Social Media Analytics

Techniques and Insights for Extracting Business Value Out of Social Media

Author: Matthew Ganis,Avinash Kohirkar

Publisher: IBM Press

ISBN: 0133892948

Category: Business & Economics

Page: 350

View: 5018

Transform Raw Social Media Data into Real Competitive Advantage There’s real competitive advantage buried in today’s deluge of social media data. If you know how to analyze it, you can increase your relevance to customers, establishing yourself as a trusted supplier in a cutthroat environment where consumers rely more than ever on “public opinion” about your products, services, and experiences. Social Media Analytics is the complete insider’s guide for all executives and marketing analysts who want to answer mission-critical questions and maximize the business value of their social media data. Two leaders of IBM’s pioneering Social Media Analysis Initiative offer thorough and practical coverage of the entire process: identifying the right unstructured data, analyzing it, and interpreting and acting on the knowledge you gain. Their expert guidance, practical tools, and detailed examples will help you learn more from all your social media conversations, and avoid pitfalls that can lead to costly mistakes. You’ll learn how to: Focus on the questions that social media data can realistically answer Determine which information is actually useful to you—and which isn’t Cleanse data to find and remove inaccuracies Create data models that accurately represent your data and lead to more useful answers Use historical data to validate hypotheses faster, so you don’t waste time Identify trends and use them to improve predictions Drive value “on-the-fly” from real-time/ near-real-time and ad hoc analyses Analyze text, a.k.a. “data at rest” Recognize subtle interrelationships that impact business performance Improve the accuracy of your sentiment analyses Determine eminence, and distinguish “talkers” from true influencers Optimize decisions about marketing and advertising spend Whether you’re a marketer, analyst, manager, or technologist, you’ll learn how to use social media data to compete more effectively, respond more rapidly, predict more successfully…grow profits, and keep them growing.

Data Science Thinking

The Next Scientific, Technological and Economic Revolution

Author: Longbing Cao

Publisher: Springer

ISBN: 3319950924

Category: Computers

Page: 390

View: 1186

This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book's three parts each detail layers of these different aspects. The book is intended for decision-makers, data managers (e.g., analytics portfolio managers, business analytics managers, chief data analytics officers, chief data scientists, and chief data officers), policy makers, management and decision strategists, research leaders, and educators who are responsible for pursuing new scientific, innovation, and industrial transformation agendas, enterprise strategic planning, a next-generation profession-oriented course development, as well as those who are involved in data science, technology, and economy from an advanced perspective. Research students in data science-related courses and disciplines will find the book useful for positing their innovative scientific journey, planning their unique and promising career, and competing within and being ready for the next generation of science, technology, and economy.

Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics

Author: Marshall Sponder

Publisher: McGraw Hill Professional

ISBN: 0071768629

Category: Business & Economics

Page: 320

View: 2507

Align Strategy With Metrics Using Social Monitoring Best Practices “Two or three years from now, every public relations firm that wants to be taken seriously in the C-suite and/or a lead marketing role will have someone like Marshall in its senior leadership ranks, a chief analytics officer responsible for ensuring that account leaders think more deeply about analytics and that thfirm works with the best available outside suppliers to integrate analytics appropriately.” —Paul Holmes, The Holmes Report “Marshall has provided much-needed discipline to our newest marketing frontier—a territory full of outlaws, medicine men, dot com tumbleweeds, and snake oil.” —Ryan Rasmussen, VP Research, Zócalo Group “Marshall Sponder stands apart from the crowd with this work. His case study approach, borne of real-world experience, provides the expert and the amateur alike with bibliography, tools, links, and examples to shortcut the path to bedrock successes. This is a reference work for anyone who wants to explore the potential of social networks.” —W. Reid Cornwell, Ph.D., Chief Scientist, The Center for Internet Research “Marshall is a solutions design genius of unparalleled knowledge and acumen, and when he applies himself to the business of social media, the result is a timely and important commentary on the state of research capabilities for social media.” —Barry Fleming, Director, Analytics & Insights, WCG, and Principal, DharmaBuilt.com About the Book Practically overnight, social media has become a critical tool for every marketing objective—from outreach and customer relations to branding and crisis management. For the most part, however, the data collected through social media is just that: data. It usually seems to hold little or no meaning on which to base business decisions. But the meaning is there . . . if you’re applying the right systems and know how to use them. With Social Media Analytics, you’ll learn how to get supremely valuable information from this revolutionary new marketing tool. One of the most respected leaders in his field and a pioneer in Web analytics, Marshall Sponder shows how to: Choose the best social media platforms for your needs Set up the right processes to achieve your goals Extract the hidden meaning from all the data you collect Quantify your results and determine ROI Filled with in-depth case studies from a range of industries, along with detailed reviews of several social-monitoring platforms, Social Media Analytics takes you beyond “up-to-date” and leads you well into the future—and far ahead of your competition. You will learn how to use the most sophisticated methods yet known to find customers, create relevant content (and track it), mash up data from disparate sources, and much more. Sponder concludes with an insightful look at where the field will likely be going during the next few years. Whether your social media marketing efforts are directed at B2B, B2C, C2C, nonprofit, corporate, or public sector aims, take them to the next step with the techniques, strategies, and methods in Social Media Analytics—the most in-depth, forward-looking book on the subject.

