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by: Rebecca Hart

Coe VII Business 1000

Rebecca Hart
Gateway to Business
Aaron Duncan

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About this Document

Gateway to Business
Aaron Duncan
Study Guide
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This 8 page Study Guide was uploaded by Rebecca Hart on Monday October 12, 2015. The Study Guide belongs to Business 1000 at University of Denver taught by Aaron Duncan in Summer 2015. Since its upload, it has received 34 views. For similar materials see Gateway to Business in Business at University of Denver.


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Date Created: 10/12/15
Coe Big Data 10122015 What is quotBig Dataquot Michael Lewis Book called Money Ball Big Data is large and complex It is the combination of structured and unstructured data Involves volume variety veracity and velocity 0 The quot4 V squot Big Data is a collection of data sets that are so large and complex that they become awkward to work with using traditional database management tools Structured Data 0 Usually stored in databases small amounts t in spreadsheets o It is understood what should be stored where 0 Think a few words or numbers in each storage location Unstructured Data 0 Naturally gathered o Unpredictable as to what is stored where 0 Think text video audio Big Data The 4 V s Volume 0 Most obvious it is big 0 Data sizes 0 Bit Single binary digit 1 or 0 0 Byte 8 bites Ex One Letter 0 Kilobyte 1024 Bytes o Megabyte 1024 Kilobytes ex 10 page paper 0 Gigabyte 1024 megabytes ex 10 minutes of blue ray 0 Terabyte 1024 Gigabytes ex 2000 hours of CD audio 0 Petabyte 1024 Terabytes ex World of war craft storage 0 Exabyte 1024 Petabytes o Terabyte and above is considered Big Data 0 Variety 0 Text 0 Pictures 0 Video 0 Numbers 0 Tweets 0 Reviews 0 Maps 0 Who knows Basically anything that you search the internet for Veracity 0 Basically do you believe in the data Do you trust the data Do you know the source 0 Velocity o How fast does it arrive DNA sequencing Tweet throughput o How fast do you need the analysis Real time traffic Facial recognition software Why are companies interested in Big data o It may set the winning companies apart from the losing companies 0 Make or break decision are based on small percentage changes Sources of Big Data 0 Customer transactions 0 Web logs 0 RFID 0 Sensor Data 0 Social Networks 0 Internet search indexing Call detail records 0 Highway traffic 0 Military surveillance 0 Health care Epidemiology 0 Astronomy Biogeochemical Genomics Atmosphere science 0 Many many more Big Data Examples 0 Google Flu Trends 0 Where Flu is peaking 0 People searching on Google for u related shit 0 Flu Trends Video 0 When will u peak Grocers Get Personal with Coupon Deals 0 Search patterns 0 Purchasing patterns 0 Groupon How Target gured out a teen girl was pregnant before her father did 0 Purchasing patterns and search patterns 0 Target 0 About 25 products 0 Analysis 0 Shopper gets pregnancy prediction score 0 Estimation of due date within small window 0 Send coupons timed for pregnancy stages Any issues with Target s methods Big Data Tools 0 Hardware Data Centers n Collections of servers and data storage devices a Accessible via networks a Internal to company or hosted remotely cloud Networked Computers Solid State Memory Hard Drives Servers More advanced techmore storage capacityspeed Get all the computers talking to each other so data is shared easier 0 SoftwareSystems o AnalysisTechniques Big Data ToolsSoftwareSystems o Databases Organized efficient storage of data In Tables n Rows n Relationships a Broken into square chunks Oracle Microsoft and many others make database software 0 Data Warehouses Bring data from many sources together Build relationships among data sets Provide access across businesses 0 Network Software How do computers talk to each other Every computer phone etc has an address 0 Internet 0 Bid Data ToolsAnalysisTechniques 0 Descriptive Statistics Find mean median mode make a graph histogram o lnferential Statistics Take descriptive stats and draw conclusions about populations 0 Optimization Well de nedproblems from business pov and use math and stats to predict the future Best mix of products for pro t 0 Simulation Run simulations to be more sure of hypothesis 0 Data Mining Examples a Collaborative ltering a Customer Churn Certain customers will stop being customers look at past behaviors of customers complaints returns and predict if a customer will leave 0 Can we keep them as a customer a Fraud detection a Financial modeling a Hiring and promotion Two critical conditions must be present a The organization must have clean consistent data a The events in that data should re ect current and future trends 0 Predictive Analytics Highly computational data mining tech that uses info and business intelligence to build a predictive model for a given business application a Insurance retail healthcare travel nancial services CRM SRM credit scoring etc a Predict possible future behavior by nding a pattern 0 Text Analytics Uses statistical Al and linguistic technologies to convert textual information into structured information n Watson from Jeopardy a Can computers automatically nd patterns for us 0 Using Big Data or Nah ls data well de ned 0 Does it deal with a large amount of data 0 Walmart sales forecasts I A o Ticketmaster ticket sales I A o Fb user interactions I A 0 NY times online news stories I A 0 NSA phone call tracking 0 Corporate Email storage 0 Net ix streaming data I A o DU student grade data X o Moneyball X 0 Levels of big data GuessNot so good GatherGood AnalyzeBetter Make DecisionsNow we re getting somewhere OOOO


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