STAT METHDEC MOD
STAT METHDEC MOD ISDS 2001
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Test 4 Study Guide Chapter 4 Data Mining 1 Factors behind the sudden popularity in data mining a Reduction in cost of data storage and processing ampincreased hardware capacity have providedthe ability to collect and accumulate data b With increased database capacities and the availability of analysis tools many companies recognized that they have untapped data and the tools to analyze c Consolidation in a data warehouse data both at the customer level and from various sources gives the ability to analyzed from a more complete view 2 Examples of applications of data mining a Identify successful therapies for illnesses and to discover new drugs reduce fraudulent behavior insurance claims and credit card usage identify customer buying patterns reclaim profitable customers aid in marketbasket analysis better target customers clients 3 Definition and characteristics of data mining a Definition i Used to describe knowledge discovery in databases ii Uses statistical mathematical and other techniques to extract and identify useful information and subsequent knowledge from large databases iii Referred to as knowledge extraction data archaeologyexplorationdredging information harvesting b Major characteristics i Data are often buried deep within large databases ii Environment is usually a clientserver architecture iii Sophisticated new tools help remove info iv Miner is often an end user v Sometimes involves finding an unexpected result and requires end users to think creatively throughout the process vi DM tools are readily combined with spreadsheets and other SW development tools vii Large amounts of data and massive search efforts 4 How data mining works a Data mining finds patterns and defines them in terms of mathematical rules that can be used for prediction or association 5 The four broad categories for data mining algorithms 51 Prediction i Tells the nature of future occurrences of certain events based on what has happened in the past ii Basic idea classification regression iii Super Bowl winner 1 Cluster analysis i Identify natural groupings of things based on their known characteristics customer demographics past purchasing behavior c Association analysis i Find the commonly cooccurring grouping of things ii Beer and diapers iii Link analysis sequence analysis d Sequential relationships 6 Other data mining procedures include a Data visualization and time series forecasting 7 The most common of all data mining approaches are classification procedures 8 Classification involves identifying patterns of data and associates those patterns with observations belonging to a certain category a Examples credit approval store location target marketing fraud detection telecommunications route or segmentation 9 The Basic Idea a Define the data b Use the data to develop a mathematical model c Then use that model to predict the unknown outcomes for future observations 10 You would use a Decision tree for classification if the outcome is categorical and the predictors are either categorical or numeric b Linear discriminant analysis if the outcome is categorical and the predictors are all numeric have normal distributions and equal variances c Linear regression if the outcome is continuous numeric and the predictors are all numeric have normal distributions and equal variances 11 Organizations must use a standardized approach for conducting a data mining project and be able to identify some proposed models CRISPDMDMAIC SEMMA 12 The six steps ofthe CRISPDM model i CrossIndustry Standard Process for Data Mining is one of the most popular nonproprietanj standard methodologies for DM b Business Understanding amp Data Understanding i h l 1 There must be much discussion to first understand the business environment and what business questions must be addressed in order to remain competitive This goes handinhand with determining what variables must be measured in order to quantify the process Example Ifyou want to predict whether or not alcohol is involved in a crash managerspolicy makers should communicate with emergency personnel in order to recognize what factors seem to be associated with alcohol involvement ex suppose after communicating it seems that young adult males driving sports car crash into a tree at night on the weekend are associated with alcohol involvement then you should collect data on age gender vehicle type number ofvehicles involved in crash day ofweek and time of day during that point their needs to be discussion of what data is available as well c Data Preparation i ii Collect data enter info into a data format to make available for use edit and save A crash is reported in paper report form and then entered into a DB Data from different PSA are standardized and edited d Modeling i Based upon the types of variables and purpose of the analyses a particular DM procedure is selected for detecting patterns and relationships That procedure is employed in order to find the best mathematical explanation of the patterns that exist 6 Evaluation i In classification for example you want to make sure that your model set of predictors is the best model for being able to predict group membership You further want to make sure your model based upon your sample is a quotfairly good representation of the relationships that exist in the population f Deployment i Once you determine the best model for describing the business process you must use that model for making business decisions Suppose you are a Macy s marketing analyst targeting customers that have a Macy s credit card You use data mining to come up with a model to help you determine to which customers you will mail a Macy s catalog Your model indicates that those cardholding customers most likely to go to your store to make a purchase are females ages 2535 with a college degree own a home have visited a Macy s store in the last 3 months and have a Macy s credit card balance between 300 and 600 You would get your marketing department to send catalogs to those customers that fit the profile and would continue to do that on a continual basis say monthly basis After testing the model for accuracy you use the estimated regression equation to predict weekly sales Deployment is this part ofthe data mining process 13 DMAIC Define Measure Analyze Improve Control a Is utilized in SiXSigmabased data mining processes It is ordinarily utilized in manufacturing service delivery management and other activities that rely on eliminating defects waste quality control problems 14 SEMMA Sample Explore Modify Model Assess a Was developed by the SAS Institute 15 How to select in linear regression the best possible model using pvalues and adjusted R2 also know how to usedeploy a regression equation for prediction a IfPvalue lt alpha then X1 is a good predictor 16 How to calculate Linear Classi cation Functions LCFO and LCF1 given the classi cation coe icients in a printout a LCFl constant coeff X1 coeff X2 coeff X3 1 7 How to use LCF s to make a decision 18 Calculate the proportion of correct classi cations 19 How to read a classi cation matrix 20 How to interpret line listing of observation information including probability of success 21 How to determine a Decision Tree prediction algorithm using a coordinate plot of data 22 How to apply a Decision Tree prediction algorithm for predicting outcomes iquot Clustering Analysis 23 Cluster analysis places observations rows customers students etc into groups such that the members share similar characteristics but the groups themselves are highly different 24 Cluster analysis is different from classification analyses in that the groups are unknown and created in cluster in cluster analysis groups are distinct and known when conducting a classification analysis Market Segmentation 25 A common application of cluster analysis is market segmentation a Some of the other examples of cluster analysis i An analysis that aids in dividing customers into groups based upon data descriptions variables so that you can target those groups with different advertising campaigns Students may remember taking a career inventory survey and based on your response to many questions you were told or put into a cluster indicating what occupation would suit you best iii Seating guests at a wedding or social events 26 Market segmentation is used to understand the buyer behavior of customers h l 27 Market segmentation is used to help retailers in targeting similar groups ofcustomers for defining the appropriate advertising campaign 28 Market segmentation examples a Suppose you are targeting your most loyal customers You could design a market segmentation questionnaire for an airline asking for demographic information such behavior items such as frequency of ying how purchased tickets who traveled with cities own to where sat airlines own money spent on airline tickets etc b Gender age income housing type and education level are common demographic variables for clustering i Music downloads targeted to young ii Hearing aids target to elderly iii Private elementary schools might define their target market as highly educated households containing women of childbearing age c Marriott International utilized data mining analyses their set of customers and as a result created different hotels experiences Association Analysis 29 Association analysis is aimed at establishing relationships between items variables columns 30 The goal ofassociation is to group variables that are similar 31 A common application ofassociation analysis is market basket analysis 32 Examples of market basket analysis a Diapers ampbeer b Cold medicine amp tissues c Crosspromotional programs WalMart at Thanksgiving displays cornbread mix canned sweet potatoes brown sugar pecans pie shells our d Crossselling on the web Amazon s use of quotcustomers who bought BookA also bought Book B Chapter 5 33 The definitionpurpose of text mining a Definition i Text Mining the semiautomatic process of extracting patterns from large amounts of unstructured data sources b Purpose i It is the same as data mining but for unstructured data ii It is the application of DM to text files to find quothiddenquot content iii It is different from web search engines they use known relationships to find docs whereas TM aims to discover new patterns 34 Some of the most popular TM analyses a Summarization extraction b Categorization classification termdocument matrix TDM c Clustering d Concept linking association 35 Common applications of text mining a Info can be gained by quotsiftingquot through court orders law discharge summaries medicine patent files technology customer comments marketing quarterly reports finance b Email text mining can be applied to messages or emails to route them to the most appropriate party to process the message 36 The term extraction is the most basic form of text mining and is used for summarization the 1st of the 4 popular types ofTM analyses 37 The termdocument matrix TDM is used for categorizationclassification clustering and concept linking association 3 of the 4 popular types of TM analyses 38 Text mining maps unstructured information in the form of a document ofwords into a structured format in the form of a featureterm vector or a concept 39 A feature vector or term vector is a weighted list ofwords which defines a concept that describes unstructured information document ofwords 40 How a feature vector is created a Eliminate articles and words identified by domain experts as commonly used words because these words have no differentiating power stopwords b Replace words with their stems or roots Eliminate plurals and various conjugations phoned phones phoning phone c Consider synonyms and phrases student and pupil Also be able to distinguish the same word having different meanings Windows and windows d Calculate the weights of the remaining terms based upon the frequency with which the word appears One common weighting measure is the term frequency TF factor which measures the number of times a word appears in a document 41 One common weighting measures is the TF factor a How it is calculated Frequency of words used divided by words left over 42 TermDocument Matrix a Definition i A digitized organized set of documents b How it is created i Rows represent the