Data-Ed Online: Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration Technologies

Welcome!
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Data Warehousing, Analytics, BI and
Meta-Integration Technologies Webinar

Date:
July 10, 2012
Time:

2:00 PM
ET
Presented by: Dr. Peter
Aiken

PRODUCED BY CLASSIFICATION DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012 1
07/10/12 © Copyright this and previous years by Data Blueprint - all rights reserved!

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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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New Group:
Data Management & Business Intelligence


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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

Data Warehousing,
Analytics, BI,
Meta-Integration
Technologies

Data Warehousing, Analytics, BI, Meta-Integration Technologies
7/10/2012

7/10/2012

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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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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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The DAMA Guide to the Data Management Body of Knowledge

Data
Management
Functions

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Published by DAMA
International

Data
Management
Functions

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Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)

Data
Management
Functions

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Published by DAMA
International
Managers (40
chapters worldwide)
DMBoK organized
around

Data
Management
Functions

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Published by DAMA
International
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization

Data
Management
Functions

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Published by DAMA
International
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

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Environmental Elements

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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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Data Management


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Data Management

Data
Program
Coordination
Organizational
Data
Integration

Data Data
Stewardship Development

Data Support
Operations


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Data Management
Manage data coherently.

Data
Program
Coordination
Organizational
Data
Integration

Data Data

Data Support
Operations


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Data Management

Data
Program
Coordination Share data across boundaries.
Organizational
Data
Integration

Data Data

Data Support
Operations


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Data Management

Data
Program
Organizational
Data
Integration

Data Data

Assign responsibilities for data.

Data Support
Operations


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Data Management

Data
Program
Organizational
Data
Integration

Data Data

Engineer data delivery systems.

Data Support
Operations


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Data Management

Data
Program
Organizational
Data
Integration

Data Data

Engineer data delivery systems.

Data Support
Operations
Maintain data availability.


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Data Management


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Summary: Data Warehousing & Business Intelligence Management


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Outline
they important?
failures
Tweeting now:
#dataed


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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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Definitions


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Definitions
• Beyond the nuts and bolts of
data management


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Definitions
data management
• Analysis of information that had
not been integrated previously


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Definitions
data management
Business Intelligence


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Definitions
data management
• Dates at least to 1958


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Definitions
data management
• Support better business
decision making


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Definitions
data management
decision making
• Technologies, applications and
practices for the collection,
integration, analysis, and
presentation of business
information


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Definitions
data management
decision making
integration, analysis, and
information
• Also described as decision
support


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Definitions
data management
decision making
integration, analysis, and Data Warehousing
• Operational extract, cleansing,
information
• Also described as decision transformation, load, and
support associated control processes for
integrating disparate data into a
single conceptual database

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Definitions, cont’d


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• Study of data to discover and
understand historical patterns to
improve future performance


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• Use of mathematics in business


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• Analytics closely resembles
statistical analysis and data mining
– based on modeling involving
extensive computation.


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• 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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Warehousing Definitions


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• 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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• Inmon:
• Kimball:
– "A copy of transaction data specifically structured
for query and analysis."


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• Inmon:
• 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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Example: Portfolio Analysis


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• Bank accounts are of varying
value and risk


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value and risk
• Cube by
– Social status
– Geographical location
– Net value, etc.


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value and risk
• Cube by
– Social status
– Net value, etc.
• Balance return on the loan
with risk of default


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value and risk
• Cube by
– Social status
– Net value, etc.
• 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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value and risk
• Cube by
– Social status
– Net value, etc.
• 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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Example: Set Analysis

from MicroStrategy, Better Business Decisions Every Day: Integrating Business Reporting & Analysis


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Example: Set Analysis



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
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--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

- 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!

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--solving original, wide-ranging, and open-ended business problems

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




07/10/12

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--solving original, wide-ranging, and open-ended business problems

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

own an area of the business and will be expected to improve it



07/10/12

Operations Research
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Operations Research
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• Interdisciplinary branch of applied mathematics and formal science


Operations Research
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• Uses methods such as mathematical modeling, statistics, and
algorithms to arrive at optimal or near optimal solutions


Operations Research
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• 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
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• 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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Indiana Jones: Raiders Of The Lost Ark


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Outline
they important?
failures
Tweeting now:
#dataed


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Top Causes of Data Warehouse Failure


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• Poor Quality Data
– Many more values of
gender code than (M/F)


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• Incorrectly Structured
Data
– Providing the correct
answer to the wrong
question


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• Incorrectly Structured
Data
– Providing the correct
answer to the wrong
question
• Bad Warehouse Design
– Overly complex


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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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Top 10 Data Warehouse Failures

from The Data Administration Newsletter, www.dtdan.com

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2. Slipped schedule


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2. Slipped schedule
3. Functions and capabilities not implemented


