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

1. AGILE DATA WAREHOUSE DESIGN WITH BIG DATA
John DiPietro & Jim Stagnitto
1

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. 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. 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. 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. AGILE DW DESIGN OVERVIEW
6

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. 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. 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. 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. How
How
Many
Why
11

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. KIMBALL DIMENSIONAL DW
Dimensional BI Semantic Layer
Dimensional Data Warehouse
Data Movement / Integration
Source Data
(Structured)
13

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. 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. 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. DATA VAULT
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Data Vault
Data Movement / Integration
Source Data
(Structured)
17

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. 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. 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. 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. TYPICAL CLICKSTREAM “PAGE VIEW” DIMENSIONAL
MODEL
What
When
What
Who
Why
22

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. 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. AGILE DW DESIGN OVERVIEW
25

26. THE FIRST DIMENSIONAL MODELER:
R.K.
Ralph Kimball?
Rudyard Kipling
26

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. THE
7WS
Framework

29. How
How
Many
Why

30. HOW DID WE GET HERE?

31. DW ARCHITECTURES: A BRIEF HISTORY
Corporate Information
Factory
Undisciplined
Dimensional
Dimensional Bus
Architecture
Data-Driven Analysis
Report-Driven Analysis
Process-Driven Analysis

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. How
Why
BEAM
How
Many
Business Event Analysis & Modeling

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. HOW
do you design a data warehouse?

36. TECH DESIGN ARTIFACTS?

37. OK, NOW VALIDATE WITH BUSINESS…

38. WHY
Agile Data Warehousing?

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. 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. 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. MODELSTORMING:
QUICK
Interactive
Inclusive
Data
Modeler
BI Stakeholders
Fun

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. REQUIREMENTS = DESIGN
4

45. COLLABORATION AT
EVERY STEP

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. WHAT
kind of model?

49. 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

51. MODELING BY ABSTRACTION

52. MODELING BY EXAMPLE:

53. AGILE DW DESIGN
PROCESS
5

54. COLLABORATIVE / CONVERSATIONAL DESIGN
Who does what?
“Customers buy products”
BEAM✲
Modeler
Subjects Verb Objects
BI
Users

55. 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

56. “Spreadsheet”-like Models
Event Table Name (filled in later)
Subject Column Name
Verb
Object Column Name
Interrogative
Details
Example Data (4-6
rows)

57. 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

58. 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”

59. THOUGHTFUL EXAMPLE DATA:
Detailed ETL
Specification

60. IDENTIFY EVENT TYPE EARLY

61. 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

62. CAPTURE WHEN DETAILS
When do Customers order
Products?
“On the Order Date”
BEAM✲
Modeler
BI Users

63. ANY OTHER WHENS ?

64. ANY OTHER WHOS ?

65. AND SO ON...

66. 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

67. MODELING DIMENSIONS:

68. ANNOTATE W TARGETED DATA PROFILING:

69. PROCEED THROUGH THE BUSINESS PROCESS
VALUE CHAIN:

70. COLLABORATIVE DIMENSION CONFORMANCE:

71. IDENTIFY HIERARCHY TYPES:

72. GRAPHICALLY DEPICT HIERARCHIES:

73. VISUALIZE THE HIERARCHIES

74. PAINT THE ORGANIZATION

75. PROTOTYPE! NOT “DATA MODEL REVIEW”

76. 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

77. IF YOU HAVE BEEN AFFECTED BY
ANY OF THE ISSUES RAISED
IN THIS PRESENTATION…

78. AGILE DATA WAREHOUSE DESIGN
LAWRENCE CORR, JIM STAGNITTO,
DECISION PRESS, NOVEMBER 2011

79. QUESTIONS/COMMENTS?
CONTACT: JIM STAGNITTO
OR JOHN DIPIETRO
215-789-4816

80. A2C CORPORATE OVERVIEW &
INDUSTRY EXPERIENCE
8
0

81. 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

82. 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

83. 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.

84. 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.

85. 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.

86. 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
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87. 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
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88. 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
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89. LET A2C HELP WITH ALL YOUR
BUSINESS SOLUTIONS

90. 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