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1、Big Data: The Management Revolution管理學(xué)專業(yè)英語教程(第四版)Big Data: The Management RevolOutlines123 Introduction Dimensions of Big DataFive Management ChallengesOutlines123 Introduction Di Introduction We define Big Data as a capability that allows companies to extract value from large volumes of data, Like

2、any capability, it requires investment in technologies, processes and governance. Value Introduction We define BiVariety refers to the number of data types. Technological advances allow organizations to generate various types of structured, semi-structured, and unstructured data.Velocity refers to t

3、he speed at which data are generated and processed.Volume refers to the amount of data an organization or an individual collects and/or generates. Dimensions of Big DataWhat are the key difference between “Big Data and “analytics”?Big Data analyticsVariety refers to the number oSAS added two additio

4、nal dimensions to big data: variability and complexity. Variability refers to the variation in data flow rates. Complexity refers to the number of data sources.Oracle introduced value as an additional dimension of big data. Firms need to understand the importance of using big data to increase revenu

5、e, and consider the investment cost of a big data project. Additional Dimensions of Big DataBig Data analyticsIBM added veracity as a fourth dimension, which represents the unreliability and uncertainty latent in data sources.SAS added two additional dimenAn integrated view of Big DataThe three edge

6、s of the integrated view of big data represent three dimensions of big data: volume, velocity, and variety. Inside the triangle are the five dimensions of big data that are affected by the growth of the three triangular dimensions: veracity, variability, complexity, decay, and value. The growth of t

7、he three-edged dimensions is negatively related to veracity, but positively related to complexity, variability, decay , and value. An integrated view of Big DataImpacts of Big Data ApplicationPersonalization marketing By exploiting big data from multiple sources, firms can deliver personalized produ

8、ct/service recommendations, coupons, and other promotional offers. Better PricingHarnessing big data collected from customer interactions allows firms to price appropriately and reap the rewards. Cost ReductionBig data analytics leads to better demand forecasts, more efficient routing with visualiza

9、tion and real-time tracking during shipments, and highly optimized distribution network management. Improved customer serviceBig data analytics can integrate data from multiple communication channels(e.g. phone, email, instant message) and assist customer service personnel in understanding the conte

10、xt of customer problems holistically and addressing problems quickly. Impacts of Big Data ApplicatioFive Management ChallengesLeadershipTalent ManagementTechnology ConcernsDecision MakingCompany CultureBig datas power does not erase the need for vision or human insight. As data become cheaper, the c

11、omplements to data become more valuable.New technologies do require a skill set that is alien to most IT departments. Its too easy to mistake correlation for causation and to find misleading patterns in the data. Big DataFive Management ChallengesLeadTechnology Concerns- Big Data Security Challenges

12、Technology Concerns- Big Data The Future of Big DataBig datas emergence has not remained isolated to a few sectors or spheres of technology, instead demonstrating broad applications across industries. In light of this reality, companies must first pursue big data capabilities as necessary ground-lev

13、el developments, which in turn may facilitate competitive advantages. Formidable challenges face firms in pursuit of big data integration, but the potential benefits of big data promise to positively impact company operations, marketing, customer experience, and more. The Future of Big DataBig dataT

14、ext 2: Is Your Company Ready for a Digital Future? - OutlineBig Data FrameworkFour Big Data StrategiesFour Pathways for TransformationThe Evolution of Big Data1234Text 2: Is Your Company Ready Big Data Framework Social AnalyticsDecision SciencePerformance ManagementData ExplorationData TypeNon-trans

15、actional DataTransactionalDataMeasurementExperimentationBusiness ObjectiveBig Data Framework Social AnalBig Data FrameworkThe First dimension - Business ObjectiveWhen developing big data capabilities, companies try to measure or experiment. When measuring, organizations know exactly what they are lo

16、oking for and look to see what the values of the measures are. When the objective is to experiment, companies treat questions as a hypothesis and use scientific methods to verify them. The Second dimension - Data TypeIn their normal course of functioning, companies collect data on their operations a

17、nd capture it in their database that has a structure or schema. We call this transactional data.In other instances, companies deal with data that come from sources other than transactions and are typically unstructured (e.g., social media data). Big Data FrameworkThe First diPopular Big Data Techniq

18、ues (1)Transactional DataBusiness Intelligence /Online Analytical Processing (OLAP): Users interactively analyze multidimensional data Users can roll-up, drill-down, and slice data BI tools provide dashboard and report capabilitiesCluster Analysis: segment objects into groups based on similar proper

19、ties or attributesData Mining:Process to discover and extract new patterns in large data setsPredictive Modeling:A model is created to best predict the probability of an outcome.A/B Testing: A method of testing in which a control group is compared to test groups to determine if there is an improveme

20、nt based on the test conditionTechniquePopular Big Data Techniques (1Popular Big Data Techniques (2)Non-transactional DataCrowdsourcing: A process for collecting data from a large community or distributed group of people Idea submission is a common crowdsourcing activityTextual Analysis: Computer al

21、gorithms that analyze natural language Topics can be extracted from text along with their linkagesSentiment Analysis: A form of textual analysis that determines a positive, negative or neutral reaction Often used in marketing brand campaignsNetwork Analysis: A methodology to analyze the relationship

22、 among nodes (e.g., people) On social media platforms, it can be used to create the social graph of follower and friends connections among usersTechniquePopular Big Data Techniques (2Four Big Data StrategiesPerformance ManagementBy exploiting big data from multiple sources, firms can deliver persona

23、lized product/service recommendations, coupons, and other promotional offers. Data ExplorationHarnessing big data collected from customer interactions allows firms to price appropriately and reap the rewards. Social AnalyticsBig data analytics leads to better demand forecasts, more efficient routing

24、 with visualization and real-time tracking during shipments, and highly optimized distribution network management.Decision ScienceBig data analytics can integrate data from multiple communication channels(e.g. phone, email, instant message) and assist customer service personnel in understanding the

25、context of customer problems holistically and addressing problems quickly. Four Big Data StrategiesPerforHow companies compare on digital business transformation?How companies compare on digitFour Pathways for TransformationStandardize firstMove companies from the Silos and Complexity quadrant to th

26、e industrialized quadrant.Rely on building a platform mindset with API-enabled services. Improve customer experience firstMove from the Silos and Complexity to the Integrated Experience quadrant.Develop new attractive offers, build mobile apps and websites, improve call centers, and empower relation

27、ship managers.Take stair stepsAlternate their focus from improving customer experience to improving operations and then back again. Create a new organization.Allow an enterprise to build its customer base, people, culture, processes, and systems from scratch to be future-ready. Four Pathways for Tra

28、nsformatiBig Data 2.0 (2005-2014) Big data 2.0 is driven by Web 2.0 and the social media phenomenon. Web 2.0 refers to a web paradigm that evolved from the web technologies of the 1990s and allowed web users to interact with websites and contribute their own content to the websites.Big Data 1.0 (1994-2004) Big data 1.0 coincides with the advent of e-commerce in 1994, during which time online firms were the main contributors the web content. User-generated content was only a marginal part of web content due to the technical limitation of web applications. The Ev

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