documents columns represent terms excluding stop terms frequencies represent the number of times a term appears in a popular document It is used for conducting analyses such as classification analysiscategorization cluster analysis and association analysisconcept linking 3 of the 4 popular types of text mining analyses 43 The text mining process can be defined in 3 consecutive tasks a Establish the corpus i The purpose is to collect all documents related to a domain of interest for analysis Once the document files are collected they should all be converted to a similar format to be quotreadquot by text mining software b Create the TermDocument Matric C c Extract the knowledge 44 Some text mining applications a 45 The web is the biggest datatext repository 46 Definition of web mining a The process of discovering relationships from web data 47 Examples ofinformation found on the web a Whose home page is linked to which other pages b How many people have on their own website hyperlinks of other websites c How a particular site is organized d Tracking ofvisitors to a website each search on a search engine each click on a link a transaction on an ecommerce site 48 The 3 different areas ofweb mining a Web content mining b Web structure analysis c Web usage mining U l 49 Web content mining a 15 similar in concept to text mining b Extracts and uses the content found within the web pages 50 Web structure mining a Links within a document indicate depth of coverage 2 Extracting useful information from the analysis oflinks found in web documents c Hubs i Pages that point to many authorities in the field A hub could be a list of recommended links on an individual s home page 1 Authority pages i Those that are linked by many hubs 51 Web usage mining and how it uses clickstream data a Extracts and uses information that is generated through web page visits traffic transactions etc b The data known as clickstream data provides a trail ofuser s activities and shows the user s browsing patterns which sites are visited which pages are accessed how much time is spent on each site etc 52 Web usage mining examples 0 4 0V Data Mining goes to Hollywood Predictor analysis for film success Sharda amp Delen Bingo correct classification rate Model results 1 SVM 2 ANN 3 CART Ensemble gt individual quotSensitivity analysis Fusion algorithm Predict and explain the financial output ofa movie Data from 9806 quot opquot to quotblockbusterquot 0 Highest ROI 0 41 BA DM help 1800Flowers Excel 0 Problem needed to make decisions in real time to increase retention decrease costs and retain customers and to respond to ecommerce competition 0 Solution SAS data mining tools to discover novel patterns about customers and turn that knowledge into business transactions 0 Results I More efficient marketing campaigns I Less mailings more response rates I Better customer experience I More repeat sales 0 42 Police Department Fights Crime with Data Mining I Worldwide law enforcement agencies use data mining to fight terrorism and crime Data mining techniques improve crime fighting by quickly and easily finding patterns in unsolved cases I SPSS software has a PASW modeler using Kohonen network 0 43 Motor Vehicle Accidents amp Driver Distractions I Problem needed a way to study correlation between motor vehicle accidents and driver distractions OOOOOOOOO O 0 Solution 3 data mining techniques were used on crash information from Fatality Analysis Reporting System 0 Kohonen networks detected clusters and revealed patterns 0 Decision trees classified effectincident and suggested relationships 0 A neural network model was trained and tested to observe model effectiveness Benefits cluster analysis and other data mining techniques were combined to verify the assumptions that the causes of certain accidents were due to distractions Used by SPSS Inc an IBM company 44 A Mine on Terrorist Funding Problem needed a better way to detect crimes such as customs fraud income tax evasion money laundering and terrorist funding Solution used data mining techniques to analyze financial transactions and detect criminal activity One example was the analysis of data on import and export transactions because illegal international trade practices have been used to fund terrorists Benefits more efficient evaluation of financial transaction data aided in fighting terrorism and increased the quality ofintelligence information 45 Data Mining in Cancer Research Delen used decision trees ANN and support vector machines along with logistic regression to develop prediction models for prostate cancer survivability Kfold showed support vector machines are most accurate predictors Delen used ANN amp decision trees for breast cancer survival 10fold showed decision trees was best predictor Datadriven research Identifying novel patterns Classification analyses Advanced data mining techniques can be used to develop models with a high degree of predictive as well as explanatory power Results complement not replace medical professionals to save more lives 46 Highmark Inc Employs Data Mining to Manage Insurance Costs Data mining tools provide means for analyzing patient data for better care at a lower cost Predicts who will fall sick and who will be most effective and take preventative measures cluster Better management of premiums 47 Coors Improves Beer Flavors with Neural Networks Panel tests take time need a link between chemical composition and sensory analysis Single neural network NeuroDimension Inc A genetic algorithm minimized network error term Analytical inputs to predict avor 48 Predicting Customer Churn A Competition of Different Tools The key to customer retention is predicting which are most at risk of defecting to a competitor and offering the most valuable of them incentives to stay Must develop accurate predictions churn scorecards Overall Gini index and lift in the top decile Salford systems used TreeNet software Decision trees and logistic regression methods best at predicting churn O 0 Data Mining Helps Develop CustomTailored Product Portfolios for Telecommunication Companies Problem existing toolset for argonauten 360 need to be augmented with effective cuttingedge yet exible data mining capabilities Solution need a unified easytouse set of analytical tools with a wide range of modeling capabilities and straightforward deployment options Company chose Statistica Data Miner Results accuracy ofprognoses improved significantly error rate cut in half 5 0V Mining Text for Security and Counterterrorism The Genoa project provides knowledge discovery tools to better quotminequot relevant information sources MITRE provides filters Tool allows an analyst to discover associations This can lead to new knowledge that can be leveraged in the analytical model to make predictions TopCat identifies different topics in a collection of docs and displays the key quotplayersquot for each topic 51 Text Mining for Patent Analysis Patent analysis is the use of analytical techniques to extract valuable knowledge from patent databases Kodak depends on its ability to secure knowledge with patents They analyze other s patents to develop a holistic view of the competitive landscape 53 Mining for Lies Deception detectioncredibility assessment techniques are essential for textbased communication Law enforcement can investigate crimes conduct security screenings monitor communications Human resources can screen applicants Message feature mining relies on elements of data and text mining techniques Neural network models performed the best then decision trees then logistic regression Automated textbased deception detection has the potential to aid those who must try to detect lies in text 5 App Case HPamp Text Mining Problems 0 Extracting useful information from thousands of customer communications such as emails that HP receives 0 Bringing in text data that is not in traditional data 0 Customizing the software package s vocabulary for the words used at HP 0 Combining data from structured databases with unstructured data from text Solution and Benefits 0 Used a Webbased tool that enables HP to augment the OLAP cubes with predictive modeling loyalty scores and customer differentiations 0 Used the results to find insights into HP s customers to improve cross selling targeted marketing customer retention and better anticipation of customer needs 0 Used SAS Text Miner to successfully develop standard data definitions and product classification models with great accuracy CHAPTER 2 DATA WAREHOUSING Objectives After completing this chapter you should know 1 the de nition of data warehouse A physical repository where relational data are speci cally organized to provide enterprise wide cleansed data in a standardized format 2 the four major characteristics of data warehousing and what each means Subjectoriented 7 data are organized by topics such as sales products customers etc Best for providing a more comprehensive view of the organization not only how a business is operating but why Integrated 7 Data from different sources are stored in a consistent fonnat Also clarity is obtained in unit of measures naming labeling of attributes etc The assumption is the data warehouse is totally integrated timevariant 7 provides data at various points in time daily weekly monthly quarterly annually 7hjstoric and current data so as to analyze trends deviations and compare and forecast outcomes etc Every data warehouse should have atime variable Example LSU enrollment retention graduation data nonvolatile 7 users cannot change the data once entered into the data warehouse This ensures that the data warehouse is almost exclusively available for access Obsolete can be deleted and changes are recorded as new data 3 additional characteristics of data ware housing designed for webbased usageapplication has relationalmultidimensional structure uses clientserver architecture to provide easy access to enduser for fewer DWs allows for realtime uptotheminute and active data access and analysis contains METADATA which is information that describes your data data about data 4 the three main types of Data Warehouses and their definitions Data Mart 7 is a subset of a data warehouse usually consisting of a single subject area marketing sales customer satisfaction inventory production etc o Dependent 7 created directly from the data warehouse This ensures that the user is usingviewing the same data available to all other users 0 Independent a small warehouse designed for a department or strategic business unit SBU Its source is not an EDW Operational Data Stores ODS 7 a type of database often used as an interim or staging area for a data warehouse especially for customer information les CIF Data are updated frequently throu the course of business operations as opposed to the static contents of a data warehous Enterprise data warehouses EDW 7 a largescale data warehouse that is used across the enterprise company for decision support Being largescaled the EDW integrates data in standard format from many sources DirecTV Enterprise Rental used EDW EDW provides data for many types of Decision Support Systems including CRM SCM BPM BAM PLM revenue management and KMS 5 the definition of metadata and be able to identify examples Data about data describes the contents of a data warehouse its structure such as field name data type default value length meaning syntax and the manner of its use 6 the major components of the data warehouse process and describe those components Data Sources 7 transactional data OLTP such as CRM and ERP data web logs from the internet external data ex census data ACCESS SQL data and other fonnats legacy systems reference to outdated computer systems etc ETL 7 Extraction Transformation Load Process Data are extracted from external data sources using custom ETL software maintained in a staging area where transformed cleansed and integrated then loaded into the Data Warehouse and or data marts Enterprise Database with Metadata 7 are maintained so that it can be used by IT personnel and users includes software programs with rules for organizing data that can be indexed and searched Data Marts optional 7 A departmental data warehouse that stores only relevant data Middleware Tools 7 tools that access the contents of the data warehouse These are the frontend applications that users have to