AGILE DATA WAREHOUSE DESIGN WITH BIG DATAJohn DiPietro & Jim Stagnitto 1

AGENDA INTRODUCTION / A2C OVERVIEWMODELING FOR END USERSROLE OF DIMENSIONAL MODELS IN BIG DATAEXAMPLE: E-COMMERCE STRUCTURED DATA: SALESSEMI-STRUCTURED DATA: CLICKSTREAMAGILE DIMENSIONAL MODELING OVERVIEWCASE STUDY REVIEWQ&A2 INTRODUCTION A2C BOUTIQUE EDM (ENTERPRISE DATA MANAGEMENT) CONSULTANCY FIRM:DATA WAREHOUSINGMASTER DATA MANAGEMENTCLOSED LOOK ANALYTICS AND VISUALIZATIONDATA & APPLICATION ARCHITECTURE JOHN DIPIETRO PRINCIPAL, CHIEF TECHNOLOGY OFFICER JIM STAGNITTO DATA WAREHOUSE & MDM ARCHITECT3 ON THURSDAY 11/14 A2CS JIM STAGNITTO AND JOHN DIPIETRO PRESENTED A WORKSHOP FEATURING AGILE DATA WAREHOUSE DESIGN - A STEP-BY-STEP METHOD FOR DATA WAREHOUSING / BUSINESS INTELLIGENCE (DW/BI) PROFESSIONALS TO BETTER COLLECT AND TRANSLATE BUSINESS INTELLIGENCE REQUIREMENTS INTO SUCCESSFUL DIMENSIONAL DATA WAREHOUSE DESIGNS.BEAMTHE METHOD UTILIZES (BUSINESS EVENT ANALYSIS AND MODELING) - AN AGILE APPROACH TO DIMENSIONAL DATA MODELING THAT CAN BE USED THROUGHOUT ANALYSIS AND DESIGN TO IMPROVE PRODUCTIVITY AND COMMUNICATION BETWEEN DW DESIGNERS AND BI STAKEHOLDERS. SPONSORED BY MICROSOFT NERD (NEW ENGLAND RESEARCH AND DEVELOPMENT CENTER) AND ATTENDED BY 93 DATA SCIENTISTS COMPETITIVE ADVANTAGECEO, Craig SpitzerPres., Scott KingCTO, John DiPietroCRO, Brian Cassidy Managing Sales Dir., Joe CattieThe founders of a2c were part of the fastest growing privately held IT consulting and staff augmentation firm in the U.S. from 1994-2002. Our Executive Management Team has over 100 years of collective experience and has been responsible for delivering over a half billion dollars of IT Consulting and staff augmentation revenue from 1994 through the present day.a2c Top Twenty Most Promising Data Analytics November 2013Alliance Consulting, Inc. 1999, 2000, 2001CEO, Alliance Consulting Group, Craig Spitzer 2001 AGILE DW DESIGN OVERVIEW6 MODELING FOR END USERS: HOW TO DESIGN TO ANSWER BUSINESS QUESTIONS?THINK ABOUT HOW QUESTIONS ARE ARTICULATED AND HOW THE ANSWERS SHOULD BE DELIVEREDIDENTIFY A COMMON QUESTION FRAMEWORKDESIGN AN ARCHITECTURE THAT EMBRACES AND LEVERAGES THIS COMMON QUESTION FRAMEWORKUTILIZE THE BEST DESIGNS AND TECHNOLOGIES TO: (A) DERIVE THE ANSWERS (B) PRESENT THEM IN COMPELLING WAYS THAT LEAD TO THE NEXT INTERESTING QUESTION!7 HOW DO WE ASK QUESTIONS? WhatWhenWhoHOW DO THIS QUARTERS SALES BY SALES REP OF ELECTRONIC PRODUCTS THAT WE PROMOTED TO RETAIL CUSTOMERS IN THE EAST COMPARE WITH LAST YEARS? When WhoWhy WhereWhat8 HOW DO WE ASK QUESTIONS? EVENTS / TRANSACTIONS E.G. SALEA IMMUTABLE "FACT" THAT OCCURS IN A TIME AND (TYPICALLY A) PLACEINTERROGATIVES: WHO, WHAT, WHEN, WHERE, WHYDESCRIPTIVE CONTEXT THAT FULLY DESCRIBES THE EVENTA SET OF DIMENSIONS" THAT DESCRIBE EVENTS9 DIMENSIONAL VALUE PROPOSITION