Organizations are rapidly consuming more data than ever before, and to drive their competitive advantage, they’re demanding interactive visualizations and interactive analyses of that data be embedded in their applications and business processes. This will enable them to make faster and more effective decisions based on data, not guesses. This practical book examines the considerations that software developers, product managers, and vendors need to take into account when making visualization and analytics a seamlessly integrated part of the applications they deliver, as well as the impact of migrating their applications to modern data platforms. Authors Federico Castanedo (Vodafone Group) and Andy Oram (O’Reilly Media) explore the basic requirements for embedding domain expertise with fast, powerful, and interactive visual analytics that will delight and inform customers more than spreadsheets and custom-generated charts. Particular focus is placed on the characteristics of effective visual analytics for big and fast data. Learn the impact of trends driving embedded analytics Review examples of big data applications and their analytics requirements in retail, direct service, cybersecurity, the Internet of Things, and logistics Explore requirements for embedding visual analytics in modern data environments, including collection, storage, retrieval, data models, speed, microservices, parallelism, and interactivity Take a deep dive into the characteristics of effective visual analytics and criteria for evaluating modern embedded analytics tools Use a self-assessment rating chart to determine the value of your organization’s BI in the modern data setting

One area where data analytics can have profound effect is your company’s procurement process. Some organizations spend more than two thirds of their revenue buying goods and services, making procurement—out of all business activities—a key element in achieving cost reduction. This report examines how your company can significantly improve procurement analytics to solve business questions quickly and effectively. Author Federico Castanedo, Chief Data Scientist at WiseAthena.com, explains how a probabilistic, bottom-up approach can significantly increase the quality, speed, and scalability of your data preparation operations—whether you’re integrating datasets or cleaning and classifying them. You’ll learn how new solutions leverage automation and machine learning, including the Tamr platform, and help you take advantage of several data-driven actions for procurement—including compliance, price arbitrage, and spend recovery.

Preparing and cleaning data is notoriously expensive, prone to error, and time consuming: the process accounts for roughly 80% of the total time spent on analysis. As this O’Reilly report points out, enterprises have already invested billions of dollars in big data analytics, so there’s great incentive to modernize methods for cleaning, combining, and transforming data. Author Federico Castanedo, Chief Data Scientist at WiseAthena.com, details best practices for reducing the time it takes to convert raw data into actionable insights. With these tools and techniques in mind, your organization will be well positioned to translate big data into big decisions. Explore the problems organizations face today with traditional prep and integration Define the business questions you want to address before selecting, prepping, and analyzing data Learn new methods for preparing raw data, including date-time and string data Understand how some cleaning actions (like replacing missing values) affect your analysis Examine data curation products: modern approaches that scale Consider your business audience when choosing ways to deliver your analysis