interact with data including data mining queries OLAP predictive analyses reporting and Visualization tools ex MS SQL MS MS Excel with PowerPivot and others 7 describe atwotiered and threetiered architecture and know the advantages and disadvantages of each Twotiered 7 0 Client Workstation Client Tier 7 allows end user to request both the application functions and data content from one server 0 Application Server and DatabaseData Warehouse run on the same server hardware platform 0 Advantage More economical than threetiered o Disadvantage Can have performance problems for large data warehouses using dataintensive applications Threetiered 7 0 Client Workstation Tier 1 7 allows the end user to request application functions ex ACCESS which in turn and requests data content ex ACCESS database les 0 Application Server Tier 2 7 responsible for execution of programs 0 Database Server Tier 3 7 houses the database or data warehouse o Advantage Seperates the application and database functions allowing for greater capacity and performance of the respective servers 0 Disadvantage Increased cost due to more hardware requirements 8 understand the architecture of webbased data warehousing Client Web Browser gtInternet IntranetExtranet gtWeb ServerTWeb Pages gtApplication Server or Data Warehouse Internet Intranet Extrane Client Web browser 9 de ne server application server database server client software Server 7 Computer hardware that provides a speci c service used by other computers Application Server 7 Computer hardware responsible for the efficient execution of procedures programs Database Server 7 Sometimes referred to as the backend holds the database or data warehouse Client Software 7 allows users to request a server s content or function 10 the issues considered when deciding on the architecture to use a Which database management systemDBMS should be used b Will parallel processing andor partitioning be used Parallel paititioning allows multiple CPUs to process multiple queries simultaneously providing stability Partitioning refers to splitting data tables into smaller tables for efficient access c Will data migration tools be used to load the data warehouse d What tools will be used to support data retrieval and analysis 11 the alternative data warehouse architectures and their basic descriptions pg 41 amp 42 in text independent data marts 7 A small data warehouse designed for a strategic business unit or a department data mart bus Kimball Approach 0 Created by Ralph Kimble o Bottomup approach the DW is an aggregate of all data marts across the enterprise the DW is built one data mart at a time 0 Data mart is subject oriented and focuses on the departmental level ie Sales marketing inventory hubandspoke Inmon Approach 0 Created by Bill Inmon father of DW approach 0 Top down approach the enterprise has one data warehouse and data marts are created from the data warehouse centralized enterprise data warehouse federated 12 the ten factors that potentially affect the architecture selection decision Know at a basic level what circumstances warrant choice of architecture Information interdependence between organizational units Upper management s information needs Urgency of need for a data warehouse Nature of enduser tasks Constraints on resources Strategic view ofthe data warehouse prior to Implementation 7 Compatibility with existing systems 8 Perceived ability of the inhouse IT staff 9 Technical Issues 10 Socialpolitical factors 13 that according to Ariyachandra and Watson 2006 Data Mart Bus HubandSpoke and Centralized EDW architectures obtain similar acceptable ratings while independent data marts were believed to be a poor solution followed the federated architecture JIILUJNH ON 14 that Teradata Corporation supports the central data warehouse architectureEnterprise Video Single vesion of the truth 15 that data integration requires three major processes data access data federation change capture and what each means Data Access 7 Refers to the ability to access and extract data from any data source Data Federation exists when one application one user interface is able to treat multiple data stores sources as one entity Change Capture 7 the process of capturing changes made at the data source and applying them throughout the entire enterprise this ensures data synchronicity one version of the truth 16 the extraction transformation and load ETL process Extraction 7reading data from one or more databases These data may come from OLTP databases spreadsheets MS Access Transformation 7 converting data from their original form to Whatever form the DW needs This step often also includes cleansing of the data to remove as many errors as possible splitting up fields ex City amp State recoding creating new vaiiables with formulae eliminating duplicates Transformation rulesprocedures can be saved as metadata Loading 7 putting the transformed data into the DW 17 what the company must do to ensure a successful data warehouse implementation process page 60 in text a de ne the plan business objectives and strategies b gather support from managers and endusers c set reasonable time frames and budgets d manage expectations 18 the criteria for selecting a data warehouse vendor 1 Financial strength 2 How well it links to ERP systems ERP Enterprise Resource Planning which is a computer system used to manage internal and external resources including tangible assets fmancials materials and human resources 3 Quali ed consultants 4 Market Share 5 Industry Experience 6 Established Partnerships 7 Product Functionality Most Obvious 8 Customer References 19 the Inmon model the Kimball model and that both ultimately result in an enterprise data warehouse 20 additional data warehouse development considerations outsourcing and securityprivacy concems Companies can completely outsorce all DW efforst and hire a company that provides a hosted data warehouse That company develops and maintains the DW There are however security and privacy concerns 21 the star schema consists of a central FACT table surrounded by several DIMENSION tables definition description and examples of both the fact table and the dimensional table Within a DW data tables are connected in various ways The most important design is called the STAR SCHEMA The design is based upon a concept referred to as dimensional modeling Dimensional modeling is a retrievalbased system that allows for querying data tables usually in terms of location time period and other classi cations like type of product etc The star schema consists of a central FACT table surrounded by several DIMENSION tables 22 The FACT CENTER table is your data the business facts KPIs key performance indicators to be analyzed ex sales It contains foreign keys FK to be linked to the dimension tables The DIMENSION GIVE MORE INFORMATION tables contain classi cation information used to de ne how your facts data are to be aggregated or summarized for example by location time period store type of product etc Dimension tables have onetomany relationships With rows in the fact table In querying the dimensions are used to sliceanddice the KPIs in the fact table to address ashoc information needs ex Just look at Alcoholrelated crashes by parish and day of week Examples DlmDate ELI ng w 11 Date Stove Numbar Day Staksj mwnca DavnfWeek WW Month Monlh ame 7 ostend Quartev StoreJd co QuarteLName ondurlld oo Veal Llnitsjuld 339 DimProduct w m EAN0Ide PrumutJ tame ProduLLCalequy Star Schema Dimension I I Dimension TIME Fad Table SALES I I Dimension GOGRAPHY I I Dimension PEOPLE What star schema model is needed to generating a powerpivot output see example in notes of Crash data summarized by Parish Day of the Week and Alcohol Usage pmsmcmehxe meensmnnhwey meensmmhwey mums amonwx 1 z 3 as s 57 PAR SHED1 23MEA an M Wm 23 Know basic OLAP Operahohs Sheee cone 4 InvolvemsmY39ES It is usually a twordAmensxonal representation of a 3edhhehsmha1 cube ohe demehsmh DseeeA shee on more Lhantwo dimensions same as shemg he shee 01a shee 1 summanzed up to he most detaded down h y ant e chmgmg the 64mm 24 the de hmh ofgranulanty Fefersto thehghe levelofdeta Example sales by hegmh vs sales by smews sales by store vs ales hy 510ml onentauon ofa cube x e rotating a cube depanment vs sales by salssxep bold on pages 6162 Stamng wnh Lh wr Semhg expectations that vou Engagmg m pohheany nane b g the datawarehouss wnh mfnrmahon just because u is avalabls ong sponsorship ehshh he mee amh loadm 26 o Focusing on traditional intemal recordoriented data and ignoring the value of external data and of text images and perhaps sound and video 0 Delivering data with overlapping and confusing de nitions 0 Believing promises of performance capacity and scalability o Believing that your problems are over when the data warehouse is up and running 0 Focusing 011 ad hoc data mining and periodic reporting instead of alerts the de nition of scalability The ability to accommodate increases in activityvolume without major changes to the process 27 ef eciency the description of realtime active data warehousing and the evolutionary process of the increased DW requirements 0 For many businesses their competitive edge is found by making fast and consistent decisions before their competition steps up 0 With the advent of RDW in 2003 managers are now focused on making decision RDW Real Time DataWarehouse the process of loading and providing data by way of a data warehouse as they become available data used for decision making data are updated on an ongoing basis as business transactions occur 0 RDW is also known as an ADW o RDW is very useful and becoming a necessity for those who interact directly with customers and Evolutionary Process 28 suppliers here the need exists for data at their ngertips At a very basic level the DW reports what ha pened in terms of reports Ex Daily sales reports At the next level the i something happened ex Sales dropped because of defective billiards tables As business needs increase decisions may be made to collect more data that allow for predicting WHAT WILL HAPPEN Predicting what will happen aids in putting preventative measures in place because ordinarily the company does not have the means to detect issues in realtime Technology and systems are put in place to allow for answering what IS happening NOW ex If DirecTV knows that their customer dropped service 3 hours ago you don t need the capability of predicting dropped service Knowing what IS happening NOW allows for companies to react and make events HAPPEN NOW de nition and skills of the data warehouse administrator DWA Read page 70 Data Warehouse Administrator DWA 7 A person responsible for the administration and management of a datawarehouse The DW must be maintained by a DWA Data Warehouse Administrator having skills that surpass 1 2 3 4 29 traditional database administrator DBA Besides excellent technical skills the DWA should be Familiarity with highperformance hardware software and networking technologies since a data warehouse is based on those Solid business insight to understand the purpose of the DW and its business justi cation Familiarity with business decision making processed to understand 110w the DW will be used Excellent communication skills to communicate with the rest of the organization the four main areas to consider when ensuring effective security of a data warehouse 1 Establishing effective corporate and security policies and procedures 2 Implementing logical security procedures and techniques to restrict access including user authentication access controls and encryption 3 limiting physical access to the data center environment 4 establishing an effective internal control review process for security and privacy 30 Major points of the following case studies a DirecTV Opening Vignette page 30 Problem The company s IT system could not handle the high data volume from customer calls along with the rapidly changing market conditions Solution Developed a realtime integrated active DW solution from Teradata and GoldenGate Results Huge business bene ts such as reduced chum rate and better managed call centers with real time data for decision making b Premier Bank Card Video is posted on Moodle and details are in the notes Problem The old system 0 Did not keep up with the user demand 0 Consisted of access and Excel 0 Manual process