Data Warehousing, Analytics, BI, Meta-Integration Overview

Welcome!
TITLE

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

Date: July 10, 2012
Time: 2:00 PM ET
Presented by: Dr. Peter Aiken

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07/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!

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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. Turns out that
proper analysis at this stage makes actual
technology selection far more accurate.
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Ask questions and submit your content
comments: #dataed
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LinkedIn Group: Join the Discussion
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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
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Data Warehousing,
Analytics, BI,
Meta-Integration
Technologies

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

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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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

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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Data Management

PRODUCED

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Data Management
Manage data coherently.

Data
Program

Coordina;on Share data across boundaries.
Organiza;onal

Data
Integra;on

Data
Data

Stewardship Development

Assign responsibilities for data.
Engineer data delivery systems.

Data
Support

Opera;ons
Maintain data availability.

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Data Management

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Summary: Data Warehousing & Business Intelligence Management

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TITLE
Outline
they important?
failures
Tweeting now:
#dataed

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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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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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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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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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Example: Set Analysis

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

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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
--not only discovering new insights, but successfully implementing them
--making a significant mark on a growing company
CarMax Example Job Posting
--developing the fundamental skills for a rewarding business career

--solving original, wide-ranging, and open-ended business problems
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
--not only discovering new insights, but successfully implementing them
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?
--making a significant mark on a growing company
-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?
--developing the fundamental skills for a rewarding business career
-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
own an area of the business and will be expected to improve it
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.
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BY CLASSIFICATION
http://www.seas.virginia.edu/careerdevelopment/index.php?option=com_careerfairstudent&task=detailView&employerId=216&eventId=3 DATE SLIDE
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 7/10/2012
24 - datablueprint.com
07/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved! 8/2/2010 © Copyright this and previous years by Data Blueprint - all rights reserved! 20

Operations Research
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• 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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TITLE
Outline
they important?
failures
Tweeting now:
#dataed

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Indiana Jones: Raiders Of The Lost Ark

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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
PRODUCED

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Top 10 Data Warehouse Failure Causes
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.tdan.com
PRODUCED

TITLE
Outline
they important?
failures
Tweeting now:
#dataed

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Health Care Provider Data Warehouse
• 1.8 million members
• 1.4 million providers
• 800,000 providers no key
• 2.2% prov_number = 9 digits (required)
• 29% prov_ssn ≠ 9 digits
• 1 User
"I
can
take
a
roomful
of

MBAs
and
accomplish

this
analysis
faster!"
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Basic Data Warehouse Analysis
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• Emphasis on
the cube
• Permits
different users
to "slice and
dice" subsets of
data
• Viewing from
different
perspectives

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Warehouse Analysis
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• Users can "drill"
anywhere
• Entire collection is
accessible
• Summaries to
transaction-level
detail

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Oracle

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Corporate Information Factory Architecture

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Kimball's DW Chess Pieces

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MetaMatrix Integration Example

• EII Enterprise
Information Integration
– between ETL and EAI -
delivers tailored views
of information to users
at the time that it is
required

36 - datablueprint.com 7/13/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

Linked Data

Linked Data is about using the Web to connect related data that wasn't
previously linked, or using the Web to lower the barriers to linking data
currently linked using other methods. More speciﬁcally, Wikipedia
deﬁnes Linked Data as "a term used to describe a recommended best
practice for exposing, sharing, and connecting pieces of data,
information, and knowledge on the Semantic Web using URIs and RDF."

linkeddata.org
37 - datablueprint.com 7/13/2012 © Copyright this and previous years by Data Blueprint - all rights reserved!

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Multiple Sources of (for example) Customer Data
Finance
Applica.on
(3rd
GL,
batch

Payroll
Data system,
no
source)
(database)
Payroll
Applica.on Finance
(3rd
GL) Data
(indexed)

Marke.ng
Data Marke.ng
Applica.on
(external
database) (4rd
GL,
query
facili.es,

no
repor.ng,
very
large)

Personnel
Data
(database)

Personnel
App.
(20
years
old,
un-‐normalized
data) Mfg.
Data
R
&
D
Data (home
grown
database) Mfg.
Applica.ons
(raw)
(contractor
supported)
R&
D
Applica.ons
(researcher
supported,
no
documenta.on)
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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

Problema)c
Data
Quality
PRODUCED

hOp://www.cio.com/ar.cle/150450/Five_Key_Business_Intelligence_Trends_You_Need_to_Know?page=2&taxonomyId=3002

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.
PRODUCED

TITLE
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
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Overview of CWM Metamodel
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Warehouse
Warehouse Warehouse
Management Process Opera.on

Analysis Data
Informa.on Business
Transforma.on OLAP
Mining Visualiza.on Nomenclature

Resources Object-‐
Record-‐ Mul.
Oriented Rela.onal XML
(ObjectModel)
Oriented Dimensional

Foundation Business Data Keys Type So`ware
Expressions
Informa.on Types Index Mapping Deployment

ObjectModel
(Core,
Behavioral,
Rela.onships,
Instance)

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

TITLE
Outline
they important?
failures
Tweeting now:
#dataed

PRODUCED

TITLE
Data Warehousing, Analytics, BI, Meta-Integration Technologies

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Goals and Principles
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
• Understand BI information needs

• Define and maintain the DW/BI
architecture

• Process data for BI

• Implement data warehouse/data marts

• Implement BI tools and user interfaces

• Monitor and tune DW processes

• Monitor and tune BI activities and performance
PRODUCED

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Primary Deliverables
• DW/BI Architecture
• Data warehouses, marts,
cubes etc.
• Dashboards-scorecards
• Analytic applications
• Files extracts (for data mining, etc.)
• BI tools and user environments
• Data quality feedback mechanism/loop
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Roles and Responsibilities
Suppliers: Consumers:
• Executives/managers • Application Users
• Subject Matter Experts • BI and Reporting
• Data governance council Users
• Information consumers • Application
• Data producers Developers and
Architects
• Data architects/analysts
• 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
• ETL
• Change Management Tools
• Data Modeling Tools
• Data Profiling Tools
• Data Cleansing Tools
• Data Integration Tools
• Reference Data Management Applications
• Master Data Management Applications
• Process Modeling Tools
• Meta-data Repositories
• Business Process and Rule Engines
PRODUCED

TITLE
Outline
they important?
failures
Tweeting now:
#dataed

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Guiding Principles
1. Obtain executive commitment and
support.
2. Secure business SMEs.
3. Be business focused and driven. Let
the business drive the prioritization.
4. Demonstrate data quality is
essential.
5. Provide incremental value.
6. Transparency and self service.
7. One size does not fit all: Find the right tools and products for each
of your segments.
8. Think and architect globally, act and build locally.
9. Collaborate with and integrate all other data initiatives, especially
those for data governance, data quality and metadata.
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.
hEp://www.agiledata.org/essays/dataWarehousingBestPrac;ces.html

PRODUCED

TITLE
Outline
they important?
failures
Tweeting now:
#dataed

PRODUCED

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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

PRODUCED

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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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Data Warehousing, Analytics, BI, Meta-Integration Overview

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