IT MAKES SENSE TO PRESENT ANSWERS TO PEOPLE USING THE SAME TAXONOMY OF EVENTS AND INTERROGATIVES (AKA: FACTS AND DIMENSIONS - DIMENSIONAL STRUCTURE) THAT THEY USE WHEN FORMING QUESTIONS;EVENTS ARE INSTANCES OF PROCESSES ;ITS BEST TO PRESENT INFORMATION TO PEOPLE WHO WILL ASK THE SYSTEM QUESTIONS IN DIMENSIONAL FORM;THIS IS TRUE REGARDLESS OF THE TYPE OF INFORMATION BEING INTERROGATED, ITS SOURCE, OR IT STUFF (LIKE DATABASE TECHNOLOGIES UTILIZED);ITS BEST TO MODEL THIS PRESENTATION LAYER BASED ON THE EVENTS (AKA: BUSINESS PROCESSES) THAT UNDERLIE THE QUESTIONS.10 How How ManyWhy 11 SCENARIOS: A BRIEF DISCUSSION OF HOW AND WHERE DIMENSIONAL MODELING AND/OR DATABASES FIT WITHIN COMMON AND EMERGING BIG DATA DATA WAREHOUSING ARCHITECTURES12 KIMBALL DIMENSIONAL DW Dimensional BI Semantic Layer Dimensional Data Warehouse Data Movement / Integration Source Data (Structured)13 KIMBALL WITH BIG DATA Dimensional BI Semantic Layer Dimensional Data WarehouseBig Data Capture (e.g. HDFS)Big Data Discovery (e.g. MR)Data Movement / Integration TierData Movement / Integration TierSource Data TierSource Data Tier(Un/Semi-Structured)(Structured)14 CORPORATE INFORMATION FACTORY (CIF) Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical)Corporate Information Factory 3NF DWData Movement / Integration Source Data (Structured)15 CIF WITH BIG DATA Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical)Big Data Capture (e.g. HDFS)Big Data DiscoveryCorporate Information Factory 3NF DW(e.g. MR)Data Movement / Integration TierData Movement / Integration TierSource Data TierSource Data Tier(Un/Semi-Structured)(Structured)16 DATA VAULT Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical)Data VaultData Movement / Integration Source Data (Structured)17 DATA VAULT WITH BIG DATA Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical)Big Data Capture (e.g. HDFS)Big Data DiscoveryData Vault(e.g. MR)Data Movement / Integration TierData Movement / Integration TierSource Data TierSource Data Tier(Un/Semi-Structured)(Structured)18 COMMON FRAMEWORK Dimensional BI Semantic Layer Dimensional Tier [Physical (Kimball) or Virtual (CIF or Data Vault)(Virtual or Physical) Persistent Un/SemiStructured Staging AreaUnstructured -> Structured Data Discovery ProcessingPersistent Structured Data Repository (not needed for Kimball)Un/Semi-Structured Data MovementStructured Data MovementUn/Semi-Structured Source DataStructured Source Data (Structured) 19Insight Generation / Data Mining COMMON FRAMEWORK Dining Room Readily Accessible to End Users (and BI Developers) Safe, Hospital Environment Data Assets Ready for Primetime Dimensionally StructuredDimensional BI Semantic Layer Dimensional Tier [Physical (Kimball) or Virtual (CIF or Data Vault)(Virtual or Physical) Persistent Un/SemiStructured Staging AreaUnstructured -> Structured Data Discovery ProcessingPersistent Structured Data RepositoryKitchen(not needed for Kimball)Un/Semi-Structured Data MovementStructured Data MovementUn/Semi-Structured Source DataStructured Source Data (Structured)Clickstream DataOff Limits to End Users Data Professionals Only Please Dangerous / Inhospitable Environment Data Assets Not Ready for Primetime Structured Variably For Data ProcessingeCommerce SaleeCommerce Example20 E-COMMERCE EXAMPLE: CLICKSTREAM Semi-Structured Recording of every page request made by a user Includes some structural elements such as when the request was made and who the user is Requires significant prep work in order to fit into a traditional row-based relational database Apples and Oranges: Pre-Sessionized Page Visits, Detailed Product Views, Catalogue Requests, Shopping Cart Adds / Deletes / Abandons, etc. Needs to be converted into separate-butrelatable dimensional facts - with many shared (conformed) dimensions21Raw Clickstream Data 25 52 164 240 274 328 368 448 538 561 630 687 730 775 825 834 39 120 124 205 401 581 704 814 825 834 35 249 674 712 733 759 854 950 39 422 449 704 825 857 895 937 954 964 15 229 262 283 294 352 381 708 738 766 853 883 966 978 26 104 143 320 569 620 798 7 185 214 350 529 658 682 782 809 849 883 947 970 979 227 390 71 192 208 272 279 280 300 333 496 529 530 597 618 674 675 720 855 914 932 183 193 217 256 276 277 374 474 483 496 512 529 626 653 706 878 939 161 175 177 424 490 571 597 623 766 795 853 910 960 125 130 327 698 699 839 392 461 569 801 862 27 78 104 177 733 775 781 845 900 921 938 101 147 229 350 411 461 572 579 657 675 778 803 842 903 71 208 217 266 279 290 458 478 523 614 766 853 888 944 969 43 70 176 204 227 334 369 480 513 703 708 835 874 895 25 52 278 730 151 432 504 830 890 71 73 118 274 310 327 388 419 449 469 484 706 722 795 810 844 846 918 130 274 432 528 967 188 307 326 381 403 523 526 722 774 788 789 834 950 975 89 116 198 201 333 395 653 720 846 70 171 227 289 462 538 541 623 674 701 805 946 964 143 192 317 471 487 631 638 640 678 735 780 865 888 935 17 242 471 758 763 837 956 52 145 161 283 375 385 676 721 731 790 792 885 182 229 276 529 43 522 565 617 859 TYPICAL CLICKSTREAM PAGE VIEW DIMENSIONAL MODEL WhatWhenWhatWhoWhy22 E-COMMERCE EXAMPLE: WEB SALES FULLY STRUCTURED THE SALE TRANSACTION TYPICALLY CARRIES ALL FUNDAMENTAL DIMENSIONS: TIME CUSTOMER REFERRING URL / SEARCH PHRASE PRODUCT PURCHASE AND/OR SHIPMENT (GEO OR URL) LOCATIONS PROMOTION / CAMPAIGN ETC. AND HOW MANY MEASURES UNIT AND PRICE QUANTITIES / AMOUNTS DISCOUNT AMOUNTS ETC.23 E-COMMERCE DIMENSIONALITY Facts (below) & Dimensions (right)Time (When)Page VisitView Start View End Session Start Session EndCustomer (Who)Web Page (Where)VisitorCurrentPre vious NextDetailed Product ViewView Start View End Session