of analyses and trending 0 Time was long 0 Error rate was high 0 Low accuracy Solution 1 B1 is critical to this company because they are focused on data and analysis of data 2 The users now have instant response and they can pull data down out of the cubes and data warehouse 3 Everybody in the company was accustomed to Excel and this is a common user interface 4 The BI solution gives them one version of the truth delivering the right information to the right people Results c Enterprise Rental Video DW is built by Teradata Corp 635 minutes 7 Posted on Moodle Problem Wanted to grow Not to have data but to have wrong data One version of the truth Had disparate databases didn t come together Solution ARMS to bring information together Results Competitive Advantage with other rental companies TV Brand Report Enterprise National Alamo Increase query volume d Enterprise Data Warehouse Delivers Cost Savings and Process Efficiencies for NCR Case 21 pages 3435 Problem NCR faced with the challenge to grow into an integrated solutionoriented business structure with a global focus Solution Teradata EDW system Results EDW drove huge organizational and process changes Reduced cost improved process efficiency and used to drive growthfocused goals for the global organization Benefits of EDW are both qualitative and quantitative The cost savings far exceeded the expense of implementing the system e First American Corporation Case 22 pages 3637 Problem Change strategy from traditional banking to one that was centered around CRM Solution Did datawarehouse about customer behavior profitable products used buying preferences and client behavior positions Results Access to info through DW enabled change and improvement f BP Lubricants Achieves BIGS Success Case 23 page 46 Problem Recent merger activity Improve BI Need to integrate data Solution EDW Marketing Sales Finance Results Improved visibility of consistent timely data g Hokuriku CocaCola Bottling Company Case 24 page 50 Problem The existing IT system was not able to meet the challenges of competitive pressures and consumer demand Solution The Teradata BI solution collects not only historical data but also nearreal time data that is then transmitted via wireless connection to head quarters for operational decision making Results 1 Cost savings and higher revenue 2 Enabled the bottler to have the right products in each vending machine at the right time to meet customer preferences 3 The number of vending machines that could be served per salesperson increased by as much as 42 4 Other benefitsmany of them overall goals for company growthhave included fresher products better customer service increased brand awareness and higher margins due to a greater product tumover and increased sale The BI system grew sales with the least amount of operational costs thus raising sales and performance levels h HP Consolidates Hundreds of Data Malts into a Single EDW Case 25 page 53 Problem Too many data marts very expensive to design and maintain Did not have enterprise view of the data Solution Consolidating data marts into an EDW Results Better BI Solution i A Large Insurance Company Integrates Its Enterprise Data with Axis Case 26 pages 6364 Problem XYZ company had no centralized view of enterprise performance and basic financial infonnation to support management with analysis and insight Solution Developed an integrated solution using bestofbreed approach BenefitsResults Standardized info assets amp the technology base Consolidated transactional amp reporting activities Freed people amp resources for more strategic amp high value contributions Single version of the truth Most important more agile 7 timely data and react accurately amp quickly j Egg Plc Fries the Competition in Near Real Time Case 27 page 66 Problem Latency Problems Solution DW and data mining in near ireal itime data access to internal users and customers Results System enables faster decision making about speci c customers and customer classes k Continental Airlines End of Chapter Case page 77 Worst gtFirst First gtFavorite Problem Bad Reputation Solution Go Forward Plan Results Increase revenue helped marketing Better decision making improved data center management 31 Key ACCESS Terms see handout l 7 49 terms 1 Microsoft Access 2010 is things related to other things used to store data and convert it into relationships Database software is used primarily for decision making by businesses that compile data from multiple records stored in tables to produce informative reports Download AdventureWorks off Moodle 2 The power of arelational database lies in the software s abilit to organize data and combine items in different ways to obtain describe 3 That power is only realized when the data are delivered to the decision maker in the appropriate form 4 Ifyou have the ability to control data and turn it into useful information you possess a marketable skill Key Terms and Concepts 1 Database 7 A le that consists of one or more tables and the supporting objects used to get data into and out of the elds 2 Relational database software 7 a computer application such as Microsoft Access that is used to store data and convert it into information 3 39 quot 39 database system 7 Data are grouped into similar collections called tables and the relationships between tables are formed by using a common eld 4 Flat or non nti M data 7 data 39 J in a single page or sheet 5 Field 7 a basic entity data element or category such as a book title or telephone number Column 6 Column 7the data eld that you assign to group data vertically into columns Example First name last name address and phone number Field 7 Record 7a complete set of all of the data about one person place event or idea Example All the fields on my class roster about one student Row 8 Row 7 the source eld that you assign to group data horizontally in a crosstab query Record 9 Table 7a collection of records 10 Primarv kev 7the field that makes each record in a table unique ex Student ID number 11 Foreign key 7a eld in one table that also is stored in a different table as a primary key Example The StudentID primary key in the Student table is joined to the EmployeeID foreign key in the Employee table in the case of a student worker on campus 12 5 Query 7a database object that enables youto ask questions about the data stored in a database and returns the answers in the order from the records that match your instructions Question 13 m 7 an interface that enables you to enter or modify record data Fill In the Blanks 14 Re ort 7 a rinted document that dis la s information rofessionally form a database a Avoid damaging data by copying data and renaming the le b Access speed 4he amount of time it takes for the storage device to make the le content available for use c drive because those drives have suf cient access s eed to sup ort the software ACCESS is a memory hog a Backing up databases on a regular basis is critical because the data is the lifeblood of an organization b To back up an Access le create a duplicate copy of the database O F D All databases have a tendency to expand with use This expansion will occur whenever the database is being used such as when new information is added the database is being viewed queries are created or run or lters are applied and removed Because the database les tend to rather large to start with any growth creates problems Compact and Repair Database Utility 7 Reduces the size of the database and eliminates wasted space i Acts like a disk defragmenter utility It nds related le sectors and reassernbles them in one location if they become scattered from database use 19 Datasheet view 7 a grid containing columns and rows where you add edit and delete records in a database table 20 Design view displays the infrastructure of a table fonn or report without displaying the data 21 Onetomany relationship xists when each record in the rst table may match one more than one or no records in the second table Because things are related 23 Cascade delete aearches the database and deletes all of the related records ex Delete a customer and all his orders will be deleted too 24 Cascade update 7connects a primary key change to the tables in which it is a foreign key 25 Indexed property 7 is a list that relates the eld values to the records that contain the eld value a b Like an index in a book using an index to nd a record in a database reduces retrieval time All primary keys must be indexed 26 Data redundancv occurs when unnecessary duplicate information exists in a database Typically want to reduce this SQL Queries in Access 27 Data types and usage example of each a b D a o quot1 F90 Text 7 stores alphanumeric data such as a student s name or address Example City Number 7contains a value that can be used in a calculation such as the number of credits a student has earned Example Height Autonumber 7 is a special data type that Access used to assign the next consecutive number each time you add a record Example customer account number DateTime 7 hold formatted dates or times and allows the values to be used in date or time arithmetic Example Library databases that store research papers Currech 7 can be used in a calculation and is used for elds that contain monetary values Example Your checking account balance YesNo 7 assumes one oftwo values such as Yes or No True or False or On or O Example Dean s list vaerlink7 stores a Web address URL Example wwwlsuedu M 7contains an object created by another application Example Excel workbook Attachment 7 data type that allows you to attach images spreadsheet les charts and other types of supported les to the records in your database 28 Date arithmetic 7a mathematical expression that calculates elapsed time Ex Add one to Today to get tomorrow 29 Filter 7 condition that helps you nd a subset of data meeting your speci cations Ex Filter by crash data by parish code 30 Sort ascending or descending 7 ascending provides an alphabetical list oftext data or a smalltolarge list of numeric data Descending arranges the records with the highest value listed rst 31 Which is rst The Sort or Filter These operations can be done in either order 32 Filter bv form 7 permits selecting the criteria from a dropdown list or applying multiple criterion Filter by selection 7selects only the records that match the preselected criteria Ineguity 7 examines a mathematical relationship such as gtltgtlt LAD bu 35 PivotChart view 7 displays a chart of the associated PivotTable view 36 PivotTable view 7 provides a convenient way to summarize and organize data about groups of records 37 Query design gpid 7 displays when you select a query s Design View it divides the window into two parts 38 Query Wizard 7 a tool that facilitates new query development through a series of dialog boxes 39 Select Query 7 searches the underlying tables to retrieve the data that satisfy the query parameters Calculated eld 7 a eld that derives its value from a formula that references one or more existing elds 4 o 42 Data demogpaphic 7 are data describing population segments by age race education and other variables 43 PivotTable Elements a Drop zon 7 an area in the PivotTable or PivotChart design grid where drop elds to organize the data Row eld Row drop zone17 the source eld that you assign to group data horizontally into rows Column eld Column drop zone17the data eld that you assign to group data vertically into columns 0 0 d Detail or totals eld 7 the data eld that contains individual values to be summarized e Drill button 7 the plus or minus sign that enables you to show or collapse details in a PivotTable f Filter eld Filter drop zone 7 the data eld that you use to create criteria to filter data in a PivotTable g Crossfooting 7the sum of a total row compared to the sum of a column total to verify that the two totals match mamma a state Counts the eamed an in ISDS 2001 Retums increase Returns 7 the mean obtained by adding bedroom house in Tulsa several quantities together and dividing the sum by the number of quantities It ignores null values Standard Deviation Ifthe average percent salary increase were 3 and the standard deviation were 5 these pay increases di er more than if the average were 3 and the standard deviation were 2 The range is more varied in the sample distribution Measures how widely spread the values are from