a2c Boston Big Data Meet-up: Agile Data Warehouse Design

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Preview this Big Data Seminar, and request the complete audio and animated download 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.   The method utilizes BEAM✲ (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. a2c's Practice Director of Information Services and Author Jim Stagnitto and CTO John DiPietro designed this presentation to provide an overview of Agile Warehouse Design that will facilitate communication between Data Modelers and Business Intelligence Stakeholders in a fun and informative one hour session. Demystify this process and find out what the 96 Data Scientists who attended November's Boston Big Data Meet-up are talking about.

“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 Gomez

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a2c Boston Big Data Meet-up: Agile Data Warehouse Design

  1. 1. AGILE DATA WAREHOUSE DESIGN WITH BIG DATA John DiPietro & Jim Stagnitto 1
  2. 2. AGENDA • INTRODUCTION / A2C OVERVIEW • MODELING FOR END USERS • ROLE OF DIMENSIONAL MODELS IN BIG DATA • EXAMPLE: E-COMMERCE • STRUCTURED DATA: SALES • SEMI-STRUCTURED DATA: CLICKSTREAM • AGILE DIMENSIONAL MODELING OVERVIEW • CASE STUDY REVIEW • Q&A 2
  3. 3. INTRODUCTION A2C • • BOUTIQUE EDM (ENTERPRISE DATA MANAGEMENT) CONSULTANCY FIRM: • DATA WAREHOUSING • MASTER DATA MANAGEMENT • CLOSED LOOK ANALYTICS AND VISUALIZATION • DATA & APPLICATION ARCHITECTURE JOHN DIPIETRO • • PRINCIPAL, CHIEF TECHNOLOGY OFFICER JIM STAGNITTO • • DATA WAREHOUSE & MDM ARCHITECT 3
  4. 4. ON THURSDAY 11/14 A2C’S 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. BEAM✲ THE 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…
  5. 5. COMPETITIVE ADVANTAGE CEO, Craig Spitzer Pres., Scott King CTO, John DiPietro CRO, Brian Cassidy Managing Sales Dir., Joe Cattie The 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 2013 Alliance Consulting, Inc. 1999, 2000, 2001 CEO, Alliance Consulting Group, Craig Spitzer 2001
  6. 6. AGILE DW DESIGN OVERVIEW 6
  7. 7. MODELING FOR END USERS: HOW TO DESIGN TO ANSWER BUSINESS QUESTIONS? • • THINK ABOUT HOW QUESTIONS ARE ARTICULATED AND HOW THE ANSWERS SHOULD BE DELIVERED • IDENTIFY A COMMON QUESTION FRAMEWORK • DESIGN AN ARCHITECTURE THAT EMBRACES AND LEVERAGES THIS COMMON QUESTION FRAMEWORK • UTILIZE THE BEST DESIGNS AND TECHNOLOGIES TO: (A) DERIVE THE ANSWERS (B) PRESENT THEM IN COMPELLING WAYS THAT LEAD TO THE NEXT INTERESTING QUESTION! 7
  8. 8. HOW DO WE ASK QUESTIONS? What When Who “HOW DO THIS QUARTER‟S SALES BY SALES REP OF ELECTRONIC PRODUCTS THAT WE PROMOTED TO RETAIL CUSTOMERS IN THE EAST COMPARE WITH LAST YEAR‟S?” When Who Why Where What 8
  9. 9. HOW DO WE ASK QUESTIONS? EVENTS / TRANSACTIONS • • E.G. SALE • A IMMUTABLE "FACT" THAT OCCURS IN A TIME AND (TYPICALLY A) PLACE INTERROGATIVES: • • WHO, WHAT, WHEN, WHERE, WHY • DESCRIPTIVE CONTEXT THAT FULLY DESCRIBES THE EVENT • A SET OF “DIMENSIONS" THAT DESCRIBE EVENTS 9
  10. 10. 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 ; • IT‟S 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); • IT‟S BEST TO MODEL THIS PRESENTATION LAYER BASED ON THE EVENTS (AKA: BUSINESS PROCESSES) THAT UNDERLIE THE QUESTIONS. 10
  11. 11. How How Many Why 11
  12. 12. SCENARIOS: A BRIEF DISCUSSION OF HOW AND WHERE DIMENSIONAL MODELING AND/OR DATABASES FIT WITHIN COMMON AND EMERGING “BIG DATA” DATA WAREHOUSING ARCHITECTURES 12
  13. 13. KIMBALL DIMENSIONAL DW Dimensional BI Semantic Layer Dimensional Data Warehouse Data Movement / Integration Source Data (Structured) 13
