Weitere ähnliche Inhalte Ähnlich wie Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies (20) Mehr von Data Blueprint (20) Kürzlich hochgeladen (20) Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies1. Welcome!
TITLE
Data Warehousing, Analytics, BI and
Meta-Integration Technologies Webinar
Date:
July 10, 2012
Time:
2:00 PM
ET
Presented by: Dr. Peter
Aiken
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2. TITLE
Commonly Asked Questions
1) Will I get copies of the slides after the
event?
YES
2) Is this being recorded so I can view it
afterwards?
YES
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3. TITLE
Live Twitter Feed & Follow Us on Facebook
Join the conversation on Twitter! www.facebook.com/datablueprint
Follow us @datablueprint and Post questions and comments
@paiken
Find industry news, insightful
Ask questions and submit your content
comments: #dataed
and event updates
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4. LinkedIn Group: Join the Discussion
TITLE
New Group:
Data Management & Business Intelligence
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5. TITLE
Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized thought-leader in
the data management field with more than 30
years of experience
• Recipient of the 2010 International Stevens
Award
• Founding Director of Data Blueprint
(http://datablueprint.com)
• Associate Professor of Information Systems
at Virginia Commonwealth University
(http://vcu.edu)
• President of DAMA International (http://dama.org)
• DoD Computer Scientist, Reverse Engineering Program Manager/
Office of the Chief Information Officer
• Visiting Scientist, Software Engineering Institute/Carnegie Mellon
University
• 7 books and dozens of articles
• Experienced w/ 500+ data management practices in 20 countries
#dataed
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6. Data Warehousing,
Analytics, BI,
Meta-Integration
Technologies
Data Warehousing, Analytics, BI, Meta-Integration Technologies
7/10/2012
8. Data Warehousing,
Analytics, BI,
Meta-Integration
Technologies
Data Warehousing, Analytics, BI, Meta-Integration Technologies
7/10/2012
9. Data Warehousing,
Analytics, BI,
Meta-Integration
Technologies
Data Warehousing, Analytics, BI, Meta-Integration Technologies
7/10/2012
10. TITLE
Abstract: DW, Analytics, BI, Meta-Integration Technologies
Meta-integration is considered data warehousing by
some, while others describe it as data virtualization.
This presentation provides an overview of meta-
integration starting with organizational requirements.
We will discuss how meta-models can be used to jump-
start organizational efforts. Participants will understand
the strengths and weaknesses of various technological
capabilities, and the key role of data quality in all of
them.
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11. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
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12. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
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13. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Data
Management
Functions
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14. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
Data
Management
Functions
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15. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
Data
Management
Functions
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16. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
Data
Management
Functions
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17. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization
Data
Management
Functions
PRODUCED BY CLASSIFICATION DATE SLIDE
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
18. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization
• Organized around
several
environmental
elements
Data
Management
Functions
PRODUCED BY CLASSIFICATION DATE SLIDE
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
19. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization
• Organized around
several
environmental
elements
Data
Management
Functions
PRODUCED BY CLASSIFICATION DATE SLIDE
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
20. TITLE
The DAMA Guide to the Data Management Body of Knowledge
PRODUCED BY CLASSIFICATION DATE SLIDE
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21. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Environmental Elements
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22. TITLE
The DAMA Guide to the Data Management Body of Knowledge
Amazon:
http://
www.amazon.com/
DAMA-Guide-
Management-
Knowledge-DAMA-
DMBOK/dp/
0977140083
Or enter the terms
"dama dm bok" at the
Amazon search
engine
Environmental Elements
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23. TITLE
Data Management
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24. TITLE
Data Management
Data
Program
Coordination
Organizational
Data
Integration
Data Data
Stewardship Development
Data Support
Operations
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25. TITLE
Data Management
Manage data coherently.
Data
Program
Coordination
Organizational
Data
Integration
Data Data
Stewardship Development
Data Support
Operations
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26. TITLE
Data Management
Manage data coherently.