the average when the data describe an entire population Variance Population Spread or dispersion of salaries for employees in similar positions in an organization Measures the square of the population standard deviation KNOW DIFFERENCE 45 Discrete data 7 classi cation of data that is measured and quanti ed in discrete unique increments categories ex the number of males and females in a class names of countries etc 46 Continuous data 7 classi cation of data in which values fall 011 a continuum and are easily scaled Ex Measurements of time temperature money volume size and distance 47 Chart a Fquot O P Families Know their use and an example Column Charts available in 2D and 3D i Column bar and stack 7 charts that display quantitative data and compare data across categories ii Example Number of volunteers per city Line Charts a chart that plots data points which are continuously distributed data to compare trends over time Place time measurements on the X axis i Line in 2D and 3D Area in 2D and 3D and XY Scatter ii Example Plot volunteer hours donated by month Display sales data by quarter Pie Ch 1 arts Pie chart 7 a chart that shows proportion of each category to the whole for a single data series Doughnut chart 7 a chart that shows values as percentages to the whole for multiple data series iii Example Shows proportion of last week s revenue generated by product line Bubble Charts 7 a variation of a scatter chart to show how sets of things compare according to various factors The sizes ofthe bubbles show the relative size of the data being plotted i Example Salaries by job title with the size of the bubble representing the percentage of the total salary budget TEST QUESTION T 48 Pivot a b 0 Chart Elements Legend 7 area of the chart that identi es which color represents the data for each data series 7 the vertical and horizontal scales displaying plotted data in a line column bar or scatter chart Gridlines 7the lines that extend across the plot area of a chart d Chart title 7 the area of a chart that displays a name describing the data depicted in a chart e Plot area 7 area of the chart that displays the data points 49 Calculations in a PivotTable a Calculated detail eld 7 the portion of a PivotTable or PivotCharl that generates a new eld and performs the stipulated calculation on all of the detail records b Calculated total eld 7 a data eld used to customize aggregated data Smallest element of a database is information in a eld Field gt Record gt TABLE rDATABASE Macro records all steps to make something happen Kaylie Milton 12712 Study guide for ISDS 2001 1 Business intelligence Bl an i V 7 y predictive views of business operations for purposes lam New W r s m m39i me i r w gt2 Blenables users to manipulate these data and conduct analysis ng managerial a reporting systems which provided b providingforexpanded computerized The capabilities included c to the EIS capabilities and appeared under the name BI the term the world s leading information technology research and advisory company d included artificial intelligence and continue to 39i i The will onseSupport model THREE MAJOR COMPONENTS b mum taken by organizations 6 The in today s business environment that facilitate change Strategy gap strategy 8 organization zquot W i r 43 I 1 quotr and goals 9 THE MAJOR OBJECTIVE 0F BI AND COMPUTER DECISION SUPPORT TO FACILITATE A STRA rljfwl r AS DESCRIBED IN THE MISSION STATEMENTS OBJECTIVES AND H O I I D h a 2 f 5quot n H s m 1 O 1 E u w E 2 5 no N 23 E N E VI A 5 Q o e h 0 5 O E 3 or ository of historical and current data m throughout an organization DW is U 9 Fl 9 m o III quotE m 0 gm 395 r n r I I created by the technical staff a Some today have current data so they v N Business Analytics a broad category of ap Middleware UJ a Excel or sales figures The two mafor categories at BA tools and techniques are 4 Equot H etitive managerial problem usually in one industry Some examples are 139 r 39 quot 39 39 39 39 16 Data mining a set of data analyses and statistical methodologies that r r n which can be aI l39 r v I n 1w 22 i I mm by theirability to scan big data to discover meaningful new correlations pattern trends Describe Business Performance Management BPM uses balance scorecard methodology to N of an organization i 39 392 Eliiii i 2 other information broadcasting and m 19 Dashboard provide a comprehensive at 39I dashboards y ni39 w mm s m trends operations These quot a 39u systems are v OLAP online analytical processing systems A for analysis and decision OLAP is an approach to quickly answer mutidimensiona anaytica questions The output where the m 23 Examples of Data produced by 0LTP 24 Define strategic imperative if necessary a 30 When planning BI initiatives identify what a company must take into account to ensure appropriate planning a Need framework for planning BI initiatives b Gartner group 2004 defines four component of the planning and execution of BI initiatives i Business ii Organization iii Functionality iv Infrastructure c Should include integration of several Bi projects across the organization and with business partners idea sharing d There should be constant oversight and accountability You should start BI and est a BICC within the company if you have 39 Strategies and objectives are properly aligned with the reasons for DW and Bi IT department can implement hardware and software capable of implementing the objectives iquot User community is aware of initiatives and are motivated for operationalizing initiatives 31 BICC Functions Business intelligence competency center a T how BI is clearly linked to strategy and execution of strategy b Encourage interaction between the potential business user communities and the IS organization c Repository and disseminator of best BI practices between and among the different lines of business d Standards of quot in Bi practices can be 39 Iand U C the company e The IS organization can learn a great deal through interaction with the user communities such as knowledge about the variety of types of analytical tools that are needed f The business user community and IS organization can better understand why the daj warehouse platform must be flexible enough to provide for changing business requirements g It can help important stakeholders like high level executives see how BI can plan an important role 32 Real Time BI emerged because of the need of instant access to data a Some examples include 39 Chargedebit and transaction is denied When you buy books from the bookstore they scan your book If the inventory has fell below a certain level an automatic order will be placed iquot Equifax uses a customer relationship management CRM application that notifies customers by email if the balance on any credit card exceeds a certain balance iv DirecTV the save team contacts customers within 3 hours of canceling their service and offers incentives to win them back 33 Four other issues to consider when implementing BI a Develop or Acquire BI systems buy lease build Justify and prioritize by way of CostBenefit Analysis know benefits Security and Privacy of Employees Needs to be protected 9957 Integration of systems and apps Case Studies 1 Applebee s a Problem Reports were static and could not merry traffic with sales b Solution TeraData c Benefits Managerial decisions and teams improved 2 DirecTV a Problem Reduce Churn rate high volume of calls Needed a better response Disparate data mart 57 Solution Save Team contact customers who wanted to cancel within 3 hours to try to convince them to stay Centrally managed BW updated in real time c Results 25 customers kept service 3 Norfolk Southern a Problem Needed to get there on time deregulation b Solution TeraData TOPS AccessNS allowed customers from all over to access current data human resource apps enterprise data warehouse GPS info c Benefits customer satisfaction dev Service office location data ext customers 4 Hoyt Highland Partners urgent care clinic cluster analysis a Problem increase competition b Solution Acxiom s PersonicX geospatial analysis consumer behavior consumer demographics c Proximity is the top factor in customers choosing their urgent clinic 5 Alltel Wireless predictive analytics tools a Problem need centralized data focus solution increase new customers and keep existing customers know who they were so they could offer incentives b Solution Axium personaX segmentation system predictive analytic tools c Benefit automating its customers lifecycle management with Acxiom s Bi software suit 6 Giant Food Stores a Problem old pricing and promotion lab labor intensive rules on paper and in peoples heads could not keep up with the pricing decisions required in the fastpaced grocery market Limited companys ability to execute more sophisticated pricing strategies b Solution ADS demand tech employed pricing system c Results rules based pricing large based chaning d Benefit predict demand based upon price level agile in pricing productivity 7 Next Net a The ubiquity of cell phones GPS devices and wireless personal digital assistants PDAs is resulting in the creation of massive new databases b REALITY MINING is a new breed of data mining to analyze these new databases and create a much better and deeper understanding of customers behaviors and movements 8 Vodafone a Squot 0 Problem Market share and total number of customers stagnated new competitors cost increased to comply with government regulation Solution i Enterprise Data warehouse ii BI tools models insights iii Marketing offer Results good view of whole enterprise analysis can spend more time on decision making and less on managing data Vodaphone implemented a BI solution which automatically initiates a marketing of fer based upon a customer s recent activity This is called trigger based Test 2 case studies Online Video Enterprise Car Rental ProblemWanted a system that could grow with the companyTook 12 phone calls to complete a rentalSometimes had wrong data Had multiple data marts that came up with different solutions that at times wouldn t work together making decision making harder SolutionBuilt ARMS automated rental management system that electronically communicated with insurance company and body shopGathered info on avg length of rental by adjuster and repair shop Became a data provider and gave them a competitive advantage Used the repair process as a measure of satisfaction Used Teradata to make applications like rental calculator auto extend and further solutions Used very current information to take care of customer used Teradata to save them on cost Brought the report time from days to hours to make intelligent business decisions faster Teradata allowed them to increase their queries from 10000 per day to 72000 More people are accessing it which lets them capture more of the market BenefitsGot reports a lot faster which allowed them to have a competitive advantage Chapter 2 Isle of Capri Casinos 0 Problem Company success relies on its relationship with its customers its ability to create a gaming entertainment and hospitality atmosphere that anticipates customers needs and exceeds their expectations Needed a data and technology architecture that enables Isle to constantly deepen its understanding of customer and efficiently meet their needs 5 monthly reports from properties but they took a week to or more to produce Solution Teradata as core solution and key partner along with IBM Cognos for Business Intelligence Brought on a management team that could enable key decision makers throughout the operation to easily frame their own initial queries and follow up questions Results Enabled its management and employees to further deepen understanding of customer behaviors by connecting data from hotel systems and data from its customer tracking systems TELCOS 21 0 Challenges Customer Retention Cost reduction customer acquisition 0 Solution O O 0 Customer Retention tracebility project began with 10 dashboards in 2009 and has since realized 24 million in cost savings shortened customer provisioning times and reduced customer defections by 30 Cost Reduction Bouygues Telecom made I Aladin Teradata based marketing operations management system Delivered more than a million in savings in a single year Customer Acquisition PakistansMobilink used its sales information model to help distribution team plan sales tactics