Start Session EndProspectCurrentPre vious NextShopping Cart ActivityActivity Start Activity EndSale (Checkout)Shipment / DeliveryProduct (What)Referring URL (Where)Promotion / Campaign (Why)Activity Type (How)ProspectSale Start Sale EndCustomerShipment DeliveryCustomer Delivery Recipient24 AGILE DW DESIGN OVERVIEW25 THE FIRST DIMENSIONAL MODELER: R.K. Ralph Kimball? Rudyard Kipling26 I keep six honest serving-men (They taught me all I knew); Their names are What and Why and When And How and Where and Who Rudyard Kipling27 THE7WS Framework How How ManyWhy HOW DID WE GET HERE? DW ARCHITECTURES: A BRIEF HISTORY Corporate Information FactoryUndisciplined DimensionalDimensional Bus ArchitectureData-Driven AnalysisReport-Driven AnalysisProcess-Driven Analysis 7WS DIMENSIONAL MODEL WhenWhoTimeCustomerDayHow Facts:EmployeeMonthMuchThird PartyFiscal PeriodManyOrganizationOften$ WhatWhereProductLocationWhyCausalGeographic Store Ship To Hospital??ServiceTransactionsPromotion Reason Weather Competition How WhyBEAMHow ManyBusiness Event Analysis & Modeling TO DOWNLOAD WITH AUDIO WORKSHOP FILE: PLEASE COMPLETE THE FOLLOWING REQUEST FORM FOR FREE LINK TO AGILE DATA WAREHOUSE DESIGN PRESENTATION. REVIEWS: EXCELLENT PRESENTATION. IT IS GOOD TO HEAR MEANINGFUL INFORMATION ABOUT NEW DEVELOPMENTS IN HOW AGILE METHODOLOGIES CAN BE APPLIED TO DW/BI WORK. BIG KUDOS TO THE PRESENTERS AND ORGANIZERS. THANKS, I FOUND IT VERY USEFUL AND ENJOYABLE.- RAMON VENEGAS EXTREMELY USEFUL TO UNDERSTAND HOW TO APPLY AGILE APPROACH TO DWH; HOW CREATE A FRAMEWORK WHERE MODEL CHANGES ARE WELCOME, AND BRING USERS TO THE PROCESS OF DWH MODELING. ALFREDO GOMEZ34 HOW do you design a data warehouse? TECH DESIGN ARTIFACTS? OK, NOW VALIDATE WITH BUSINESS WHY Agile Data Warehousing? WATERFALL BI/DW DEVELOPMENT Limited Stakeholder Interaction Analysis Design Development This YearStakeholder InputBDUF RequirementsData ModelNext YearTest ReleaseETLBIDATAVALUE? AGILE DW/BI DEVELOPMENT Stakeholder interaction?JEDUFBI PrototypingETLReview ReleaseThis YearNext YearIteration 1VALUE?Iteration 2ETL BI Iteration 3RevADMVALUEIteration VALUE!DATAIteration nVALUE!VALUE! STATE OF THE DW FIELD SOLID:DIMENSIONAL DATA WAREHOUSE DESIGN IS MATUREPROVEN DESIGN PATTERNS EXIST FOR COMMON REQUIREMENTS HIT OR MISS:COLLECTING UNAMBIGUOUS AND THOROUGH REQUIREMENTSSLOTTING REQUIREMENTS INTO PROVEN DESIGN PATTERNSEND-USER OWNERSHIP AND VALIDATIONTOO OFTEN: SNATCHING DEFEAT FROM THE JAWS OF VICTORY41 MODELSTORMING: QUICKInteractiveInclusiveData ModelerBI StakeholdersFun BEAM METHODOLOGY Structured, non-technical, collaborative working conversation directly with BI UsersBEAM BI Users Business Process, Organizational, Hierarchical, and Data Knowledge Focused Data ProfilingData ModelerBI Stakeholders Logical and Physical (Kimball-esque) Dimensional