The digital transformation underway at Under Armour is erasing any stale stereotypes that athletes and techies don’t mix. While hardcore runners sporting the company’s latest microthread singlet can’t see Hadoop, Apache Hive, Apache Spark, or Presto, these technologies are teaming up to track some serious mileage.
Under Armour is working on a “connected fitness” vision that connects body, apparel, activity level, and health. By combining the data from all these sources into an app, consumers will gain a better understanding of their health and fitness, and Under Armour will be able to identify and respond to customer needs more quickly with personalized services and products. The company stores and analyzes data about food and nutrition, recipes, workout activities, music, sleep patterns, purchase histories, and more.
Compiling, storing, and analyzing these types of structured and unstructured data at this scale would have been nearly impossible a decade ago. Today, companies can use Hadoop to merge their data from business applications, business analytics, web logs, the Internet of Things (IoT), and many other sources to deliver context-relevant insights. When companies collect data from all sources to augment the core of their business, they often realize real-time business insights that give them a competitive edge.
Over the years, companies have invested significant amounts of time and money on untangling data schemas and making data consistent. The end goal was always to have more visibility and business insight – and to gain access to a greater portion of their own valuable business data as well as customer and partner data. Within enterprises, 60 to 73 percent of data is never used for business intelligence, analytics, or applications. An integrated, Hadoop-based data lake with integrated business systems has the potential to reduce those percentages significantly and give companies access to valuable big data signals.
Hadoop doesn’t need to enforce schema to store data, and it can store and process very large sets of structured and unstructured data. For enterprises, the unstructured data is especially intriguing, as images, video, audio, and social media are taking over the digital universe and greatly outpacing the growth of structured data. When business systems are integrated with Hadoop data lakes and business data, they have a 360-degree view of what is happening in the business.
Companies using Hadoop are storing data in thousands of nodes, and they can process that data more efficiently by implementing various SQL-based or MapReduce distributed compute frameworks. The open-source Apache Software Foundation has also opened the door to integrating multiple emerging data-processing frameworks so that all types of data can be analyzed and mined for business insights.
At Under Armour, an analytics data warehouse, SQL-based big data processing engine, and machine-learning engine work together to provide business and user insights, personalized recommendations, search enhancements, and data access, but the data innovations won’t end there.
Several Apache projects with Hadoop have defined a flexible framework that can be integrated with machine-learning tools or deep-learning libraries, which enhances digital image detection and recognition. A huge library of product images, for example, can be processed quickly or individuals in crowds can be identified automatically.
Retailers can boost sales by relating pictures of their products with a customer’s past shopping preferences. In addition, they can merge sentiment analytics and track all the steps in a customer journey, including competitive offerings and prospect behavior. By tracking social media, website activity, and call-center data after a product or service launch, companies easily understand which products are successful based on consumer posts and customer feedback.
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