Social Data Analytics is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease.  You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization’s next social data analytics project.Provides foundational understanding of new and emerging technologies―social data, collaboration, big data, advanced analyticsIncludes case studies and practical examples of success and failuresWill prepare you to lead projects and advance initiatives that will benefit you and your organization

Social Data Analytics: Collaboration for the Enterprise (The Morgan Kaufmann Series on Business Intelligence)

Computers & Technology

Krishnan, Krish

Rogers, Shawn P.

Computer Science

Morgan Kaufmann

Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse.

Data Warehousing in the Age of Big Data (The Morgan Kaufmann Series on Business Intelligence)

Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.).

Building Big Data Applications

Software

Academic Press

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now!Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text.
Master these ten objectives:
<ul>
<li>Build an unstructured data warehouse using the 11-step approach</li>
<li>Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure</li>
<li>Overcome challenges including blather, the Tower of Babel, and lack of natural relationships</li>
<li>Avoid the Data Junkyard and combat the Spider's Web</li>
<li>Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative development</li>
<li>Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement</li>
<li>Design the Document Inventory system and link unstructured text to structured data</li>
<li>Leverage indexes for efficient text analysis and taxonomies for useful external categorization</li>
<li>Manage large volumes of data using advanced techniques such as backward pointers</li>
<li>Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances</li>
</ul>

Building the Unstructured Data Warehouse: Architecture, Analysis, and Design

Inmon, W.H.

Databases & Big Data

Technics Publications, LLC

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