Analyzing Multi-Structured Data
- 3,626 views
Given its ability to analyze structured, unstructured, and "multi-structured" data, Hadoop is an increasingly viable option for analytics and business intelligence within the enterprise. Dramatically ...
Given its ability to analyze structured, unstructured, and "multi-structured" data, Hadoop is an increasingly viable option for analytics and business intelligence within the enterprise. Dramatically more scalable and cost-effective than traditional data warehousing technologies, Hadoop is also increasingly used to perform new kinds of analytics that were previously impossible. When it comes to Big Data, retailers are at the forefront of leveraging large volumes of nuanced information about customers, to improve the effectiveness of promotional campaigns, refine pricing models, and lower overall customer acquisition costs. Retailers compete fiercely for consumers' attention, time, and money, and effective use of analytics can result in sustained competitive advantage. Forward-thinking retailers can now take advantage of all data sources to construct a complete picture of a customer. This invariably consists of both structured data (customer and inventory records, spreadsheets, etc.) and unstructured data (clickstream logs, email archives, customer feedback and comment fields, etc.). This allows, for example, online retailers with structured, transactional sales data to connect that data with unstructured comments from product reviews, providing insight into how reviews affect consumers' propensity to purchase a particular product. This session will examine several real-world customer use cases applying combined analysis of structured and unstructured data.
- Total Views
- Views on SlideShare
- Embed Views