Data
Program
Coordination Share data across boundaries.
Organizational
Data
Integration
Data Data
Stewardship Development
Data Support
Operations
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27. TITLE
Data Management
Manage data coherently.
Data
Program
Coordination Share data across boundaries.
Organizational
Data
Integration
Data Data
Stewardship Development
Assign responsibilities for data.
Data Support
Operations
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28. TITLE
Data Management
Manage data coherently.
Data
Program
Coordination Share data across boundaries.
Organizational
Data
Integration
Data Data
Stewardship Development
Assign responsibilities for data.
Engineer data delivery systems.
Data Support
Operations
PRODUCED BY CLASSIFICATION DATE SLIDE
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29. TITLE
Data Management
Manage data coherently.
Data
Program
Coordination Share data across boundaries.
Organizational
Data
Integration
Data Data
Stewardship Development
Assign responsibilities for data.
Engineer data delivery systems.
Data Support
Operations
Maintain data availability.
PRODUCED BY CLASSIFICATION DATE SLIDE
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30. TITLE
Data Management
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31. TITLE
Summary: Data Warehousing & Business Intelligence Management
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32. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
PRODUCED BY CLASSIFICATION DATE SLIDE
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
33. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 15
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
34. TITLE
DW, Analytics, BI, Meta-Integration Technologies
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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35. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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36. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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37. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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38. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
Business Intelligence
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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39. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
Business Intelligence
• Dates at least to 1958
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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40. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
Business Intelligence
• Dates at least to 1958
• Support better business
decision making
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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41. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
Business Intelligence
• Dates at least to 1958
• Support better business
decision making
• Technologies, applications and
practices for the collection,
integration, analysis, and
presentation of business
information
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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42. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
Business Intelligence
• Dates at least to 1958
• Support better business
decision making
• Technologies, applications and
practices for the collection,
integration, analysis, and
presentation of business
information
• Also described as decision
support
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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43. TITLE
DW, Analytics, BI, Meta-Integration Technologies
Definitions
• Beyond the nuts and bolts of
data management
• Analysis of information that had
not been integrated previously
Business Intelligence
• Dates at least to 1958
• Support better business
decision making
• Technologies, applications and
practices for the collection,
integration, analysis, and Data Warehousing
presentation of business
• Operational extract, cleansing,
information
• Also described as decision transformation, load, and
support associated control processes for
integrating disparate data into a
single conceptual database
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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44. TITLE
Definitions, cont’d
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45. TITLE
Definitions, cont’d
• Study of data to discover and
understand historical patterns to
improve future performance
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46. TITLE
Definitions, cont’d
• Study of data to discover and
understand historical patterns to
improve future performance
• Use of mathematics in business
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47. TITLE
Definitions, cont’d
• Study of data to discover and
understand historical patterns to
improve future performance
• Use of mathematics in business
• Analytics closely resembles
statistical analysis and data mining
– based on modeling involving
extensive computation.
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48. TITLE
Definitions, cont’d
• Study of data to discover and
understand historical patterns to
improve future performance
• Use of mathematics in business
• Analytics closely resembles
statistical analysis and data mining
– based on modeling involving
extensive computation.
• Some fields within the area of
analytics are
– enterprise decision
management, marketing
analytics, predictive science,
strategy science, credit risk
analysis and fraud analytics.
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49. TITLE
Warehousing Definitions
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50. TITLE
Warehousing Definitions
• Inmon:
– "A subject oriented, integrated, time variant, and
non-volatile collection of summary and detailed
historical data used to support the strategic
decision-making processes of the organization."
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51. TITLE
Warehousing Definitions
• Inmon:
– "A subject oriented, integrated, time variant, and
non-volatile collection of summary and detailed
historical data used to support the strategic
decision-making processes of the organization."
• Kimball:
– "A copy of transaction data specifically structured
for query and analysis."