  14. 14. KIMBALL WITH BIG DATA Dimensional BI Semantic Layer Dimensional Data Warehouse Big Data Capture (e.g. HDFS) Big Data Discovery (e.g. MR) Data Movement / Integration Tier Data Movement / Integration Tier Source Data Tier Source Data Tier (Un/Semi-Structured) (Structured) 14
  15. 15. CORPORATE INFORMATION FACTORY (CIF) Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Corporate Information Factory 3NF DW Data Movement / Integration Source Data (Structured) 15
  16. 16. CIF WITH BIG DATA Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Big Data Capture (e.g. HDFS) Big Data Discovery Corporate Information Factory 3NF DW (e.g. MR) Data Movement / Integration Tier Data Movement / Integration Tier Source Data Tier Source Data Tier (Un/Semi-Structured) (Structured) 16
  17. 17. DATA VAULT Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Data Vault Data Movement / Integration Source Data (Structured) 17
  18. 18. DATA VAULT WITH BIG DATA Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Big Data Capture (e.g. HDFS) Big Data Discovery Data Vault (e.g. MR) Data Movement / Integration Tier Data Movement / Integration Tier Source Data Tier Source Data Tier (Un/Semi-Structured) (Structured) 18
  19. 19. COMMON FRAMEWORK Dimensional BI Semantic Layer Dimensional Tier [Physical (Kimball) or Virtual (CIF or Data Vault) (Virtual or Physical) Persistent Un/SemiStructured Staging Area Unstructured -> Structured Data Discovery Processing Persistent Structured Data Repository (not needed for Kimball) Un/Semi-Structured Data Movement Structured Data Movement Un/Semi-Structured Source Data Structured Source Data (Structured) 19 Insight Generation / Data Mining
  20. 20. COMMON FRAMEWORK Dining Room Readily Accessible to End Users (and BI Developers) Safe, Hospital Environment Data Assets “Ready for Primetime” Dimensionally Structured Dimensional BI Semantic Layer Dimensional Tier [Physical (Kimball) or Virtual (CIF or Data Vault) (Virtual or Physical) Persistent Un/SemiStructured Staging Area Unstructured -> Structured Data Discovery Processing Persistent Structured Data Repository Kitchen (not needed for Kimball) Un/Semi-Structured Data Movement Structured Data Movement Un/Semi-Structured Source Data Structured Source Data (Structured) Clickstream Data Off Limits to End Users Data Professionals Only Please Dangerous / Inhospitable Environment Data Assets “Not Ready for Primetime” Structured Variably For Data Processing eCommerce Sale eCommerce Example 20
  21. 21. 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) dimensions 21 Raw 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
  22. 22. TYPICAL CLICKSTREAM “PAGE VIEW” DIMENSIONAL MODEL What When What Who Why 22
  23. 23. 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
  24. 24. E-COMMERCE DIMENSIONALITY Facts (below) & Dimensions (right) Time (When) Page Visit View Start View End Session Start Session End Customer (Who) Web Page (Where) Visitor Current
Pre vious Next Detailed Product View View Start View End Session Start Session End Prospect Current
Pre vious Next Shopping Cart Activity Activity Start Activity End Sale (Checkout) Shipment / Delivery Product (What) Referring URL (Where) Promotion / Campaign (Why) Activity Type (How) ✔︎ ✔︎ ✔︎ Prospect ✔︎ ✔︎ ✔︎ ✔︎ Sale Start Sale End Customer ✔︎ ✔︎ ✔︎ ✔︎ Shipment Delivery Customer Delivery Recipient ✔︎ 24
  25. 25. AGILE DW DESIGN OVERVIEW 25
  26. 26. THE FIRST DIMENSIONAL MODELER: R.K. Ralph Kimball? Rudyard Kipling 26
  27. 27. “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 Kipling 27
  28. 28. THE 7WS Framework
  29. 29. How How Many Why
  30. 30. HOW DID WE GET HERE?
