Six Big Data use cases for modern business
March 1, 2019
March 1, 2019
Today’s organizations have vast amounts of data from all aspects of their operations. You probably hear analysts talk about harnessing the power of big data during your daily morning coffee break, but how exactly can big data provide business intelligence unlike other data mining techniques? How is it different from running SQL queries or navigating your Excel spreadsheets?
In this blog, I'll identify six powerful big data use cases and their impacts on various industries. They showcase how structured and unstructured content processing, NoSQL databases, predictive analytics, machine learning, and advanced search relevance ranking techniques have made search and big data analytics a strategic part of the modern business’ vision.
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Log data is a fundamental foundation of many business big data applications. Log management and analysis tools have been around long before big data. But with the exponential growth of business activities and transactions, log data can become a huge headache to be stored, processed, and presented in the most efficient, cost-effective manner.
Many commercial and open source log analytics tools can provide you the ability collect, process, and analyze massive log data without having to dump the data into relational databases and retrieving it through SQL queries. 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 are now widely used for various business goals, from IT system security and network performance, to market trends and e-commerce personalization.
Remember when you were leisurely (or frantically) browsing online shopping sites to find that perfect gift for a friend or family member (or yourself)? How often do you type in the search box, click on the navigation bar, expand product descriptions, or add a product to your cart? If you were an e-commerce company, every one of these actions can become the key to optimizing the entire shopping experience. And thus, the daunting tasks of collecting, processing, and analyzing shoppers’ behavior and transaction data open up enormous opportunities for big data in e-commerce.
A powerful search and big data analytics platform allows e-commerce companies to (1) clean and enrich product data for a better search experience on both desktops and mobile devices; and (2) use 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. There has also been a new movement towards real-time e-commerce personalization enabled by big data's massive processing power.
If you’ve been on online media streaming platforms, you may have noticed those “recommended for you” videos, movies, or music. Doesn’t it feel great to have a selection personalized only for you? It’s easy. It’s time-saving. Overall, a satisfying user experience, right? Have you also noticed that the more videos and movies you watched, the better those recommendations became? As the media and entertainment space is filled by strong competitors, the ability to deliver the top user experience will be the winning factor.
Big data, with its scalability and power to process massive amounts of both structured (eg. video titles users search for, music genre they prefer) and unstructured data (eg. user viewing/listening patterns), can enable companies to analyze billions of clicks and viewing data from you and other users like you for the best recommendations. Over time, through machine learning and predictive analytics, the recommendations become better tailored to the user’s taste.
Recruiters often feel they don’t have the (right) tools in the race to place candidates as quickly as possible in a competitive environment. As matching resume keywords with job descriptions no longer provides the desired results, new approaches to using big data for recruiting have allowed recruiters to speed up and automate the placement process like never before.
A big data recruitment platform can mine from internal databases and provide a 360-degree view of a candidate, such as education, experience, skill sets, job titles, certifications, geography, and anything else recruiters can think of, then compare them to the company’s past hiring experience, salaries, previously successful candidates, etc. to identify the “best match.” These platforms can even go beyond matching to anticipate recruiting needs and suggest candidates before positions are posted, allowing recruiters to be more proactive – a competitive edge against their competitors.
Organizations that handle large amount of financial transactions continue searching for more innovative, effective approaches to fight fraud. Medical insurance agencies are no exception, as fraud can cost the industry up to $5 billion annually. In the traditional fraud detection model, fraud investigators need to work with BI analysts to run complex SQL queries from bill and claim data, then wait weeks or months to get the results back. This process sometimes causes lengthy delays in legal fraud cases, thus, huge losses for business.
With big data technologies, billions of billing and claim records can be processed and pulled into a search engine, so that investigators can analyze individual records by performing intuitive searches on a graphical interface. Predictive analytics and machine learning capabilities enable a big data platform for fraud detection to provide automatic red flag alerts as soon as it recognizes a pattern that matches a previously known fraud scheme.
For research publishing companies, giving their online subscribers the right content they want is critical to building authority, expand subscriber base, and boost the bottom line. In addition to investing in great SEO effort to make the publishing site searchable, strategizing how well the content can be served once users are on the website is a primary factor impacting conversion and repeat business.
With the rise of personalization, big data brings a new paradigm for processing and analyzing both content data (authors, titles, topics) and user data (document downloads, preferences). First, a powerful search engine helps clean and enrich research documents’ metadata to ensure users find the most relevant content and explore related content easily. Then, through machine learning and predictive analytics, the publisher will be able to serve content in a particular order in which the user’s most favorite content appear in the top results. How do they know for sure? Because they can repeatedly test and score the search engine’s performance offline to predict search accuracy and abandonment rates before putting the engine into production on the live website.
As social, cloud, and information have become the driving forces of the modern business, we expect to see more and more innovative use cases that leverage search and big data analytics to make sense and make use of the vast amount of data. Like the cloud, big data is here to stay and continue to enrich the business technology ecosystem in the coming years.