machine learning and predictive analytics to order in the top results.