on smarter data driven strategies that kept its suppliers fully stocked Data warehousing helps MultiCare 22 I Challenges Wanted to reduce Septicema mortality rate I Solutions Implemented an adaptive data warehouse A health care speci c data model and subsequent clinical and process improvement services to measure and effect care through organizational and process improvements 0 0 BP Lubricants 2 3 Clinical data to drive improvement compiled data from multiple data sources across continuum of care Systemwide critical care collaborative permanent integrated teams of clinicians technologists analysts and quality personnel that addressed 3 bodies of work I Standard of care I Early warning system I Efficient delivery I Challenges Wanted to improve consistency transparency and accessibility of management information and business intelligence I Solution Business Intelligence and Global Standards BIGS with KALIDO adaptive EDW at its heart KALIDO integrates and stores information from multiple source systems to provide consolidated views for O O 0 Marketing Sales Finance I Benefits 0 00000 Improved consistency and transparency Accommodation of both global and local standards Fast cost effective and exible implementation cycle Minimal disruption of existing services Identification of data quality issues Improved ability to respond intelligently to new business opportunities Coke s Data warehouse 24 Collects historical and near real time data from vending machines via wireless connection It could report when a popular item sells out if a customer was short changed machine malfunction Lead to the ability to accurately forecast demand and total sales increase 10 percent Also due to more accurate machine servicing overtime and other costs were reduced because salespersons were able to service up to 42 more machines Starwood Hotels amp Resorts 25 Challenge Increases in number of hotels over recent years led to the need for business critical information about its hotels and customers They had a legacy system but due to more and more information needed to expand Solutions Selected Oracle Exadata Database allows Starwood Hotels to Extract Transform and Load operations from operational reports Could generate reports in 4 to 6 hours instead of the previous 18 hours Provides hotel managers and corporate executives with near real time information to make optimal business decisions Michigan State Agencies 26 Chapter 3 Through customer service resource optimization and the innovative use of IT the Michigan Department of Technology Management and Budget DTMB and its 10000 users from 5 major dept and 20 agencies and 100 bureaus allowed them to do their jobs more effectively 0 Their EDW saves nearly a million dollars per business day in financial benefits Michigan has been ambitious in its attempts to solve real life problems with innovative sharing and comprehensive data analysis Michigan has leveraged large amounts of data to create innovative approaches to the use of the DW delivering efficient reliable enterprise solutions using multiple data channels SelfService for Travel and Transport Challenge The ability to effectively communicate a value proposition to existing and potential customers is key to winning and retaining customers Travel arrangements often made on an ad hoc basis special immediate purpose made it difficult to analyze costs or instate optimal purchase agreements Solutions Travel and Transport implemented Information Builders WebFOCUS business intelligence platform Dashboard driven expense management application lead to companies ability to better plan track analyze and budget their travel expenses and more effectively benchmark them against other companies Saves company time from the manual reporting used before Delta Lloyd Group 31 FEMA 32 Tableau saves Challenge Needed to generate reports in annual and half year reports for the NYSE and to meet IFRS international financial reporting standards Accounting team put data in and had to sift out errors manually which was very time consuming Solution Used IBM Cognos to create Financial Statement Reporting FSR The software was completed in just 6 weeks and was trialed immediately Results Due to ability to work on documents simultaneously it reduced overtime and saved money Challenges When there was disaster there would be ood of water that covered the Earth followed by a ood ofpaper at FEMA Solution From Computer Sciences Corporation they put the paper reports on an intranet and selected the information they wanted to see and got an onscreen report Used WebFOCUSto complete solution Blastrac 33 Challenges Company did not have consistent reporting method in place and consequently preparation of reports for various parts of company was tedious Spreadsheets were often inaccurate and hard to understand and useless to sales team Needed affordable solution that could deploy quickly without disrupting current systems Solutions Used Tableau software as a visual data analysis solution that allowed company s analysts to quickly and intuitively create visually compelling reports Cut down on time required to create reports which saved money TIBCO and cancer vaccine trials 36 Challenge Great inconsistency and redundancy inherent in their previous data registry They needed to have an indepth analytic capability and an overview were required simultaneously Solution Used TIBCOspotfire computational and visual analysis tool for data exploration and discovery I Results Summarization and visualization of their data represents costeffective means of making informed decisions about future cancer vaccine clinical trials Saudi Telecom 36 I Problem STC needed to identify relevant metrics properly visualize them and provide them to the right people often with time sensitive information Solution Selected Dundas because of its rich visualization alternatives Dundas refined their dashboards to function properly which lead to them doing an enterprise wide project to transform their data to create more proactive monitoring environment Benefits Decreased the amount of service tickets by 55 using visualization tools and dashboards Customer satisfaction levels increased lead to increased customer base lead to increased revenues Used custom KPIs IBM Cognos helps Mace I Challenge The spreadsheet on which they based their international reports had 40 tabs and hundreds of cross links which made it easy to make mistakes and lack of standardized approach was affecting accuracy and consistency I Solution Used IBM Cognos implementation was quick and simple due to a lot of in house knowledge on Cognos Quickly used information from previous compleX reports and automated them which requires less effort and speeds up process EXpediacom s scorecard I Problem Customer satisfaction is key to their mission strategy and success Needed to be able to track monitor and resolve customer issues Before there was no uniform way of measuring satisfaction of analyzing the drivers of satisfaction or determining impact of satisfaction on company profits I Solution Found the fundamental drivers of satisfaction and put them on its scorecard o Deciding how to measure satisfaction determine which of their 20 databases would be useful in demonstrating satisfaction This became basis for the scorecards and KPIs 0 Setting the right performance targets determine whether KPI targets had short term or long term payoffs 0 Putting data into conteXt tie data to ongoing customer satisfaction projects I Benefits the scorecard provides customer satisfaction group with ability to drill down investigation of info in detail into data to find underlying trends Allowed customer service group to immediately see how well it was doing in terms of KPIs Went on to be used in other business units of the company ISDS Exam 3Case Studies DirecTVI Opening Vignette 0 Problem as their business grew DirecTV faced the challenge of accommodating such a large data volume and rapidly changing market conditions 0 Management wanted reports that could be used for measuring and maintaining customer service attracting new customers and preventing customer churn Also they wanted to reduce the resource load that its current data management system imposed on its CPUs They implemented a data warehouse from which information had to be pulled from every night this process was very long and was straining the system Solution The companies main two goals were to o Sendfresb data to the call center in a very smallperiod oftime less than 15mins o Simpli l Changed data capture to reduce the amount ofmaintenance 0 They decided to implement the GoldenGate integration system 0 Results It allowed measuring the churn rate in real time so they used that data to target specific customers in order to reduce the churn rate They started contacting customers who had just discontinued their service and get them back also they started sales campaigns that could target specific customers for retention and prioritize them for special offers They also received information on customer service calls in order to manage the service and make it more efficient Realtime callcenter reports were used by management to compare daily call volumes Premier Bank Card 0 Problem old system which did not keep up with user demand consisted of access and excel manual process of analyses and trending time was long error rate was high and low accuracy 0 Solution Microsoft BI an endtoend solution focused on data and analysis of data user now has instant response and they can pull data down out of the cubes and data warehouse everybody in the company was accustomed to Excel and this is a common user interface gives them one version of the truth delivering the right information to the right people 0 Bene ts exible and selfservice information is more accurate saves time saves money quotBi Picture of analyzed data helped their bottom line Enterprise Rental Video DW is built by Teradata Corp 0 Problem many calls to complete rentals needed better customer service a lot ofinfo from insurance 0 Solution made it faster single version of the truth all information together Enterprise Data Warehouse Delivers Cost Savings and Process Efficiencies for NCR 0 Problem challenge to grow into an integrated solutionoriented business structure with a global focus 0 Solution Teradata EDW system 0 Results EDW drove huge organizational and process changes reduced cost improved process efficiency and used to drive growthfocused goals for the global organization First American Corporation WORST TO FIRST 0 Problem 60 million lost traditional banking system 0 Solution Transformed from a traditional banking system to one that was centered on CRM by using the VISION data warehouse which stores information about customer behavior products they used buying preferences 0 Results VISION provided the information about profitable and non profitable customers retention strategies lowercost distribution channels strategies to expand customer relationships redesigned information ows First American achieved a revolutionary change moving itselfinto the quotsweet 16 of financial corporation services BP Lubricants Achieves BIGS Success Case 23 page 46 0 Problem improve consistency transparency and accessibility of management information and El 0 Solution came up with Enterprise Data Warehouse very complex 0 Results consistent easier faster more exible reporting improved ability to respond intelligently to new business opportunities Hokuriku CocaCola Bottling Company 0 Problem Competitive pressures and consumer demand coca cola needed to make its vending machines more profitable 0 Solution HCCBD data warehouse and analytical software implemented by Teradata collects nearreal time data from vending machines viewed as a store Put on a wireless network instead of using a modem to gather near0real time point of sales POS data 9 Forecasted demand and identified problems quickly 0 Results Total sales increased by 10 costs reduces by 46 each salesperson was able to service up to 42 more vending machines HewlettPackard Company case 25 0 Problem There was a need for analytical data in the company that led to the creation of many data marts Data silos were very expensive to design and maintain 0 Solution HewlettPackard planned to consolidate its 762 data marts around the world into one single EDW In order to gain a superior