Beyond Big Data

Using Social MDM to Drive Deep Customer Insight

Author: Martin Oberhofer,Eberhard Hechler,Ivan Milman,Scott Schumacher,Dan Wolfson

Publisher: IBM Press

ISBN: 0133509818

Category: Computers

Page: 272

View: 4107

Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult—often, because it’s so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM’s leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM’s enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends

The Power of Cognitive Marketing: IBM Watson Marketing Insights

Author: Theresa Morelli,Colin Linsky,IBM Redbooks

Publisher: IBM Redbooks

ISBN: 0738456047

Category: Computers

Page: 22

View: 5036

How do you keep the pulse of your customers today? Customers are leaving more clues than ever on what they want and need. However, the ability to get a singular view, observe trends and changes in behavior, and then respond proactively is not as simple as it seems. It can often feel like shooting at a moving target. IBM® Watson Marketing Insights provides marketing analysts with a dynamic view of customer behavior and the power of predictive insights without requiring analytics skills. Presented in an interactive visual format, marketers receive a daily feed of insights and prioritized recommendations that allow them to quickly and easily identify the most impactful areas for targeted marketing outreach. This IBM RedguideTM publication introduces the IBM Watson Marketing Insights solution and highlights the business value of the solution. It provides a high-level architecture and identifies key components of the architecture.

Outside-In Marketing

Using Big Data to Guide your Content Marketing

Author: James Mathewson,Mike Moran

Publisher: IBM Press

ISBN: 0133375617

Category: Business & Economics

Page: 208

View: 748

Supercharge ROI by Rebuilding Content Marketing Around Your Customer! Marketing has always been about my brand, my product, my company. That’s “inside-out” marketing. Today, customers hate it—and ignore it. What does work? Customized messages they already care about. Marketing that respects their time and gives them immediate value in exchange for their attention. Marketing that’s “outside-in.” Now, two renowned digital marketing thought leaders show how to integrate content marketing with Big Data to create high-ROI, outside-in marketing. James Mathewson and Mike Moran share new practices, techniques, guidelines, and metrics for engaging on your customers’ terms, using their words, reflecting their motivations. Whether you’re a content marketer, marketing executive, or analyst, you’ll learn how to: • Ease your customers’ pain—solve what keeps them up at night—with compelling content experiences • Build content that’s essential to clients and prospects in each step of their buyer journeys • Integrate search and social data into all facets of content development to continually improve its effectiveness • Build evergreen content that is continuously improved to better meet the needs of your clients and prospects • Apply advanced machine learning, text analytics, and sentiment analysis to craft more discoverable, shareable content • Shape your messages to intercept your clients’ and prospects’ information discovery in Google • Transform culture and systems to excel at outside-in marketing

Performance and Capacity Implications for Big Data

Author: Dave Jewell,Ricardo Dobelin Barros,Stefan Diederichs,Lydia M. Duijvestijn,Michael Hammersley,Arindam Hazra,Corneliu Holban,Yan Li,Osai Osaigbovo,Andreas Plach,Ivan Portilla,Mukerji Saptarshi,Harinder P. Seera,Elisabeth Stahl,Clea Zolotow,IBM Redbooks

Publisher: IBM Redbooks

ISBN: 0738453587

Category: Computers

Page: 46

View: 6231

Big data solutions enable us to change how we do business by exploiting previously unused sources of information in ways that were not possible just a few years ago. In IBM® Smarter Planet® terms, big data helps us to change the way that the world works. The purpose of this IBM RedpaperTM publication is to consider the performance and capacity implications of big data solutions, which must be taken into account for them to be viable. This paper describes the benefits that big data approaches can provide. We then cover performance and capacity considerations for creating big data solutions. We conclude with what this means for big data solutions, both now and in the future. Intended readers for this paper include decision-makers, consultants, and IT architects.

Getting Started with Data Science

Making Sense of Data with Analytics

Author: Murtaza Haider

Publisher: IBM Press

ISBN: 0133991237

Category: Business & Economics

Page: 400

View: 4163

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Enterprise Master Data Management

An SOA Approach to Managing Core Information

Author: Allen Dreibelbis,Eberhard Hechler,Ivan Milman,Martin Oberhofer,Paul van Run,Dan Wolfson

Publisher: Pearson Education

ISBN: 0132704277

Category: Computers

Page: 656

View: 6704

The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration

Python Social Media Analytics

Author: Siddhartha Chatterjee,Michal Krystyanczuk

Publisher: Packt Publishing Ltd

ISBN: 1787126757

Category: Computers

Page: 312

View: 5122

Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract actionable insights from your social data using various Python tools A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn Understand the basics of social media mining Use PyMongo to clean, store, and access data in MongoDB Understand user reactions and emotion detection on Facebook Perform Twitter sentiment analysis and entity recognition using Python Analyze video and campaign performance on YouTube Mine popular trends on GitHub and predict the next big technology Extract conversational topics on public internet forums Analyze user interests on Pinterest Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.

