Companies across every industry are benefitting from the scale and processing power that Hadoop provides to gain deeper insights to customers and operations. From retail firms that are trying to micro-segment and improve customer experiences, to Web 2.0 companies innovating and disrupting business models in their industries, all of them are leveraging what’s possible with Big Data on Hadoop to create competitive advantage.
By providing one platform that is cost-effective for both data storage and processing, CIOs are rethinking their enterprise data architecture to identify the best place for certain types of workloads. In addition, data which was previously too expensive to store, is now made available for analysis to improve business insights. Hadoop enables capturing and storing data from every touch point of the organization. Moreover, Hadoop can process data in place (e.g., data transformation, cleansing, scoring), freeing up resources for special-purpose systems such as data warehouses, recommendation engines, content serving, and other downstream applications.
Users across industries are now able to bring structured, unstructured and semi-structured data sources together on one platform to perform deeper and more accurate analysis for improved customer interactions and day-to-day operations. Examples include building customer 360-degree views of both transactions and interactions, measuring brand health across channels, improving real-time fleet logistics, improving risk modeling and fraud detection algorithms, and improving quality control on assembly lines.
In addition to data management and analytic applications powered by Hadoop, more organizations are powering business-critical operational applications where low latency and transactional consistency are important. A large number of Web 2.0 companies are providing Hadoop-based services such as advertisement auction engines, product search, and family history services, while large enterprises are leveraging search and NoSQL databases on Hadoop (in-Hadoop databases) to create data-driven services for their customers. Examples include real-time product offers for customers, power grid optimization, and cable advertising optimization using set-top box data.