  31. 31. DW ARCHITECTURES: A BRIEF HISTORY Corporate Information Factory Undisciplined Dimensional Dimensional Bus Architecture Data-Driven Analysis Report-Driven Analysis Process-Driven Analysis
  32. 32. 7WS DIMENSIONAL MODEL When Who Time Customer Day How – Facts: Employee Month Much Third Party Fiscal Period Many Organization Often £$€ What Where Product Location Why Causal Geographic Store Ship To Hospital ?? Service Transactions Promotion Reason Weather Competition
  33. 33. How Why BEAM How Many Business Event Analysis & Modeling
  34. 34. 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 GOMEZ 34
  35. 35. HOW do you design a data warehouse?
  36. 36. TECH DESIGN ARTIFACTS?
  37. 37. OK, NOW VALIDATE WITH BUSINESS…
  38. 38. WHY Agile Data Warehousing?
  39. 39. WATERFALL BI/DW DEVELOPMENT Limited Stakeholder Interaction Analysis Design Development This Year Stakeholder Input BDUF Requirements Data Model Next Year Test Release ETL BI DATA VALUE?
  40. 40. AGILE DW/BI DEVELOPMENT Stakeholder interaction ? JEDUF BI Prototyping ETL Review Release This Year Next Year Iteration 1 VALUE? Iteration 2 ETL BI Iteration 3Rev ADM VALUE Iteration … VALUE! DATA Iteration n VALUE! VALUE!
  41. 41. STATE OF THE DW FIELD • • SOLID: DIMENSIONAL DATA WAREHOUSE DESIGN IS MATURE • PROVEN DESIGN PATTERNS EXIST FOR COMMON REQUIREMENTS • • HIT OR MISS: COLLECTING UNAMBIGUOUS AND THOROUGH REQUIREMENTS • SLOTTING REQUIREMENTS INTO PROVEN DESIGN PATTERNS • END-USER OWNERSHIP AND VALIDATION • TOO OFTEN: SNATCHING DEFEAT FROM THE JAWS OF VICTORY 41
  42. 42. MODELSTORMING: QUICK Interactive Inclusive Data Modeler BI Stakeholders Fun
  43. 43. BEAM✲ METHODOLOGY Structured, non-technical, collaborative working conversation directly with BI Users BEAM✲ • BI User’s Business Process, Organizational, Hierarchical, and Data Knowledge • Focused Data Profiling Data Modeler BI Stakeholders • Logical and Physical (Kimball-esque) Dimensional Data Models • Example data • Detailed and Testable ETL Specification • Instantiated DW Prototype
  44. 44. REQUIREMENTS = DESIGN 4
  45. 45. COLLABORATION AT EVERY STEP
  46. 46. AGILE DATA MODELING REQUIREMENTS: • TECHNIQUES FOR ENCOURAGING INTERACTION • MUST USE SIMPLE, INCLUSIVE NOTATION AND TOOLS • MUST BE QUICK: HOURS RATHER THAN DAYS – MODELSTORMING • BALANCE „JUST IN TIME‟ (JIT) AND „JUST ENOUGH DESIGN UP FRONT‟ (JEDUF) TO REDUCE DESIGN REWORK • DW DESIGNERS MUST EMBRACE DATA MODEL CHANGE, ALLOW MODELS TO EVOLVE, AVOID GENERIC DATA MODELS; NEED DESIGN PATTERNS THEY CAN TRUST TO REPRESENT TOMORROW‟S BI REQUIREMENTS TOMORROW • ETL AND BI DEVELOPERS MUST EMBRACE DATABASE CHANGE; NEED TOOL SUPPORT 46
  47. 47. WHAT kind of model?