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2. Slipped schedule
4. Unhappy users


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2. Slipped schedule
4. Unhappy users
5. Unacceptable performance


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2. Slipped schedule
4. Unhappy users
6. Poor availability


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2. Slipped schedule
4. Unhappy users


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2. Slipped schedule
4. Unhappy users
8. Poor quality data/reports


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2. Slipped schedule
4. Unhappy users


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2. Slipped schedule
4. Unhappy users
10. Project not cost justified


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Outline
they important?
failures
Tweeting now:
#dataed


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Health Care Provider Data Warehouse


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• 1.8 million members


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• 1.4 million providers


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• 800,000 providers no key


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• 2.2% prov_number = 9 digits (required)


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• 29% prov_ssn ≠ 9 digits


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• 1 User


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• 1 User
"I can take a
roomful of MBAs
and accomplish this
analysis faster!"
CLASSIFICATION DATE SLIDE
PRODUCED BY

Basic Data Warehouse Analysis
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• Emphasis on
the cube


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• Emphasis on
the cube
• Permits
different users
to "slice and
dice" subsets of
data


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• Emphasis on
the cube
• Permits
different users
to "slice and
dice" subsets of
data
• Viewing from
different
perspectives


Warehouse Analysis
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Warehouse Analysis
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• Users can "drill"
anywhere


Warehouse Analysis
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anywhere
• Entire collection
is accessible


Warehouse Analysis
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anywhere
• Entire collection
is accessible
• Summaries to
transaction-level
detail


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Corporate Information Factory Architecture


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Oracle


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Kimball's DW Chess Pieces


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Multiple Sources of (for example) Customer Data

R& D Applications
(researcher supported, no documentation)

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Multiple Sources of (for example) Customer Data
Finance Application
(3rd GL, batch
Payroll Data system, no source)
(database)
Payroll Application Finance
(3rd GL) Data
(indexed)

Marketing Data Marketing Application
(external database) (4rd GL, query facilities,
no reporting, very large)

Personnel Data
(database)

Personnel App.
(20 years old,
un-normalized data) Mfg. Data
R&D
Data (home grown
(raw) database) Mfg. Applications
(contractor supported)
R& D Applications
(researcher supported, no documentation)

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Outline
they important?
failures
Tweeting now:
#dataed


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Styles of Business Intelligence


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Business Intelligence Features


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Business Intelligence Features

Problematic Data Quality

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Polling Question #2
Which is a key business intelligence trend?

1. Market Consolidation Means a lot more Choices for
Business Intelligence Users.
2. There's so much data, and too much insight.
3. The Convergence of Structured and Unstructured
Data Will Create Better Business Intelligence.
4. Applications Will Not Provide New Views of Business
Intelligence Data.


5 Key Business Intelligence Trends
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1. There's so much data, but too little
insight. More data translates to a
greater need to manage it and make
it actionable.
2. Market Consolidation Means Fewer
Choices for Business Intelligence
Users.
3. Business Intelligence Expands from the Board Room to the Front
Lines. Increasingly, business intelligence tools will be available at
all levels of the corporation
4. The Convergence of Structured and Unstructured Data Will
Create Better Business Intelligence.
5. Applications Will Provide New Views of Business Intelligence
Data. The next generation of business intelligence applications is
moving beyond the pie charts and bar charts into more visual
depictions of data and trends. http://www.cio.com/article/150450/
Five_Key_Business_Intelligence_Trends_You_Need_to_Know?
page=2&taxonomyId=3002

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Outline
they important?
failures
Tweeting now:
#dataed


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Meta Data Models

Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission


Overview of CWM Metamodel
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http://www.omg.org/technology/documents/modeling_spec_catalog.htm

Overview of CWM Metamodel
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Warehouse
Warehouse Warehouse
Management Process Operation

Analysis Data Information Business
Transformation OLAP
Mining Visualization Nomenclature

Resources Object- Record- Multi
Oriented Relational XML
Oriented Dimensional
(ObjectModel)

Foundation Business Data Keys Type Software
Expressions Index Mapping Deployment
Information Types

ObjectModel
(Core, Behavioral, Relationships, Instance)

http://www.omg.org/technology/documents/modeling_spec_catalog.htm

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Outline
they important?
failures
Tweeting now:
#dataed


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      
      
      
      
      
      



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Goals and Principles



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1. To support and enable
effective business analysis
and decision making by
knowledgeable workers



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1. To support and enable
effective business analysis
and decision making by
knowledgeable workers
2. To build and maintain the
environment/infrastructure
to support business
intelligence activities,
specifically leveraging all
the other data management
functions to cost effectively
deliver consistent integrated
data for all BI activities


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Activities


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Activities
• Understand BI information needs


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Activities
• Define and maintain the DW/BI
architecture


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Activities
architecture
• Process data for BI


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Activities
architecture
• Implement data warehouse/data marts


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Activities
architecture
• Implement BI tools and user interfaces


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Activities
architecture
• Monitor and tune DW processes


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Activities
architecture
• Monitor and tune DW processes
• Monitor and tune BI activities and performance


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Primary Deliverables


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• DW/BI Architecture


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• Data warehouses, marts,
cubes etc.


