《管理学专业英语教程（精编版）（第二版）》课件Unit 11 Big Data-The Managment Revolution.ppt

1、OutlinesOutlines123 Introduction Dimensions of Big DataFive Management Challenges Introduction We define Big Data as a capability that allows companies to extract value from large volumes of data,Like any capability,it requires investment in technologies,processes and governance.ValueVarietyVariety

2、refers to the number of data types.Technological advances allow organizations to generate various types of structured,semi-structured,and unstructured data.VelocityVelocity refers to the speed at which data are generated and processed.Volume Volume refers to the amount of data an organization or an

3、individual collects and/or generates.Dimensions of Big DataWhat are the key difference between“Big Data and“analytics”?Big Data analyticsSAS added two additional dimensions to big data:variability and complexity.VariabilityVariability refers to the variation in data flow rates.ComplexityComplexity r

4、efers to the number of data sources.Oracle introduced valuevalue as an additional dimension of big data.Firms need to understand the importance of using big data to increase revenue,and consider the investment cost of a big data project.Additional Dimensions of Big DataBig Data analyticsIBM added ve

5、racityveracity as a fourth dimension,which represents the unreliability and uncertainty latent in data sources.An integrated view of Big DatavThe three edges of the integrated view of big data represent three dimensions of big data:volume,velocity,and variety.vInside the triangle are the five dimens

6、ions of big data that are affected by the growth of the three triangular dimensions:veracity,variability,complexity,decay,and value.vThe growth of the three-edged dimensions is negatively related to veracity,but positively related to complexity,variability,decay,and value.Impacts of Big Data Applica

7、tionFive Management ChallengesLeadershipTalent ManagementTechnology ConcernsDecision MakingCompany Culturev Big datas power does not erase the need for vision or human insight.v As data become cheaper,the complements to data become more valuable.v New technologies do require a skill set that is alie

8、n to most IT departments.v Its too easy to mistake correlation for causation and to find misleading patterns in the data.Big DataTechnology Concerns-Big Data Security ChallengesThe Future of Big DataBig datas emergence has not remained isolated to a few sectors or spheres of technology,instead demon

9、strating broad applications across industries.In light of this reality,companies must first pursue big data capabilities as necessary ground-level developments,which in turn may facilitate competitive advantages.Formidable challenges face firms in pursuit of big data integration,but the potential be

10、nefits of big data promise to positively impact company operations,marketing,customer experience,and more.Text 2:Is Your Company Ready for a Digital Future?-OutlineBig Data FrameworkFour Big Data StrategiesFour Pathways for TransformationThe Evolution of Big Data1234Big Data Framework Social Analyti

11、csDecision SciencePerformance ManagementData ExplorationData TypeNon-transactional DataTransactionalDataMeasurementExperimentationBusiness ObjectiveBig Data FrameworkThe First dimension-Business ObjectiveWhen developing big data capabilities,companies try to measure or experiment.When measuring,orga

12、nizations know exactly what they are looking 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 c

13、ollect data on their operations and 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).Popular Big Data Technique

14、s(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 properties or a

15、ttributesData 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 improvement based o

16、n the test conditionTechniquePopular 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 algorithms that analyze natural language Topi

17、cs 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 among nodes(e.g.,people)On social media platf

18、orms,it can be used to create the social graph of follower and friends connections among usersTechniqueFour Big Data StrategiesHow companies compare on digital business transformation?Four Pathways for TransformationStandardize first Move companies from the Silos and Complexity quadrant to the indus

19、trialized 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 relationship manage

20、rs.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.Big Data 2.0(2005-2014)Big Data 2.0(20

21、05-2014)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(1994-200

22、4)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 Evolution of big dataHow does the big data e

23、volve?When the first commercial mainframe computers were introducedBig Data 3.0(2015-)Big Data 3.0(2015-)Big data 3.0 encompasses data from Big Data 1.0 and Big Data 2.0.The main contributors of Big Data 3.0 are the loT applications that generate data in the form of images,audio,and video.The loT re

24、fers to a technology environment in which devices and sensors have unique identifiers with the ability to share data and collaborate over the internet even without any human intervention.The Evolution of big data1.02.03.0In this era,web mining techniques were developed to analyze users online activi

25、ties.Web mining can be divided into three different types:web usage mining,web structure mining,and web content mining.With the rapid growth of the IoT,connected devices and sensors will surpass social media and e-commerce websites as the primary sources of big data.Social media embodied the principles of Web 2.0 and created a paradigm shift in the way organizations operate and collaborate.