Data Models Example data Detailed and Testable ETL Specification Instantiated DW Prototype REQUIREMENTS = DESIGN4 COLLABORATION AT EVERY STEP AGILE DATA MODELING REQUIREMENTS: TECHNIQUES FOR ENCOURAGING INTERACTIONMUST USE SIMPLE, INCLUSIVE NOTATION AND TOOLSMUST BE QUICK: HOURS RATHER THAN DAYS MODELSTORMINGBALANCE JUST IN TIME (JIT) AND JUST ENOUGH DESIGN UP FRONT (JEDUF) TO REDUCE DESIGN REWORKDW DESIGNERS MUST EMBRACE DATA MODEL CHANGE, ALLOW MODELS TO EVOLVE, AVOID GENERIC DATA MODELS; NEED DESIGN PATTERNS THEY CAN TRUST TO REPRESENT TOMORROWS BI REQUIREMENTS TOMORROWETL AND BI DEVELOPERS MUST EMBRACE DATABASE CHANGE; NEED TOOL SUPPORT46 WHAT kind of model? CALENDARPRODUCTDate KeyProduct KeyDate Day Day in Week Day in Month Day in Qtr Day in Year Month Qtr Year Weekday Flag Holiday FlagProduct Code Product Description Product Type Brand Subcategory CategorySALES FACT Date Key Product Key Store Key Promotion KeyQuantity Sold Revenue Cost Basket Count STOREPROMOTIONStore KeyPromotion KeyStore Code Store Name URL Store Manager Region CountryPromotion Code Promotion Name Promotion Type Discount Type Ad Type MODELING BY ABSTRACTION MODELING BY EXAMPLE: AGILE DW DESIGN PROCESS5 COLLABORATIVE / CONVERSATIONAL DESIGNWho does what? Customers buy productsBEAM ModelerSubjects Verb ObjectsBI Users DESIGN USING NATURAL LANGUAGE VERBS EVENTS RELATIONSHIPS FACT TABLESNOUNS DETAILS ENTITIES DIMENSIONSMAIN CLAUSE SUBJECT-VERB-OBJECTPREPOSITIONS CONNECT ADDITIONAL DETAILS TO THE MAIN CLAUSEINTERROGATIVES THE 7WS DIMENSION TYPESBUSINESS VOCABULARY - NO IT-SPEAK55 Spreadsheet-like Models Event Table Name (filled in later)Subject Column Name Verb Object Column NameInterrogativeDetails Example Data (4-6 rows) Straightforward Methodology 1 1 1 1 1 1Subject-Verb-Object1 1 1 3 1 1WhoWhatWhenDeclare Event Type WhereHow (many)WhySufficient Detail Fact GranularityHow1 1 1 4 1 11 1 1 5 1 1 1 1 2 1 1 1 1 1 1 6 1 1 1 1 1 7 1 1 1 1 8 1 1 1 1 1 1 9 1 1Initial Data ExamplesQuantities - Facts CAPTURE EXAMPLE DATA: verbon/at/everySUBJECTOBJECTEVENT DATE[who][what][when][where][how many][why][how]TypicalTypical/PopularTypicalTypicalTypical/AverageTypical/NormalTypical/NormalDifferentDifferentDifferentDifferentDifferentDifferentDifferentRepeatRepeatRepeatRepeatRepeatRepeatRepeatMissingMissingMissingMissingMissingMissingMissingGroupMultiple/BundleOld, LowOld, Low ValueOldest neededNearMin, Negative, 0New, HighNew, HighMost Recent, FutureFarMax, PrecisionMulti-LevelENGAGE CLARIFY DEFINITIONS / CONFORM DIMENSIONSMultiple ValuesExceptionalExceptionalILLUSTRATE EXCEPTIONS DRIVE OUT UNIQUENESS SHOW AND TELL THOUGHTFUL EXAMPLE DATA:Detailed ETL Specification IDENTIFY EVENT TYPE EARLY ADJUST CONVERSATION BASED ON EVENT TYPE DISCRETE EVENT -> TRANSACTION INSTANTANEOUS/SHORT DURATION, IRREGULARLY OCCURRING