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52. TITLE
Warehousing Definitions
• Inmon:
– "A subject oriented, integrated, time variant, and
non-volatile collection of summary and detailed
historical data used to support the strategic
decision-making processes of the organization."
• Kimball:
– "A copy of transaction data specifically structured
for query and analysis."
• Key concepts focus on:
– Subjects
– Transactions
– Non-volatility
– Restructuring
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53. TITLE
Example: Portfolio Analysis
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54. TITLE
Example: Portfolio Analysis
• Bank accounts are of varying
value and risk
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55. TITLE
Example: Portfolio Analysis
• Bank accounts are of varying
value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.
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56. TITLE
Example: Portfolio Analysis
• Bank accounts are of varying
value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.
• Balance return on the loan
with risk of default
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57. TITLE
Example: Portfolio Analysis
• Bank accounts are of varying
value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.
• Balance return on the loan
with risk of default
• How to evaluate the portfolio as a whole?
– Least risk loan may be to the very wealthy, but there are a very
limited number
– Many poor customers, but greater risk
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58. TITLE
Example: Portfolio Analysis
• Bank accounts are of varying
value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.
• Balance return on the loan
with risk of default
• How to evaluate the portfolio as a whole?
– Least risk loan may be to the very wealthy, but there are a very
limited number
– Many poor customers, but greater risk
• Solution may combine types of analyses
– When to lend, interest rate charged
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59. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
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60. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
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61. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
PRODUCED BY CLASSIFICATION DATE SLIDE
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62. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 21
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
63. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 21
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
64. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 21
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
65. TITLE
Example: Set Analysis
from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 21
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
66. 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 Best
Companies to Work For.” And we are hiring talented individuals who are interested in:--solving original, wide-ranging, and open-ended business problems
TITLE
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
CarMax Example Job Posting
If that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many of
whom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, political
science). These analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, what
should we pay for it, what should we price it for?
-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?
-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales?
-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand
-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond?
-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?
-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?
Even early in your career at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruit
used data analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 months
with the company. That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop the
fundamental and advanced skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP of
Strategy and Analysis started his career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who have
chosen to pursue a business degree have enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.
Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailer
in the country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, is
bigger than the home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores and
achieve $25+ billion in annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talented
associates, a healthy work-life balance, and excellent compensation and benefits.
An ideal candidate will have
--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition such
as scholarships, awards, honor societies
-- Passion for business and desire to develop into a strong business leader
We encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us at
college_recruiting@carmax.com.
http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3
PRODUCED BY CLASSIFICATION DATE SLIDE
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07/10/12
24 © Copyright this and previous years by Data Blueprint - all rights reserved!
67. 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 Best
Companies to Work For.” And we are hiring talented individuals who are interested in:--solving original, wide-ranging, and open-ended business problems
TITLE
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
CarMax Example Job Posting
If that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many of
whom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, political
science). These analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, what
should we pay for it, what should we price it for?
--solving original, wide-ranging, and open-ended business problems
-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?
-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales?
--not only discovering new insights, but successfully implementing them
-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand
-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond?
--making a significant mark on a growing company
-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?
-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?
Even early in your career --developing the fundamental skills for a rewarding business career
at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruit
used data analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 months
with the company. That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop the
fundamental and advanced skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP of
Strategy and Analysis started his career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who have
chosen to pursue a business degree have enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.
Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailer
in the country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, is
bigger than the home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores and
achieve $25+ billion in annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talented
associates, a healthy work-life balance, and excellent compensation and benefits.
An ideal candidate will have
--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition such
as scholarships, awards, honor societies
-- Passion for business and desire to develop into a strong business leader
We encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us at
college_recruiting@carmax.com.
http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 22
- datablueprint.com 8/2/2010 © Copyright this and previous years by Data Blueprint - all rights reserved!