Analytics Across the Enterprise

How IBM Realizes Business Value from Big Data and Analytics

Author: Brenda L. Dietrich,Emily C. Plachy,Maureen F. Norton

Publisher: IBM Press

ISBN: 013383588X

Category: Business & Economics

Page: 224

View: 5259

How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics

Analytics in a Big Data World

The Essential Guide to Data Science and its Applications

Author: Bart Baesens

Publisher: John Wiley & Sons

ISBN: 1118892747

Category: Business & Economics

Page: 256

View: 7409

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Building Big Data and Analytics Solutions in the Cloud

Author: Wei-Dong Zhu,Manav Gupta,Ven Kumar,Sujatha Perepa,Arvind Sathi,Craig Statchuk,IBM Redbooks

Publisher: IBM Redbooks

ISBN: 0738453994

Category: Computers

Page: 101

View: 6845

Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments. This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.

Common Information Models for an Open, Analytical, and Agile World

Author: Mandy Chessell,Gandhi Sivakumar,Dan Wolfson,Kerard Hogg,Ray Harishankar

Publisher: IBM Press

ISBN: 0133366332

Category: Computers

Page: 240

View: 699

Maximize the Value of Your Information Throughout Even the Most Complex IT Project Foreword by Tim Vincent, IBM Fellow and Vice President, CTO for IBM Analytics Group To drive maximum value from complex IT projects, IT professionals need a deep understanding of the information their projects will use. Too often, however, IT treats information as an afterthought: the “poor stepchild” behind applications and infrastructure. That needs to change. This book will help you change it. Five senior IBM architects show you how to use information-centric views to give data a central role in project design and delivery. Using Common Information Models (CIM), you learn how to standardize the way you represent information, making it easier to design, deploy, and evolve even the most complex systems. Using a complete case study, the authors explain what CIMs are, how to build them, and how to maintain them. You learn how to clarify the structure, meaning, and intent of any information you may exchange, and then use your CIM to improve integration, collaboration, and agility. In today’s mobile, cloud, and analytics environments, your information is more valuable than ever. To build systems that make the most of it, start right here. Coverage Includes • Mastering best practices for building and maintaining a CIM • Understanding CIM components and artifacts: scope, perspectives, and depth of detail • Choosing the right patterns for structuring your CIM • Integrating a CIM into broader governance • Using tools to manage your CIM more effectively • Recognizing the importance of non-functional characteristics, such as availability, performance, and security, in system design • Growing CIM value by expanding their scope and usage • Previewing the future of CIMs

Creating Value with Big Data Analytics

Making Smarter Marketing Decisions

Author: Peter C. Verhoef,Edwin Kooge,Natasha Walk

Publisher: Routledge

ISBN: 1317561929

Category: Business & Economics

Page: 316

View: 6139

Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.

Mining the Social Web

Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

Author: Matthew A. Russell,Matthew Russell

Publisher: "O'Reilly Media, Inc."

ISBN: 1449388345

Category: Computers

Page: 332

View: 7125

Provides information on data analysis from a vareity of social networking sites, including Facebook, Twitter, and LinkedIn.

Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions

Author: National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Board on Mathematical Sciences and Their Applications,Committee on Applied and Theoretical Statistics,Committee on Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions

Publisher: National Academies Press

ISBN: 0309450780

Category: Mathematics

Page: 164

View: 4205

The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members’ career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€"exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€"these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R.

Exploring the Boundaries of Big Data

Author: Bart van der Sloot,Dennis Broeders,Erik Schrijvers

Publisher: WRR Verkenningen

ISBN: 9789462983588

Category: Big data

Page: 286

View: 8223

Though the exact nature and delineation of Big Data is still unclear, it seems likely that Big Data will have an enormous impact on our daily lives. 'Exploring the Boundaries of Big Data' serves as preparatory work for The Netherlands Scientific Council for Government Policy's advice to the Dutch government, which has asked the Council to address questions regarding Big Data, security and privacy. It is divided into five parts, each part engaging with a different perspective on Big Data: the technical, empirical, legal, regulatory and international perspective.

Managing Data in Motion

Data Integration Best Practice Techniques and Technologies

Author: April Reeve

Publisher: Newnes

ISBN: 0123977916

Category: Computers

Page: 204

View: 5341

Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"