A growing number of senior executives are coming to realize that strategic supply management can be a significant driver of improved corporate performance and better risk management. Spend analysis is the most critical component, and the starting point, of strategic supply management and is essential to achieving world-class performance. If you can't analyze your spend data, many opportunities for improvement beyond the low hanging fruit are without doubt being missed. One of the foundation blocks for transforming an organization's practices to world-class supply management is the business process called strategic sourcing. And one of the requirements for enabling world-class strategic sourcing results is rigorous spend analysis. There is a lot of confusion on what constitutes spend analysis and what benefits can it provide. Spend analysis can help improve several areas: the identification of cost reduction opportunities, the prioritization of sourcing projects, the negotiated results, and the tracking and monitoring to ensure that negotiated results reach the bottom-line. It can also play an important role in improving compliance with Sarbanes-Oxley. However, implementations are complex and involve many departments within an organization. If the implementations are done correctly, they can yield high ROI. If not, they can consume money and deliver no returns due to lack of adoption. Spend Analysis: The Window into Strategic Sourcing is the definitive text on this important subject, written by leaders in the field. It is currently the only reference that provides in-depth guidance on what spend analysis really is, what it involves specifically and how to use it in your journey to world-class performance. It provides both an executive overview and technical details for practitioners. Readers do not need specialized knowledge in database systems, information technology or any other field to gain insights from reading this book. It is a must read for procurement, supply chain management, operations, and finance executives, and for practitioners and consultants.

DESCRIPTION There's priceless insight trapped in the flood of data users leave behind as they interact with web pages and applications. Those insights can be unlocked by using intelligent algorithms like the ones that have earned Facebook, Google, Twitter, and Microsoft a place among the giants of web data pattern extraction. Improved search, data classification, and other smart pattern matching techniques can give an enormous advantage to understanding and interacting with users. Algorithms of the Intelligent Web, Second Edition has been totally revised and teaches the most important approaches to algorithmic web data analysis, enabling readers to create machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors, and website logs. Key machine learning concepts are explained and introduced with many code examples in Python's scikit-learn. The book guides readers through the underlying machinery and intelligent algorithms to capture, store, and structure data streams. Readers will explore recommendation engines from the example of Netflix movie recommendations and dive into classification via statistical algorithms, neural networks, and deep learning. They will also consider the ins and outs of ranking and how to test applications based on intelligent algorithms. KEY SELLING POINTS Machine learning for newbies Easily accessed examples Concepts presented are technology agnostic AUDIENCE To get the most from this book, you should have a good foundation in Java programming and a general understanding of internet technology. ABOUT THE TECHNOLOGY This book provides an overview, with easy to access examples, of algorithms which learn from data. Such algorithms have been widely adopted by many large internet companies such as Facebook and Google and are continuing to grow in popularity. This book has many examples in Python using the scikit-learn library, however the concepts presented are technology agnostic and can be easily applied with any common programming language.