sense ofits own business and to determine how best to serve its customers 0 Results in 2006 consolidated the data marts into the new data warehouse all the disparate data marts will ultimately be eliminated A Large Insurance Company Integrates Its Enterprise Data with Axis Case 26 0 Problem insurance company grew and did not have an integrated data management system 0 Solution a developed standardized into assets in EDW AXIS using best breed approach 0 Results Hobandspoke architecture picture Egg Pic Fries the Competition in Near Real Time Case 27 page 66 0 Problem system needed to be updated to address latency issues 0 Solution CDW refreshed in a near real time Results sales and marketing campaigns developed in minutes faster decision making about specific customers and customer classes Continental Airlines Problem In 1995 continental was in deep financial trouble It had filed bankruptcy twice and was going to file the third one This re ected on its performance low percentage of on time departures frequent baggage arrival problem and customers turned away due to overbooking in 2006 was the 5th largest airline in the United States Solution 1994 Gordon Bethune became CEO initiated the Go Forward Plan 4 interrelated parts to be implemented simultaneously in order to improve customers valued performance and better understanding their needs and perceptions 1999 decided to integrate everything into one single data warehouse IT revenue and operational data sources Continental moves realtime data tominute hourly about customers reservations and operations includes revenue customer relationship crew operations and payroll security and fraud ight operations Results Continental became a leader in the realtime BI eliminated7million in fraud reduced costs by 41 million with a 30 million investment in hardware over 6 years continental had reached over 500 million in increased revenues and reduced costs Business Intelligence A Managerial Perspective on Analytics 3rd Edition BUMSWINESS INTELLIGENCE lmlgcri1l Perspective r nnAnnIyIicH Chapter 3 Busmess Reporting 3 39a Visual Analytics and Business Performance Management Learning Objectives Define business reporting and understand its historical evolution Recognize the need for and the power of business reporting Understand the importance of datainformation visualization Learn different types of visualization techniques Appreciate the value that visual analytics brings to BlBA Continued WMQQDMWMM Slide 3 2 Learning Objectives Know the capabilities and limitations of dashboards Understand the nature of business performance management BPM Learn the closedloop BPM methodology Describe the basic elements of balanced scorecards WMONMWMM Slide 3 a Learn Chapter 3 case studies Skip Case 35 Dallas Cowboys p 121 Skip Endof Chapter Case p 142 Learn the problem solution and benefits of the online video on Moodle for Enterprise Car Rental WMONMWMM Slide 3 4 Opening Vignettempp 9698 Self Service Reporting Environment Saves Miilions For Corporate Customers Background Business Challenge Solution Results Answer amp discuss the case questions mmozaummm Slide 3 5 Questions for the Opening Vignette 1 What does Travel and Transport Inc do 2 Describe the complexity and the competitive nature of the business environment in which Travel and Transport Inc functions 3 What were the main business challenges 4 What was the solution How was it implemented 5 Why do you think a multivendor multitool solution was implemented 6 List and comment on three main benefits of the implemented system implemented mmaaouwmm Slide 3 a Business Reporting De nitions and Concepts pp 99100 Report Information 9 Decision then ACTON Report Any communication artifact prepared to convey specific information in a presentable form A report can fulfill many functions To ensure proper departmental functioning To provide information To provide the results of an analysis To persuade others to act To create an organizational memory KMS momazoummm sud 3 1 What is a Business Report p 100 Awritten document that contains information regarding business matters Purpose to improve managerial decisions Source data from inside and outside the organization via the use of ETL Format text tables graphscharts Distribution inprint email portalintranet Data acquisition 9 Information generation 9 Decision making 9 Process management mmeaaummm Stick 3 a Role of Information Business Reporting in Managerial Decision Making p 101 Business Functions Action decision g a g Repositories quotxix Informa39ion reporting Womzcmmumm SM Key to Any Successful Report p 100 Clarity Brevity Completeness Correctness Report types in terms of content and format 1 Informal a single letter or a memo 2 Formal 10100 pages cover executive summary text 3 Short report periodic informative investigative WMORMWMM Slide 3 10 Application Case 31 pp 102103 mun Emuves Ammy and Womanmum SM 3 u Types of Business Reports pp 101103 Metric Management Reports Help manage business performance through outcome oriented metrics SLAs for externals KPIs for internals Can be used as part of Six Sigma andor TQM DashboardType Reports Graphical presentation of several performance indicators in a single page using dialsgauges Balanced ScorecardType Reports Include financial customer business process and learning amp growth indicators momaaaummm Slide 3 u Components of Business Reporting Systems pp1 031 04 Common characteristics OLTP online transaction processing ERP POS SCM RFID Sensors Web Data supply volume variety velocity ETL Data storage data metadata Business logic how transactionsevents are converted into metrics scorecards amp dashboards Publication medium builds the reports and hosts them for users or disseminates them to users Assurance right information to the right people in the right wayformat momazoummm Slldc 3 1 Application Case 32 pp 104105 Flood of Paper Ends at FEMA Questions for Discussion 1 What is FEMA and what does it do 2 What are the main challenges that FEMA faces in delivering its services 3 How did FEMA improve its inefficient reporting practices WMQJOMWMM Slide 3 14 Data and Information Visualization p 105 The use of visual representations to explore make sense of and communicate data Data visualization vs Information visualization Information aggregation summarization and contextualization of data Related to information graphics scientific visualization and statistical graphics Often includes charts graphs illustrations WMQQDMWMM Slide 3 15 Application Case 33 p 106 Tableau Saves Blastrac Thousands of Dollars with Simpli ed Information Shanng Questions for Discussion 1 How did Blastrac achieve significant cost saving in reporting and information shanng 2 What were the challenge the proposed solution and the obtained results mmoaoumm Inc slide 3 16 SKIP A Brief History of Data Visualization p 107 Data visualization can date back to the second century AD Most developments have occurred in the last two and a half centuries Until recently it was not recognized as a discipline Today s most popular visual forms date back a few centuries mmaaaummm Slldc 3 17 The First Pie Chart Created by William Playfair in 1801 p 107 EXPGI B and lmpons m and from DENMARK 8 NORWAY IIIammo William Playfair is widely credited as the inventor of the modern chart having created the first line and pie charts Wm b m W13 ning 13 lama I39m mm m 33m 1127111 in my mama mrL MMIM Wm Womzammm Slidesll Decimation of Napoleon s Army During the 1812 Russian Campaign t 1Moscou y z u r W r 7 ByCharlesJosqph Arguably the most popular multidimensional chart WQNuhrmmm Slldcs 19 SKIP A Brief History of Data Visualization pp 108109 WW I I 39a39s39 g sElal Wing5E I I 20108 and beyond Google Maps has set new standards for data visualization with its intuitive Web mapping software upmaannmmm Slide 3 20 Application Case 34 p 109 TIBCO Spotfire Provides DanaFarber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials Questions for Discussion 1 How did DanaFarber Cancer Institute use TIBCO Spotfire to enhance information reporting and visualization 2 What were the challenges the proposed solution and the obtained results mmoaoumm Inc slide 3 11 Different Types of Charts and GraphS pp 110113 Which one to use Where and when Know definition and examples of each Basic Charts and Specialized Charts and Graphs Graphs I Line Chart Histogram I BarChart Gantt Chart 39 Pie Chart I PBEETraChheigMa Scatter Plot Fauuigiaph p I Bubble Chart I Heat MapTree Map I I I I I I mmoaaummm Slide 3 22 A Gapminder Chart Wealth and Health Chart 399 1 439 Z 039 39 3395 39 Z See gapmindenorg for interesting animated examples tuna 2000 394000 own 000 wouo39 39 I I r v I Int M 7 7 mm am am mu m 1 mm tall men use i we I w 7 mW Womanrmmm Slldcs 23 The Emergence of Data Visualization and Visual Analytics pp 114115 challengers leaders Maoic Quadrant for Business Intellioence mm o QiikTecn and Analvtics Platforms hosts Q 39 lechStrategy V T Source Ga rtner com Losingt g quot5 SAP Many datfa gastritis Visualization a J m mmwm asm 39Sallent Manugemanl Company companies are in the 33 4th quadrant a m l39liClllE players visionaries toward visualization it oilism A5 of February 2013 Mannieramming Slides 2 Skip The Emergence of Data Visualization and sual Analytics pp 114116 Emergence of new companies Tableau Spotfire QIikView Increased focus by the big players MicroStrategy improved Visual Insight SAP launched Visual Intelligence SAS launched Visual Analytics Microsoft bolstered PowerPivot with Power View IBM launched Cognos Insight Oracle acquired Endeca mmaaaummm Slldc 3 25 Visual Analytics p 115116 1 Typical charts graphs and other visual elements used in visualizationbased applications usually involve two dimensions sometimes three and fairly small subsets of data sets 2 In contrast the data in these systems reside in a complex data warehouse 3 Visualization differs from traditional charts and graphs in complexity of data sets and use of multiple dimensions and measures mmeaaummm Slldc 3 5 Visual Analytics p 116 A recently coined term Information visualization predictive analytics Information visualization Descriptive backward focused what happened what is happening Predictive analytics Predictive future focused what will happen why will it happen we want to know There is a stronq move toward visual analytics MMQaaumMamm Slldc 3 21 Visual Analytics by SAS Institute i l Integration Rolebased Views SAS Visual Analytics Architecture Big data In memory Massively parallel processing mm 2014 harm Mien Inc Slide 3 28 3th a At teradatauniversitynetworkcom you can learn more about SAS VA experiment with the tool Performance Dashboards p 119 Performance dashboards are commonly used in BPM software suites and BI platforms Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily drilled in and further explored information density mmaaaummm Slldc 3 so Performance Dashboards p W39 Executive Dashboard H w Lsuvuumj Scmtica Tum n up so to s9nun MONICWMM sums31 Performance Dashboards pp 121122 Dashboard design The fundamental challenge of dashboard design is to display all the required information on a single screen clearly and without distraction in a manner that can be assimilated quickly Three layers of information Monitoring KPls Keep Performance Indicator Analysis root cause of problems Management detailed operational data that identify what actions to take to resolve problem mmaaouwmm Slldc 3 32 Application Case 36 pp 122124 Saudi Telecom Company Excels with Information Visualization Questions for Discussion 1 Why do you think telecommunication companies are among the prime users of information visualization tools 2 How did Saudi Telecom use information visualization 3 What were their challenges the proposed solution and the obtained results MMQNIOWMM Stick 3 a Skip Application Case CALL CENTER DASHBOAR 4quot quot W Dundas Data V suallzalIonMc AGENT mm mu AHnmIns I mas CALLS RATE my mm Va Wevels M 92 I A g 7 12 72 HpvmmnFGm gFr M 39 x 3 39 1 m 25 39 WDAYWEBSIEY Ivfquot o 8 V j 2 29 31 mm meladura M o m A 5 A4 Mr H 3 8 u 5ndpr M v m A V 1 3 vs 74 u Mmummly N 7 w 17 76 am u M n 20 12 74 umycmuns W 1 vs 25 74 Pal In Naru a M 7o 39 7 H 14 7o FHth39IIHrEm lEr M 53 w u as mnvonnegm M 63 4 m 11 an thavd Brawgan M S7 5 IE 57 am Sagan H as 1 m I a hr rm o 62 quot 7 L n IE 67 Dawd snngmy M 6 a 14 6 AVG SPEED or ANSWER