07/10/12
24 © Copyright this and previous years by Data Blueprint - all rights reserved!
68. 15 years ago, CarMax started as a way to make the car buying experience simple, fair, and fun. Today CarMax is a FORTUNE 500 retailer and one of FORTUNE’s “100 Best
Companies to Work For.” And we are hiring talented individuals who are interested in:--solving original, wide-ranging, and open-ended business problems
TITLE
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
--developing the fundamental skills for a rewarding business career
CarMax Example Job Posting
If that sounds like you, the Strategy Analyst position is the unique opportunity you’ve been looking for. The strategy team at CarMax currently consists of over 40 analysts, many of
whom are recent college graduates from top schools with a variety of academic backgrounds (computer science, economics, English, engineering, journalism, math, political
science). These analysts lead advances and decisions in several key business areas:-Inventory and pricing—what is the optimal selection of inventory, how do we acquire it, what
should we pay for it, what should we price it for?
--solving original, wide-ranging, and open-ended business problems
-Expansion planning—which markets should we enter and how do we store those markets? Will each $10-30 million store investment generate a sufficient economic return?
-Credit strategy—how can our bank (CarMax Auto Finance) approve more customers for loans and convert more approvals to sales?
--not only discovering new insights, but successfully implementing them
-Marketing and consumer insight—how do we reach our customers, increase traffic to our stores, and best use the internet to drive sales and build our brand
-Industry and competitive research—what middle- and long-term risks are we exposed to, and how best do we prepare to respond?
--making a significant mark on a growing company
-Production—how do we increase vehicle reconditioning quality while reducing cost and production time?
-Sales process and workforce—what is the best way to serve customers in our stores, and how do we manage, motivate and compensate our sales team?
Even early in your career --developing the fundamental skills for a rewarding business career
at CarMax, you will have the responsibility to own an area of the business and will be expected to improve it. For example, one undergraduate recruit
used data analysis to reformulate our retail pricing strategy, pitched and sold his idea to the senior executive team, and implemented a new system nationwide in his first 6 months
with the company. That is the kind of impact you can make at CarMax. And as you do this, you will work closely with the senior executives and analytical managers to develop the
fundamental and advanced skills that underpin a successful career in business. In fact, most of our managers in the strategy group started at CarMax as analysts, and our VP of
Strategy and Analysis started his career here through our undergraduate recruiting program. While an MBA is not required to advance or contribute at CarMax, analysts who have
chosen to pursue a business degree have enjoyed superior acceptance rates at their first choice schools, including Harvard, Chicago, UVa, Columbia, and Duke.
own an area of the business and will be expected to improve it
Your opportunities to develop, contribute, and lead as an analyst at CarMax are as great as the company’s opportunity to grow. While CarMax is already the largest used car retailer
in the country (with over $8 billion in sales and over 90 superstores across the country), we have only 2% of the 1 to 6-year-old used car market, which, at $280 billion annually, is
bigger than the home improvement or consumer electronics industries. CarMax is already growing at 15% a year, and over the next 10 years plans to have 250-300 stores and
achieve $25+ billion in annual sales. As an analyst, you can be an integral part of that growth, all while enjoying a casual and friendly environment, a diverse group of talented
associates, a healthy work-life balance, and excellent compensation and benefits.
An ideal candidate will have
--Demonstrated top caliber analytic and problem solving skills --History of achievement demonstrated by top 15% GPA, with a quantitative major(s), and/or other recognition such
as scholarships, awards, honor societies
-- Passion for business and desire to develop into a strong business leader
We encourage you to apply. For more information, please visit us at the career fair, on our website (www.carmax.com/collegerecruiting), or email us at
college_recruiting@carmax.com.
http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 22
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07/10/12
24 © Copyright this and previous years by Data Blueprint - all rights reserved!
