《管理学专业英语教程(第4版)》教学课件—lesson16BigData-TheManagmentRevolution.ppt

1、Big Data:The Management Revolution管理学专业英语教程（第四版）管理学专业英语教程（第四版）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

2、 in technologies,processes and governance.ValueVarietyVariety 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.Vol

3、ume 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 analyticsSAS added two additional dimensions to big data:variability and complexity.VariabilityVariability ref

4、ers to the variation in data flow rates.ComplexityComplexity refers 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.A

5、dditional Dimensions of Big DataBig Data analyticsIBM added veracityveracity 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

6、,velocity,and variety.vInside 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.vThe growth of the three-edged dimensions is negatively related to veracity,but positively related to comp

7、lexity,variability,decay,and value.Impacts of Big Data ApplicationFive 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 v

8、aluable.v New technologies do require a skill set that is alien 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 is

9、olated 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-level developments,which in turn may facilitate competitive advantages.Formidable challenges face

10、 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.Text 2:Is Your Company Ready for a Digital Future?-OutlineBig Data FrameworkFour Big Data StrategiesFour Pathways for Transformation

11、The Evolution of Big Data1234Big Data Framework Social AnalyticsDecision SciencePerformance ManagementData ExplorationData TypeNon-transactional DataTransactionalDataMeasurementExperimentationBusiness ObjectiveBig Data FrameworkThe First dimension-Business ObjectiveWhen developing big data capabilit

12、ies,companies try to measure or experiment.When measuring,organizations 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 dimens

13、ion-Data TypeIn their normal course of functioning,companies collect 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 u

14、nstructured(e.g.,social media data).Popular Big Data Techniques(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 Analysi

15、s:segment objects into groups based on similar properties 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

16、to test groups to determine if there is an improvement based on 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 A

17、nalysis:Computer algorithms 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

18、the relationship among nodes(e.g.,people)On social media platforms,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

19、 companies from the Silos and Complexity quadrant to the 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

20、 websites,improve call centers,and empower relationship managers.Take stair steps Alternate 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 sc

21、ratch to be future-ready.Big Data 2.0(2005-2014)Big 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 conte

22、nt to the websites.Big Data 1.0(1994-2004)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 we

23、b applications.The Evolution of big dataHow does the big data evolve?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

24、 generate data in the form of images,audio,and video.The loT refers 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

25、mining techniques were developed to analyze users online activities.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.