EVENTS OR TRANSACTIONSRECURRING EVENT -> PERIODIC SNAPSHOT MEASUREMENTREGULARLY OCCURRING EVENTS, ONGOING PROCESSES, TYPICALLY USE TO MEASURE CUMULATIVE OF DISCRETE EVENTSEVOLVING EVENT -> ACCUMULATING SNAPSHOT TIMELINENON-INSTANTANEOUS/LONGER DURATION, IRREGULARLY OCCURRING EVENTS OR TRANSACTIONSREPRESENTS CURRENT STATUS - REFLECTS ADJUSTMENTS61 CAPTURE WHEN DETAILS When do Customers order Products? On the Order Date BEAM ModelerBI Users ANY OTHER WHENS ? ANY OTHER WHOS ? AND SO ON... MODEL HOW MANY MEASURES: ADDITIVE CAN BE SUMMED UP OVER ANY COMBINATION OF DIMENSIONS. NO SPECIAL RULESNON-ADDITIVE CAN NOT BE SUMMED OVER ANY DIMENSION E.G. UNIT PRICE OR TEMPERATURE MUST BE AGGREGATED IN OTHER WAYS E.G. AVERAGE, MIN, MAX DEGENERATE DIMENSIONS TRANSACTION #, TIMESTAMPS, FLAGSSEMI-ADDITIVE CAN NOT BE SUMMED ACROSS AT LEAST ONE DIMENSION E.G. BALANCES CAN NOT BE SUMMED OVER TIME66 MODELING DIMENSIONS: ANNOTATE W TARGETED DATA PROFILING: PROCEED THROUGH THE BUSINESS PROCESS VALUE CHAIN: COLLABORATIVE DIMENSION CONFORMANCE: IDENTIFY HIERARCHY TYPES: GRAPHICALLY DEPICT HIERARCHIES: VISUALIZE THE HIERARCHIES PAINT THE ORGANIZATION PROTOTYPE! NOT DATA MODEL REVIEW RECAP: COLLABORATIVE AND AGILE DATA MODELINGDATA SOURCINGDATA CONFORMANCEREQUIREMENTS = DESIGN SLOTS DIRECTLY INTO PROVEN AND MATURE DIMENSIONAL DATA WAREHOUSING DESIGN PATTERNSVALIDATION THROUGH PROTOTYPING SEMI-AUTOMATED BUILD OF DIMENSIONAL DATA WAREHOUSEPERFECT COMPLIMENT TO AGILE BI TOOLS AND METHODS (E.G. PENTAHO)76 IF YOU HAVE BEEN AFFECTED BY ANY OF THE ISSUES RAISED IN THIS PRESENTATION AGILE DATA WAREHOUSE DESIGN LAWRENCE CORR, JIM STAGNITTO, DECISION PRESS, NOVEMBER 2011 QUESTIONS/COMMENTS? CONTACT: JIM STAGNITTO OR JOHN DIPIETRO215-789-4816 A2C CORPORATE OVERVIEW & INDUSTRY EXPERIENCE8 0 COMPANY OVERVIEW TECHNOLOGY SOLUTION CONSULTANCY HEADQUARTERED IN PHILADELPHIA WITH REGIONAL OFFICES IN NEW YORK AND BOSTONSERVICING HEALTHCARE, LIFE SCIENCE, TEL-COM AND FINANCIAL SERVICES INDUSTRIES WITH RECENT OBTAINMENT OF OUR GSA SCHEDULE TO PURSUE FEDERAL GOVERNMENT OPPORTUNITIESCONSULTANT BASE OF OVER 2500 PROVEN IT PROFESSIONALS THROUGHOUT THE NORTH EAST REGION WITH A RECRUITING NETWORK WHICH PROVIDES NATIONAL COVERAGE8 1 COMPANY OVERVIEW FLEXIBLE APPROACH TO HELPING OUR CLIENTS WITH THEIR INITIATIVES PROJECT-BASED SOLUTIONSSTAFF AUGMENTATIONMANAGED SERVICE OFFERINGS ON-SHORE QA , DEVELOPMENT & APPLICATION SUPPORTEXECUTIVE & PROFESSIONAL SEARCH8 2 a2cs Recruiting Engine and Methodology is one of the Best in the Industry CAPABLE OF PRODUCING QUALITY RESULTS ON-DEMAND FOR OUR CLIENTS. RESOURCE MANAGERS CONTINUALLY SILO DISCIPLINES WITH AVAILABLE CANDIDATES WHO HAVE PROVEN THEIR ABILITIES WITH A2C OVER THE PAST DECADE. THE A2C SOLUTIONS ORGANIZATION IS INSTRUMENTAL IN THE SCREENING AND SELECTION PROCESS TO ENSURE THAT