69. Operations Research
TITLE
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
70. Operations Research
TITLE
• Interdisciplinary branch of applied mathematics and formal science
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71. Operations Research
TITLE
• Interdisciplinary branch of applied mathematics and formal science
• Uses methods such as mathematical modeling, statistics, and
algorithms to arrive at optimal or near optimal solutions
PRODUCED BY CLASSIFICATION DATE SLIDE
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
72. Operations Research
TITLE
• Interdisciplinary branch of applied mathematics and formal science
• Uses methods such as mathematical modeling, statistics, and
algorithms to arrive at optimal or near optimal solutions
• Typically concerned with optimizing the maxima (profit, assembly
line performance, crop yield, bandwidth, etc) or minima (loss, risk,
etc.) of some objective function
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DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 23
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
73. Operations Research
TITLE
• Interdisciplinary branch of applied mathematics and formal science
• Uses methods such as mathematical modeling, statistics, and
algorithms to arrive at optimal or near optimal solutions
• Typically concerned with optimizing the maxima (profit, assembly
line performance, crop yield, bandwidth, etc) or minima (loss, risk,
etc.) of some objective function
• Operations research helps management achieve its goals using
scientific methods http://en.wikipedia.org/wiki/Operations_research
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
74. TITLE
Indiana Jones: Raiders Of The Lost Ark
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
75. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 25
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
76. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 25
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
77. TITLE
Top Causes of Data Warehouse Failure
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
78. TITLE
Top Causes of Data Warehouse Failure
• Poor Quality Data
– Many more values of
gender code than (M/F)
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
79. TITLE
Top Causes of Data Warehouse Failure
• Poor Quality Data
– Many more values of
gender code than (M/F)
• Incorrectly Structured
Data
– Providing the correct
answer to the wrong
question
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 26
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
80. TITLE
Top Causes of Data Warehouse Failure
• Poor Quality Data
– Many more values of
gender code than (M/F)
• Incorrectly Structured
Data
– Providing the correct
answer to the wrong
question
• Bad Warehouse Design
– Overly complex
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
81. TITLE
Polling Question #1
What is the #1 reason why Data Warehouses Fail?
1. Functions and capabilities not
implemented
2. The project is over budget
3. Inability to expand
4. Too complicated for users
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82. TITLE
Top 10 Data Warehouse Failures
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
83. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
84. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
from The Data Administration Newsletter, www.dtdan.com
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DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
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85. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
86. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
87. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
5. Unacceptable performance
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
88. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
5. Unacceptable performance
6. Poor availability
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
89. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
5. Unacceptable performance
6. Poor availability
7. Inability to expand
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
90. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
5. Unacceptable performance
6. Poor availability
7. Inability to expand
8. Poor quality data/reports
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
91. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
5. Unacceptable performance
6. Poor availability
7. Inability to expand
8. Poor quality data/reports
9. Too complicated for users
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
92. TITLE
Top 10 Data Warehouse Failures
1. The project is over budget
2. Slipped schedule
3. Functions and capabilities not implemented
4. Unhappy users
5. Unacceptable performance
6. Poor availability
7. Inability to expand
8. Poor quality data/reports
9. Too complicated for users
10. Project not cost justified
from The Data Administration Newsletter, www.dtdan.com
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 28
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
93. TITLE
Outline
1. Data management overview
2. What are DW, analytics, BI and meta-
integration technologies and why are
they important?
3. Top 10 causes of data warehouse
failures
4. DW & architecture focus
5. Business intelligence focus
6. The use of meta models
7. DW, analytics & BI building blocks
8. Guiding principles & best practices
Tweeting now:
9. Take aways, references and Q&A
#dataed
PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 29
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!
Hinweis der Redaktion \n \n \n \n \n 1977-2010=33 years\n 1977-2010=33 years\n 1977-2010=33 years\n 1977-2010=33 years\n 1977-2010=33 years\n 1977-2010=33 years\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than on the overall size of the convoy. Their conclusion, therefore, was that a few large convoys are more defensible than many small ones.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n