TDWI White Paper Library
TDWI maintains this library of white papers as a resource for in-depth research and commentary about the big data, business intelligence, data warehousing, and analytics industry. The content in this repository is crafted by TDWI's software and consulting partners. To find out how your company can promote its content in this library, click here.
With predictive analytics, the enterprise learns from its cumulative experience (data), and takes action to apply what's been learned.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights.
Data exploration and analysis is a repetitive, iterative process, but in order to meet business demands, data scientists do not always have the luxury of long development cycles. What if data scientists could answer bigger and tougher questions faster?
Honda's process for gathering customer feedback about issues and classifying this information was extremely time consuming as individuals had to read and classify each message, which averages about 310,000 messages per month in Japan alone. So Honda worked with IBM to implement a cognitive solution using IBM Watson Explorer to help extract and classify the incoming feedback.
Financial services firms are facing a new set of challenges and risks. In an increasingly global, mobile, and connected world, customers expect the companies with which they do business to leverage Big Data, analytics, mobile, cloud, and other technologies to improve the customer experience.
This document outlines how best-in-class firms use efficient content processes to achieve higher levels of customer-centricity.
This report helps enterprise architecture (EA) professionals make the right choice when requirements are skewed to the needs of information workers who need to create, collaborate on, share, and find enterprise content.