um nuwuuu we A MW rm mm mm 7921 WNW y Amuvgdm 3 r Vmarr raum Fi 3 y MM 3 212mm I W9 41 k M u A94 w w uwmm m m mu luv uw m w m m 13 Ian1 mm mm 131 um mu um man we Womzcmmm Performance Dashboards p 124 What to look for in a dashboard Use of visual components to highlight data and exceptions that reguire action Transparent to the user meaning that they require minimal training and are extremely easy to use Combine data from a variety of systems into a single summarized unified view of the business Enable drill down or drillthrough to underlying data sources or reports Present a dynamic realworld view with timely data Require little coding to implement deploy and maintain momenzoummm Slldc 3 35 Best Practices in Dashboard Design pp 124126 Benchmark KPls with Industry Standards Wrap the Metrics with Contextual Metadata lt TEST QUESTION Validate the Design by a Usability Specialist Prioritize and RankAlerts and Exceptions Enrich Dashboard with BusinessUser Comments Present Information in Three Different Levels Pick the Right Visual Constructs Provide for Guided Analytics mmaaoummm Stick 3 an Best Practices in Dashboard Design p 125 Present Information in Three Different Levels 1 Visual dashboard level at a glance what do you see 2 Static report level already been decided just showing report 3 Selfservice cube level When a user navigates the dashboard a simple set of 812 KPls can be presented which would give a sense of what is doing well and what is not mmeaaummm Stick 3 1 Business Performance Management p 126 Business Performance Management BPM is A realtime system that alerts manaoers to potential Opportunities impendinq problems and threats and then empowers them to react throuqh models and collaboration mmazoummm Slide 3 Business Performance Management BPM refers to the business processes methodologies metrics and technologies used by enterprises to measure monitor and manage business performance BPM encompasses three key components A set of integrated closedloop management and analytic processes supported by technology Tools for businesses to define strategic goals and then measuremanage performance against them A core set of processes Methods and tools for monitoring key performance indicators KPls linked to organizational strategy momenzoummm sud 3 39 A ClosedLoop Process to Optimize Business Performance p 127 m Process Steps 1 Strategize 2 Plan 3 Monitoranalyze 4 Actadjust Each with its own process steps Max41me snagsco irategize Where Do We Want to Go 0 127 This is the process of a identifying and stating the organization39s mission vision and objectives and b developing plans at different levels of granularity strategic tactical and operational to achieve these objectives mmaaaummm slide 3 41 Strategize Where Do We Want to Go p 127 Strategic planning Common tasks for the strategic planning process 1 Conduct a current situation analysis 2 Determine the planning horizon Conduct an environment scan Identify critical success factors Complete a gap analysis Create a strategic vision Develop a business strategy Identify strategic objectives and goals WMQRMWMM Stu3 41 NQQ PP Plan How Do We Get There pp 127128 Operational planninq Operational plan plan that translates an organization s strategic objectives and goals into a set of welldefined tactics and initiatives resources requirements and expected results for some future time period usually a year Operational planning can be Tacticcentric ooerationallv focused Budgetcentric plan financiallv focused WWQNMWMM sud3 43 Plan How Do We Get There pp 127128 I When operational managers know and understand the what ie the organizational objectives and goals strategy plan they will be able to come up with the how ie detailed operational and financial plans strategy plan Operational and financial plans answer two questions What tactics and initiatives will be pursued to meet the performance targets established by the strategic plan What are the expected financial results of executing the tactics Operational plan momaaaummm Slide 3 44 MonitorAnalge How Are We Doing p 128 1 When the operational and financial plans are undenNay it is imperative that the performance of the organization be monitored 2 A comprehensive framework for monitoring performance should address two key issues what to monitor and how to monitor mmaaoummm Stick 3 45 MonitorAnalyze How Are We Doing p 128 A comprehensive framework for monitoring performance should address two key issues Particular What to monitor KPls Critical success factors Stratedic Goals and tardets How to monitor mmaaaummm Slldc 3 46 Act and Adiust Err W 1 What do we need to do differently 2 Whether a company is interested in growing its business or simply improving its operations virtually all strategies depend on new projects creating new products entering new markets acquiring new customers or businesses or streamlininq some processes The nal part of this loop is taking action and adjusting current actions based on analysis of problems and opportunities upWQQDMWMM Slide 3 47 9 Act and Adjust What Do We Need to Do Differently Success or mere survival depends on new Droiects creatinq new products enterinq new markets acquirinq new customers or businessesx streamlininq some process Many new projects and ventures fail momenzoummm Slldc 3 43 Application Case 37 pp 129130 IBM Cognos Express Helps Mace for Faster and Better FINANCIAL Business Reporting Questions for Discussion 1 What was the reporting challenge Mace was facing Excel spreadsheets Do you think this is an unusual challenge specific to Mace No 2 What was the approach for a potential solution Automated drill down detailed 3 What were the results obtained in the short term and what weFe the futuFe plens mmeaaummm Stick 3 49 Performance Measurement p 130 Performance measurement system A system that assists managers in tracking the implementations of business strategy by comparing actual results against strategic goals and obiectives Comprises systematic comparative methods that indicate progress or lack thereof against goals TEST QUESTION mmaaaummm Slide 3 so Performance Measurement p 130 Popular business performance measurement systems Balanced scorecards Dashboards and scorecards look at it and you know Six Sigma DMAIC mmeaoumm In sm 3 51 KPls and Operational Metrics pp 130131 Key performance indicator KPI A KPI represents a strategic objective and metrics that measure performance against a goal Distinguishing features of Strategy Encodings Targets Time frames Ranges Benchmarks mmeaaummm Slldc 3 52 Distinguishing features of KPls p 131 1 Strategy KPIs embody a strategic objective 2 Targets KPIs measure performance against specific targets Targets are defined in strategy planning or budgeting sessions and can take different forms WW 3 Ranges Targets have performance ranges eg above on or below target mmaaaummm Stick 3 s Distinguishing features of KPls p 131 4 Encodings Ranges are encoded in software enabling the visual display of performance eg green yellow red Encodings can be based on percentages or more complex ruli 5 Time frames Targets are assigned time frames by which they must be accomplished A time frame is often divided into smaller intervals to provide performance mileposts 6 Benchmarks Targets are measured against a baseline or benchmark The previous year39s results often serve as a benchmark but arbitrary numbers or external benchmarks may also be used compare WWGJNMWMM Slldca Performance Measurement p 131 Key performance indicator KBI Outcome KPls vs Driver KPls value drivers lagging indicators leading indicators eg revenues eg sales leads Driver KPIs have a significant effect on outcome KPIs but the reverse is not necessarily true TEST QUESTION ill mmaaoummm Stick 3 ss Performance Measurement System p 132 Balanced Scorecard BSC 1 Most popular performance measurement systems in use are variants of 880 2 A performance measurement and management methodology that helps translate an organization s nancial customer internal process and learning and growth objectives and targets into a set of actionable initiatives HighlightedA mmaaaummm Slide 3 so Balanced Scorecard Flnanma To succeed nanuauy huw um we appear 10 our shareholders Cus omer To achweve our appear In our customersquot mlema Business Processes 3 a 0 1 s 5 m 2 shareholders and E g 9 E customers what 0 s S 2 busmess processes must we excew 1 and Improve The meaning of balance Strategy Map and Balanced Scorecard Fig 312 p 134 Strategy Map Balanced Scorecard Strategic Initiatives Linked Objectives Measures and Targets Action Plans Increase Net Net income Increase Flnanclal Income growth 25 Increase M I aintenance Increase Change licensmg cusmmer Customquot retention rate 15 and maintenance contracts Retention Improve Call Issue 1 I S d d d process mprove tan ar ize center turniwound I 30 call center processes Performance time 22393quot 525 133151 i I 25 bonus u rade Growth Turnover rate 0 pg Copyright 2015 Pearson Education Balanced in BSC p 135 Combined set of measures for 1 915 Financial and nonfinancial Leading and lagging Internal and external Quantitative and qualitative Short term and long term mmaaoumm Inc Slldc 3 SI Six Si ma as a Performance Measurement System pp 135136 Six Sigma TEST QUESTION A performance manaqement methodology aimed at reducing the number of defects in a business process to as close to zero defects per million opportunities DPMO as possible Sigma 0 symbol that statisticians use to measure the variability in a process the number of defects business upmaaonmmm Slide 340 Six Sigma as a Performance Measurement System pp 136 The DMAIC performance model A closedloop business improvement model that encompasses the steps of defining measuring analvzinci improvmq and controlling a process Lean Six Sigma not going to ask I I F I I Combined to improve performance management mMQNMWMM slid 3 61 Skip Comparison of Balanced Scorecard and Six Sigma TABLE 31 Comparison of Balanced Scorecard and Six Sigma Balanced Scorecard Strategic management system Relates to the longerterm View of the business Designed to develop balanced set of measures Identifies measurements around vision and values Critical management processes are to clarify visionstrategy communicate plan set targets align strategic initiatives and enhance feedback Balances customer and internal operations without a clearly defined Six Sigma Performance measurement system Provides snapshot of business39s performance an identifies measures that drive performance tow profitability Designed to identify a set of measurements the impact profitability Establishes accountability for leadership for Wellness and profitability includes all business processes management and operational Balances management and employees roles balances costs and revenue of heavy processes WQMICWWM Slide 3 62 Application Case 38 Expediacom s Customer Satisfaction Scorecard Questions for Discussion 1 Who are the customers for Expediacom Online shoppers Why is customer satisfaction a very important part of their business If not then business will go down 2 How did Expediacom improve customer satisfaction with scorecards 3 What were the challenges the proposed solution and the obtained results WMQQDMWMM Slide 3 as End of the Chapter Learn Chapter Highlights pp 139140 Learn Key Terms p 140 mmozoummm sud 3 64 End of the Chapter Questions comments mmozoummm sud 65 This work is protected by United States copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning Dissemination or sale of any part of this work including on the World Wide Web wit destroy the integrity of the work and is not permit ted The work and materials from it should never be made available to students except by instructors using the accompanying text in their classes All recipients of this work are expected to abide by these restrictions and to honor the intended pedagogical purposes and the needs of other instructors who rely on these materials All rights reserved No part ofthis publication may be reproduced stored in a retrieval system ortransmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the prior written permission ofthe publisher Printed in the United States of America WQMIOWMM sumsu Business Intelligence A Managerial Perspective on Analytics 3rcl Edition ti39fs iNEss INTELLIGENCE V ManagerialPcrqpcctixc nnAnulyIiCs Chapter 2 Data Warehousing