  48. 48. CALENDAR PRODUCT Date Key Product Key Date Day Day in Week Day in Month Day in Qtr Day in Year Month Qtr Year Weekday Flag Holiday Flag Product Code Product Description Product Type Brand Subcategory Category SALES FACT Date Key Product Key Store Key Promotion Key Quantity Sold Revenue Cost Basket Count STORE PROMOTION Store Key Promotion Key Store Code Store Name URL Store Manager Region Country Promotion Code Promotion Name Promotion Type Discount Type Ad Type
  49. 49. MODELING BY ABSTRACTION
  50. 50. MODELING BY EXAMPLE:
  51. 51. AGILE DW DESIGN PROCESS 5
  52. 52. COLLABORATIVE / CONVERSATIONAL DESIGN Who does what? “Customers buy products” BEAM✲ Modeler Subjects Verb Objects BI Users
  53. 53. DESIGN USING NATURAL LANGUAGE • VERBS – EVENTS – RELATIONSHIPS – FACT TABLES • NOUNS – DETAILS – ENTITIES – DIMENSIONS • MAIN CLAUSE – SUBJECT-VERB-OBJECT • PREPOSITIONS – CONNECT ADDITIONAL DETAILS TO THE MAIN CLAUSE • INTERROGATIVES – THE 7WS – DIMENSION TYPES • BUSINESS VOCABULARY - NO “IT-SPEAK” 55
  54. 54. “Spreadsheet”-like Models Event Table Name (filled in later) Subject Column Name Verb Object Column Name Interrogative Details Example Data (4-6 rows)
  55. 55. Straightforward Methodology 1 1 1 1 1 1 Subject-Verb-Object 1 1 1 3 1 1 Who What When Declare Event Type Where How (many) Why Sufficient Detail Fact Granularity How 1 1 1 4 1 1 1 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 1 Initial Data Examples Quantities - Facts
  56. 56. CAPTURE EXAMPLE DATA: verb on/at/every SUBJECT OBJECT EVENT DATE [who] [what] [when] [where] [how many] [why] [how] Typical Typical/Popular Typical Typical Typical/Average Typical/Normal Typical/Normal Different Different Different Different Different Different Different Repeat Repeat Repeat Repeat Repeat Repeat Repeat Missing Missing Missing Missing Missing Missing Missing Group Multiple/Bundle Old, Low Old, Low Value Oldest needed Near Min, Negative, 0 New, High New, High Most Recent, Future Far Max, Precision Multi-Level ENGAGE CLARIFY DEFINITIONS / CONFORM DIMENSIONS Multiple Values Exceptional Exceptional ILLUSTRATE EXCEPTIONS “DRIVE OUT UNIQUENESS” “SHOW AND TELL”
  57. 57. THOUGHTFUL EXAMPLE DATA: Detailed ETL Specification
  58. 58. IDENTIFY EVENT TYPE EARLY
  59. 59. ADJUST CONVERSATION BASED ON EVENT TYPE DISCRETE EVENT -> TRANSACTION • • INSTANTANEOUS/SHORT DURATION, IRREGULARLY OCCURRING EVENTS OR TRANSACTIONS RECURRING EVENT -> PERIODIC SNAPSHOT – MEASUREMENT • • REGULARLY OCCURRING EVENTS, ONGOING PROCESSES, TYPICALLY USE TO MEASURE CUMULATIVE OF DISCRETE EVENTS EVOLVING EVENT -> ACCUMULATING SNAPSHOT – TIMELINE • • NON-INSTANTANEOUS/LONGER DURATION, IRREGULARLY OCCURRING EVENTS OR TRANSACTIONS • REPRESENTS CURRENT STATUS - REFLECTS ADJUSTMENTS 61
  60. 60. CAPTURE WHEN DETAILS When do Customers order Products? “On the Order Date” BEAM✲ Modeler BI Users
  61. 61. ANY OTHER WHENS ?
  62. 62. ANY OTHER WHOS ?
  63. 63. AND SO ON...
