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  <titleInfo>
    <title>Big data, big analytics</title>
    <subTitle>emerging business intelligence and analytic trends for today's businesses</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Minelli, Michael</namePart>
    <namePart type="date">1974-</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Chambers, Michele</namePart>
  </name>
  <name type="personal">
    <namePart>Dhiraj, Ambiga</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">bibliography</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">nju</placeTerm>
    </place>
    <dateIssued encoding="marc">2013</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>xxiii, 187 pages : iII., 25 cm</extent>
  </physicalDescription>
  <abstract>The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics.</abstract>
  <tableOfContents>What Is Big Data and Why Is It Important?. A Flood of Mythic "Start-Up" Proportions ; Big Data Is More Than Merely Big ; Why Now? ; A Convergence of Key Trends ; Relatively Speaking ... ; A Wider Variety of Data ; The Expanding Universe of Unstructured Data ; Setting the Tone at the Top ; Notes. --
Industry Examples of Big Data. Digital Marketing and the Non-line World ; Don't Abdicate Relationships ; Is IT Losing Control of Web Analytics? ; Database Marketers, Pioneers of Big Data ; Big Data and the New School of Marketing ; Consumers Have Changed. So Must Marketers ; The Right Approach: Cross-Channel Lifecycle Marketing ; Social and Affiliate Marketing ; Empowering Marketing with Social Intelligence ; Fraud and Big Data ; Risk and Big Data ; Credit Risk Management ; Big Data and Algorithmic Trading ; Crunching Through Complex Interrelated Data ; Intraday Risk Analytics, a Constant Flow of Big Data ; Calculating Risk in Marketing ; Other Industries Benefit from Financial Services' Risk Experience ; Big Data and Advances in Health Care ; "Disruptive Analytics" ; A Holistic Value Proposition ; BI Is Not Data Science ; Pioneering New Frontiers in Medicine ; Advertising and Big Data: From Papyrus to Seeing Somebody ; Big Data Feeds the Modern-Day Donald Draper ; Reach, Resonance, and Reaction ; The Need to Act Quickly (Real-Time When Possible) ; Measurement Can Be Tricky ; Content Delivery Matters Too ; Optimization and Marketing Mixed Modeling ; Beard's Take on the Three Big Data Vs in Advertising ; Using Consumer Products as a Doorway ; Notes. --
Big Data Technology ; The Elephant in the Room: Hadoop's Parallel World ; Old vs. New Approaches ; Data Discovery: Work the Way People's Minds Work ; Open-Source Technology for Big Data Analytics ; The Cloud and Big Data ; Predictive Analytics Moves into the Limelight ; Software as a Service BI ; Mobile Business Intelligence is Going Mainstream ; Ease of Mobile Application Deployment ; Crowdsourcing Analytics ; Inter- and Trans-Firewall Analytics ; R &amp; D Approach Helps Adopt New Technology ; Adding Big Data Technology into the Mix ; Big Data Technology Terms ; Data Size 101 ; Notes. --
Information Management. The Big Data Foundation ; Big Data Computing Platforms (or Computing Platforms That Handle the Big Data Analytics Tsunami) ; Big Data Computation ; More on Big Data Storage ; Big Data Computational Limitations ; Big Data Emerging Technologies. --
Business Analytics. The Last Mile in Data Analysis ; Geospatial Intelligence Will Make Your Life Better ; Listening: Is It Signal or Noise? ; Consumption of Analytics ; From Creation to Consumption ; Visualizing: How to Make It Consumable? ; Organizations Are Using Data Visualization as a Way to Take Immediate Action ; Moving from Sampling to Using All the Data ; Thinking Outside the Box ; 360° Modeling ; Need for Speed ; Let's Get Scrappy ; What Technology Is Available? ; Moving from Beyond the Tools to Analytic Applications ; Notes. --
The People Part of the Equation. Rise of the Data Scientist ; Learning over Knowing ; Agility ; Scale and Convergence ; Multidisciplinary Talent ; Innovation ; Cost Effectiveness ; Using Deep Math, Science, and Computer Science ; The 90/10 Rule and Critical Thinking ; Analytic Talent and Executive Buy-in ; Developing Decision Sciences Talent ; Holistic View of Analytics ; Creating Talent for Decision Sciences ; Creating a Culture That Nurtures Decision Sciences Talent ; Setting Up the Right Organizational Structure for Institutionalizing Analytics. --
Data Privacy and Ethics. The Privacy Landscape ; The Great Data Grab Isn't New ; Preferences, Personalization, and Relationships ; Rights and Responsibility ; Playing in a Global Sandbox ; Conscientious and Conscious Responsibility ; Privacy May Be the Wrong Focus ; Can Data Be Anonymized? ; Balancing for Counterintelligence ; Now What?</tableOfContents>
  <note type="statement of responsibility">Michael Minelli, Michele Chambers, Ambiga Dhiraj.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Business intelligence</topic>
  </subject>
  <subject>
    <topic>Inteligencia empresarial</topic>
  </subject>
  <subject>
    <topic>Empresas</topic>
    <topic>Innovaciones tecnológicas</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Information technology</topic>
  </subject>
  <subject>
    <topic>Tecnologías de la información</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Electronic data processing</topic>
  </subject>
  <subject>
    <topic>Procesamiento electrónico de datos</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Data mining</topic>
  </subject>
  <subject>
    <topic>Procesamiento de datos</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Strategic planning</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Política mundial</topic>
    <temporal>Siglo XXI</temporal>
  </subject>
  <classification authority="lcc">HD 38.7 M664b 2013</classification>
  <classification authority="ddc">658.4/72</classification>
  <relatedItem type="series">
    <titleInfo>
      <title>Wiley CIO series</title>
    </titleInfo>
  </relatedItem>
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    <titleInfo>
      <title>Big data, big analytics</title>
    </titleInfo>
    <name>
      <namePart>Minelli, Michael, 1974-</namePart>
    </name>
    <originInfo>
      <publisher>Hoboken, New Jersey : John Wiley &amp; Sons, Inc., [2013]</publisher>
    </originInfo>
    <identifier type="local">(DLC) 2012046973</identifier>
  </relatedItem>
  <identifier type="isbn">9781118147603 (cloth)</identifier>
  <identifier type="isbn">111814760X (cloth)</identifier>
  <identifier type="lccn">2012044882</identifier>
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    <recordCreationDate encoding="marc">121109</recordCreationDate>
    <recordChangeDate encoding="iso8601">20241031062528.0</recordChangeDate>
    <recordIdentifier source="BJBSDDR">17525487</recordIdentifier>
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