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

Big Data: The Management Revolution,管理学专业英语教程（第四版）,Big Data: The Management Revol,Outlines,Introduction,Dimensions of Big Data,Five Management Challenges,Outlines123 Introduction Di,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.,Value,Introduction We define Bi,Variety 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 the 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 Data,What are the key difference between “Big Data and “analytics”?,Big Data analytics,Variety refers to the number o,SAS added two additional 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 revenue, and consider the investment cost of a big data project.,Additional Dimensions of Big Data,Big Data analytics,IBM added veracity as a fourth dimension, which represents the unreliability and uncertainty latent in data sources.,SAS added two additional dimen,An integrated view of Big Data,The three edges 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 the three-edged dimensions is negatively related to veracity, but positively related to complexity, variability, decay , and value.,An integrated view of Big Data,Impacts of Big Data Application,Personalization marketing By exploiting big data from multiple sources, firms can deliver personalized product/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 visualization 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 context of customer problems holistically and addressing problems quickly.,Impacts of Big Data Applicatio,Five Management Challenges,Leadership,Talent Management,Technology Concerns,Decision Making,Company Culture,Big datas power does not erase the need for vision or human insight. As data become cheaper, the complements 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 Data,Five Management ChallengesLead,Technology Concerns- Big Data Security Challenges,Technology Concerns- Big Data,The Future of Big Data,Big 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-level 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 data,Text 2: Is Your Company Ready for a Digital Future? - Outline,1,2,3,4,Text 2: Is Your Company Ready,Big Data Framework,Data Type,Non-transactional Data,TransactionalData,Measurement,Experimentation,Business Objective,Big Data Framework Social Anal,Big Data Framework,The First dimension - Business ObjectiveWhen developing big data capabilities, 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 dimension - 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 unstructured (e.g., social media data).,Big Data FrameworkThe First di,Popular Big Data Techniques (1),Technique,Popular Big Data Techniques (1,Popular Big Data Techniques (2),Technique,Popular Big Data Techniques (2,Four Big Data Strategies,Performance ManagementBy exploiting big data from multiple sources, firms can deliver personalized 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 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 context of customer problems holistically and addressing problems quickly.,Four Big Data StrategiesPerfor,How companies compare on digital business transformation?,How companies compare on digit,Four Pathways for Transformation,Four Pathways for Transformati,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 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 Evolution of big data,How does the big data evolve?,When the first commercial mainframe computers were introduced,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 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.,Big Data 2.0 (2005-2014) Big d,The Evolution of big data,In this era, web 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.,The Evolution of big data1.02.,