  64. 64. MODEL HOW MANY MEASURES: • ADDITIVE – CAN BE SUMMED UP OVER ANY COMBINATION OF DIMENSIONS. NO SPECIAL RULES • NON-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, FLAGS SEMI-ADDITIVE – CAN NOT BE SUMMED ACROSS AT LEAST ONE DIMENSION E.G. BALANCES CAN NOT BE SUMMED OVER TIME 66
  65. 65. MODELING DIMENSIONS:
  66. 66. ANNOTATE W TARGETED DATA PROFILING:
  67. 67. PROCEED THROUGH THE BUSINESS PROCESS VALUE CHAIN:
  68. 68. COLLABORATIVE DIMENSION CONFORMANCE:
  69. 69. IDENTIFY HIERARCHY TYPES:
  70. 70. GRAPHICALLY DEPICT HIERARCHIES:
  71. 71. VISUALIZE THE HIERARCHIES
  72. 72. PAINT THE ORGANIZATION
  73. 73. PROTOTYPE! NOT “DATA MODEL REVIEW”
  74. 74. RECAP: COLLABORATIVE AND AGILE • • DATA MODELING • DATA SOURCING • DATA CONFORMANCE REQUIREMENTS = DESIGN • • SLOTS DIRECTLY INTO PROVEN AND MATURE DIMENSIONAL DATA WAREHOUSING DESIGN PATTERNS VALIDATION THROUGH PROTOTYPING • • SEMI-AUTOMATED BUILD OF DIMENSIONAL DATA WAREHOUSE • PERFECT COMPLIMENT TO AGILE BI TOOLS AND METHODS (E.G. PENTAHO) 76
  75. 75. IF YOU HAVE BEEN AFFECTED BY ANY OF THE ISSUES RAISED IN THIS PRESENTATION…
  76. 76. AGILE DATA WAREHOUSE DESIGN LAWRENCE CORR, JIM STAGNITTO, DECISION PRESS, NOVEMBER 2011
  77. 77. QUESTIONS/COMMENTS? CONTACT: JIM STAGNITTO OR JOHN DIPIETRO 215-789-4816
  78. 78. A2C CORPORATE OVERVIEW & INDUSTRY EXPERIENCE 8 0
  79. 79. COMPANY OVERVIEW • TECHNOLOGY SOLUTION CONSULTANCY HEADQUARTERED IN PHILADELPHIA WITH REGIONAL OFFICES IN NEW YORK AND BOSTON • SERVICING HEALTHCARE, LIFE SCIENCE, TEL-COM AND FINANCIAL SERVICES INDUSTRIES WITH RECENT OBTAINMENT OF OUR GSA SCHEDULE TO PURSUE FEDERAL GOVERNMENT OPPORTUNITIES • CONSULTANT BASE OF OVER 2500 PROVEN IT PROFESSIONALS THROUGHOUT THE NORTH EAST REGION WITH A RECRUITING NETWORK WHICH PROVIDES NATIONAL COVERAGE 8 1
  80. 80. COMPANY OVERVIEW • FLEXIBLE APPROACH TO HELPING OUR CLIENTS WITH THEIR INITIATIVES • PROJECT-BASED SOLUTIONS • STAFF AUGMENTATION • MANAGED SERVICE OFFERINGS – “ON-SHORE QA , DEVELOPMENT & APPLICATION SUPPORT” • EXECUTIVE & PROFESSIONAL SEARCH 8 2
  81. 81. a2c’s 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.
  82. 82. THE A2C TEAM A2C’S CULTURE PROVIDES AN ABILITY TO ATTRACT AND RETAIN THE BEST TALENT IN THE INDUSTRY AND FOSTERS CREATIVITY, INTEGRITY, GROWTH AND TEAMWORK.
  83. 83. 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.
  84. 84. A2C SOLUTION ENGAGEMENT STRUCTURES • TECHNOLOGY STRATEGY & ROADMAP FORMULATION • NEEDS & READINESS ASSESSMENT • PACKAGE & PLATFORM SELECTIONS • PROOF OF CONCEPT IMPLEMENTATION • REQUIREMENTS DISCOVERY & SPECIFICATIONS • PROGRAM/PROJECT MANAGEMENT • FULL LIFE CYCLE & APPLICATION DEVELOPMENT • INFRASTRUCTURE & FACILITIES INITIATIVES • MANAGED SERVICES & MAINTENANCE SUPPORT 8 6
  85. 85. 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 STRATEGIES • BUSINESS ARCHITECTURE & OPTIMIZATION PRACTICE UTILIZES “SIX SIGMA LEAN” METHODOLOGIES TO ANALYZE, RE-ENGINEER AND AUTOMATE OUR CLIENT‟S 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 PERFORMANCE • PROGRAM 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 STANDARDS 8 7
  86. 86. 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 ENTERPRISE • A2C'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 PLATFORM 8 8
  87. 87. LET A2C HELP WITH ALL YOUR BUSINESS SOLUTIONS
  88. 88. CONNECT TO A2C For Further information on the Agile Data Warehouse Design please contact: John DiPietro, CTO or Jim Stagnitto, Practice Director of Information Services a2c.com a2c Philadelphia 1801 Market Street Suite 2430 Philadelphia, PA 19103 215-789-4816 contact: Joe Cattie JCattie@a2c.com a2c Boston 100 Grandview Road Suite 215 Braintree, MA 02184 781-848-0005 contact: Scott King SKing@a2c.com a2c New York 401 Greenwich Street 3rd Floor New York, NY 10013 212-913-0933 contact: John DiPietro JDiPietro@a2c.com

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