CANDIDATES SUBMITTED TO CLIENTS ARE AN IDEAL MATCH. THE A2C TEAM A2CS CULTURE PROVIDES AN ABILITY TO ATTRACT AND RETAIN THE BEST TALENT IN THE INDUSTRY AND FOSTERS CREATIVITY, INTEGRITY, GROWTH AND TEAMWORK. ALTERNATIVE SOLUTIONS A2C PROVIDES CLIENTS WITH AN ALTERNATIVE SOLUTION TO A BIG 4 CONSULTANCY AT SUBSTANTIAL SAVINGS FOR PROJECTS THAT ARE BETWEEN $500K AND $5M DUE TO FLEXIBILITY, AGILITY AND FOCUS. A2C SOLUTION ENGAGEMENT STRUCTURES TECHNOLOGY STRATEGY & ROADMAP FORMULATIONNEEDS & READINESS ASSESSMENTPACKAGE & PLATFORM SELECTIONSPROOF OF CONCEPT IMPLEMENTATIONREQUIREMENTS DISCOVERY & SPECIFICATIONSPROGRAM/PROJECT MANAGEMENTFULL LIFE CYCLE & APPLICATION DEVELOPMENTINFRASTRUCTURE & FACILITIES INITIATIVESMANAGED SERVICES & MAINTENANCE SUPPORT8 6 A2C SOLUTIONS CAPABILITIES ENTERPRISE DATA MANAGEMENT PRACTICE HELPS CLIENTS MANAGE THEIR COMPLETE INFORMATION LIFECYCLE FROM THEIR ON-LINE TRANSACTIONAL SYSTEMS TO THEIR DATA WAREHOUSING, ENTERPRISE REPORTING, DATA MIGRATION, BACK-UP AND RECOVERY STRATEGIESBUSINESS ARCHITECTURE & OPTIMIZATION PRACTICE UTILIZES SIX SIGMA LEAN METHODOLOGIES TO ANALYZE, RE-ENGINEER AND AUTOMATE OUR CLIENTS BUSINESS PROCESSES TO LEVERAGE HUMAN WORKFLOW AND BUSINESS RULES ENGINE TECHNOLOGIES TO CREATE EFFICIENCIES AND PROVIDE BUSINESS UNIT OWNERS WITH THE NECESSARY METRICS TO CONTINUALLY IMPROVE PERFORMANCEPROGRAM MANAGEMENT OFFICE OVERSEES ALL ASPECTS OF SOLUTIONS PLANNING AND DELIVERY ACROSS CLIENT ENGAGEMENT TEAMS AND PROVIDES THE METHODOLOGY AND FRAMEWORKS WHICH ARE BASED ON PMI INDUSTRY STANDARDS8 7 A2C SOLUTIONS CAPABILITIES APPLICATION DEVELOPMENT & MANAGED SERVICES PRACTICE HELPS CLIENTS ARCHITECT, IMPLEMENT AND DEPLOY THE LATEST MICROSOFT AND ENTERPRISE JAVA BASED APPLICATIONS WHICH ARE BUILT ON PROVEN FRAMEWORKS AND ARCHITECTURES FOR THE ENTERPRISEA2C'S SDLC DELIVERY MODEL IS COMPRISED OF OVER 20 YEARS COLLECTIVE BEST PRACTICES AND INDUSTRY PROVEN METHODOLOGIES THAT ALLOW OUR DELIVERY TEAMS TO RAPIDLY DESIGN, DEVELOP AND IMPLEMENT SOLUTIONS. OUR SDLC MODEL HAS BEEN DESIGNED TO COMPLEMENT OUR PROJECT MANAGEMENT METHODOLOGY, UTILIZING ITERATIVE DEVELOPMENT CYCLES THAT ENABLE PROJECT TEAMS TO PROVIDE CONSISTENTLY HIGH QUALITY, ON-TIME DELIVERABLES, REGARDLESS OF TECHNOLOGY PLATFORM8 8 LET A2C HELP WITH ALL YOUR BUSINESS SOLUTIONS CONNECT TO A2C For Further information on the Agile Data Warehouse Design please contact: John DiPietro, CTOor Jim Stagnitto, Practice Director of Information Servicesa2c.coma2c Philadelphia 1801 Market Street Suite 2430 Philadelphia, PA 19103 215-789-4816 contact: Joe Cattie JCattie@a2c.coma2c Boston 100 Grandview Road Suite 215 Braintree, MA 02184 781-848-0005 contact: Scott King SKing@a2c.coma2c New York 401 Greenwich Street 3rd Floor New York, NY 10013 212-913-0933 contact: John DiPietro JDiPietro@a2c.com