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Disco SA: Problems • Didn’t know customer – Needed to understand behavior of loyal customers • Retail Analytics • Reliant on OLTP systems and IT ppl to solve/prepare information – More employees needed more access to data to try to figure out anomalies/patterns – ‘full-service’ gas station Disco SA: Solution • Used data warehouse to centrally store data data mining used to find unknown patterns to bridge analysis gap – Descriptive Data Mining = finds patterns in data to explain/describe behavior • Segmenting = putting customers into distinct groups • Clustering = describes customer in segment – Few specific dimensions determine cluster – Predictive Data Mining = finds patterns that are used to ID trends • Finding characteristics of customers who are likely to buy particular product • Decision Tree = visual & interactive model used to break data into groups Cascade Designs: Problem • Diverse collection of loosely integrated standalone applications ‘legacy systems’ – Developed and supported by a few internal IT people – ‘full-service’ gas station • System complexity limited its efficiency and growth for company Cascade Designs: Solution & Benefits • ERP (Enterprise Resources Planning ) System – Real-time INFO – Individual Responsibility – Better control/management of inventories/procedures – Better product decisions (effective choices) • Ex: carabineers – Discovery of 20/80 customers Identifying BI Opportunities • 1. Do Homework – WHERE : BI will be used/needed • Functional Areas = dept. of buiness (FIN, OPS, HR) • Business Units = line of business that crosses funtions – Cross-functional and business-unit applications have bigger payoff potential protecting competitive advantage – WHO : will use BI and benefit from information • BI at Higher Level = need for summarized data that supports analysis of trends/patterns w/in and across functional areas • BI at Lower Level = need detailed data that is operational in nature and specific to functional area BI Opportunities • 1. Doing Homework (cont.) – WHAT : information (measures/dimensions) • Measures = KSF for functional areas /business units – Base Measures = measures captured at transaction level – Calculated Measures = computation of base measures • Dimensions = ‘by,by,by’ = data you need for analysis • Level of Detail = summarization can be derived from detail – Ex: target POS down to hour instead of minute was sufficient for good results BI Opportunities • 2. Sharing & Collecting Ideas – Brainstorming Teams = specify measures & dimensions • Why ?’s What ?’s answers define SO – Answering what you NEED out of system to perform successful analysis – BI blueprint = measure and dimension analysis (p.127) BI Opportunities • Evaluating Alternatives= synthesis of BI blueprint to list of BI opportunity areas – Group requirements by Opportunity Areas • Opportunity Areas = logical grouping of measure requirements w/ consistent data of dimensions – Consistent set of requirements/data that can be used by many groups of users – Grade Opps by Importance • Actionability = empowerment of employees to be able to ‘act’ on data • Materiality = can you save/make $$ with info • Tactical vs. Strategic – Strategic = LT = ‘process view’ – Tactical = ST = ‘functional view’ BI Opportunities • Evaluating Alternatives (cont.) • Grade Opps by Difficulty – Cross-Functionality of Design • Functional Opps = easy = functional view – Used in one functional area • Cross-Functionality = Hard = ‘process view’ – Existence & Accessibility of Data – Complexity of Calculation = BI Opportunities • Evaluating Alternatives (cont) • Rank Opps = BI Scorecard Level of Effort--low I = high priority/easy = GO FOR IT II = low & med priority, easy to do = CONFIDENT, maybe more homework II III = low priority/hard = case by case & maybe pilot test IV = high priority/hard = pilot test I Business Priority-----high III IV Case Summary • Audi = used BI to help improve efficiency of assembly line/operations • CompUSA = used BI to help improve day-today store management and operations • Cascade Design = used BI to help product/inventory management & to maintain stable workforce • DiscoSA = used BI to enhance service to keep customers loyal CAN YOU IDENTIFY PROBLEM EACH COMPANY ENCOUNTERED, SOLUTION THAT WAS IMPLEMENTED, & BENEFITS THAT CAME FROM EACH SOLUTION