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cubes etc.
• Dashboards-scorecards


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cubes etc.
• Analytic applications


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cubes etc.
• Files extracts (for data mining, etc.)


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cubes etc.
• BI tools and user environments


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cubes etc.
• BI tools and user environments
• Data quality feedback mechanism/loop


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Roles and Responsibilities


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Suppliers:
• Executives/managers
• Subject Matter Experts
• Data governance council
• Information consumers
• Data producers
• Data architects/analysts


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Suppliers:
• Data governance council
• Information consumers
• Data producers

Participants:
• Data Stewards
• Data Architects
• Data Analysts
• Application Architects
• Data Governance Council
• Data Providers
• Other BI Professionals

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Suppliers: Consumers:
• Executives/managers • Application Users
• Subject Matter Experts • BI and Reporting
• Data governance council Users
• Information consumers • Application
Developers and
• Data producers
Architects
• Data integration
Participants: Developers and
• Executives/managers Architects
• Data Stewards • BI Vendors and
• Subject Matter Experts Architects
• Data Architects • Vendors, Customers
• Data Analysts and Partners
• Application Architects
• Data Governance Council
• Data Providers
• Other BI Professionals

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Technology


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Technology
• ETL


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Technology
• ETL
• Change Management Tools


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Technology
• ETL
• Data Modeling Tools


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Technology
• ETL
• Data Profiling Tools


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Technology
• ETL
• Data Cleansing Tools


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Technology
• ETL
• Data Integration Tools


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Technology
• ETL
• Reference Data Management Applications


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Technology
• ETL
• Master Data Management Applications


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Technology
• ETL
• Process Modeling Tools


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Technology
• ETL
• Meta-data Repositories


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Technology
• ETL
• Meta-data Repositories
• Business Process and Rule Engines

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Outline
they important?
failures
Tweeting now:
#dataed


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Guiding Principles


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Guiding Principles
1. Obtain executive commitment and
support.


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Guiding Principles
support.
2. Secure business SMEs.


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Guiding Principles
support.
3. Be business focused and driven. Let
the business drive the prioritization.


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Guiding Principles
support.
4. Demonstrate data quality is essential.


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Guiding Principles
support.
5. Provide incremental value.


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Guiding Principles
support.
6. Transparency and self service.


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Guiding Principles
support.
7. One size does not fit all: Find the right tools and products for each of
your segments.


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Guiding Principles
support.
your segments.
8. Think and architect globally, act and build locally.


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Guiding Principles
support.
your segments.
9. Collaborate with and integrate all other data initiatives, especially
those for data governance, data quality and metadata.


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Guiding Principles
support.
your segments.
10. Start with the end in mind.


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Guiding Principles
support.
your segments.
10. Start with the end in mind.
11. Summarize and optimize last, not first.


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6 Best Practices for Data Warehousing

1. Do some initial architecture
envisioning.

2. Model the details just in time (JIT).

3. Prove the architecture early.

4. Focus on usage.

5. Organize your work by requirements.

6. Active stakeholder participation.

http://www.agiledata.org/essays/
dataWarehousingBestPractices.html

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Outline
they important?
failures
Tweeting now:
#dataed


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Summary: Data Warehousing & Business Intelligence Management


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References


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Additional References
• http://www.information-management.com/infodirect/20050909/1036703-1.html
• http://www.agiledata.org/essays/dataWarehousingBestPractices.html
• http://www.cio.com/article/150450/
Five_Key_Business_Intelligence_Trends_You_Need_to_Know?
page=2&taxonomyId=3002
• http://www.computerworld.com/s/article/9228736/
Business_Intelligence_and_analytics_Conquering_Big_Data?taxonomyId=9
• http://www.enterpriseirregulars.com/5706/the-top-10-trends-for-2010-in-analytics-
business-intelligence-and-performance-management/
• http://www.itbusinessedge.com/cm/blogs/vizard/taking-the-analytics-pressure-off-the-
data-warehouse/?cs=50698
• http://www.informationweek.com/news/software/bi/240001922


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Questions?

+ =

It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.


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Upcoming Events
August Webinar:
Your Documents and Other Content:
Managing Unstructured Data
August 14, 2012 @ 2:00 PM – 3:30 PM ET
(11:00 AM-12:30 PM PT)
September Webinar:
Let’s Talk Metadata: Strategies and Successes
September 11, 2012 @ 2:00 PM – 3:30 PM ET
(11:00 AM-12:30 PM PT)
Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
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