Peter Aiken, Ph.D. & Steven MacLauchlan Data Warehousing Strategies
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Copyright 2014 by Data Blueprint
PremiseTwo types of listeners … 1. Interested in how to
approach the subject of warehousing data – Need to integrate disparate
data – Need more holistic view of
business operations – Management just discovered
data warehouses and wants you to "build one"
2. Have complex and/or messy data warehouse practices – Want to improve them
Data Warehousing Strategies
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Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present
greaterrisk(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced Data
Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA
Foundational Data Management Practices
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Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
UsesReuses
What is data management?
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Sources
Data Governance
Data Engineering
Data Delivery
DataStorage
Specialized Team Skills
Understanding the current and future data needs of an enterprise and making that data effective and efficient in supporting business activitiesAiken, P, Allen, M. D., Parker, B., Mattia, A., "Measuring Data Management's Maturity: A Community's Self-Assessment" IEEE Computer (research feature April 2007)
Data management practices connect data sources and uses in an organized and efficient manner • Storage • Engineering • Delivery • Governance
When executed, engineering, storage, and delivery implement governance
Note: does not well-depict data reuse
Maintain fit-for-purpose data, efficiently and effectively
DMM℠ Structure of 5 Integrated DM Practice Areas
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Manage data coherently
Manage data assets professionally
Data architecture implementation
Data engineering implementation
Organizational support
Dat
a M
anag
emen
t Bod
y of
Kno
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dge
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Data Management
Functions
DAMA DM BoK & CDMP
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• Data Management Body of Knowledge (DMBOK) – Published by DAMA International, the
professional association for Data Managers (40 chapters worldwide)
– Organized around primary data management functions focused around data delivery to the organization and several environmental elements
• Certified Data Management Professional (CDMP) – Series of 3 exams by DAMA International and
ICCP – Membership in a distinct group of
fellow professionals – Recognition for specialized knowledge in a
choice of 17 specialty areas – For more information, please visit:
• www.dama.org, www.iccp.org
Warehousing data in the context of data management
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Assumes you have • An overarching data strategy • A strategy for becoming
familiar with "big data technologies"
• Made a decision to not make available (integrating or storing) needed data
• Decided to increase (or decrease) the complexity of existing DM practices
• Decided to learn more about this DM BoK slice
UsesReusesSources
Data Governance
Data Engineering
Data Delivery
DataStorage
Specialized Team Skills
Data Warehousing Strategies
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1. Warehousing data in the context of data management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
Payroll Application(3rd GL)Payroll Data
(database)
R& D Applications(researcher supported, no documentation)
R & D Data (raw) Mfg. Data
(home grown database)
Mfg. Applications(contractor supported)
Finance
Data (indexed)
Finance Application(3rd GL, batch
system, no source)
Marketing Application(4rd GL, query facilities, no reporting, very large)
Marketing Data
(external database)
Personnel App.(20 years old,
un-normalized data)
Personnel Data
(database)
Typical System Evolution
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Multiple Sources of (for example)
Customer Data
Payroll Data (database)
R & D Data (raw)
Mfg. Data (home grown
database)
Finance
Data (indexed)
Marketing Data
(external database)
Personnel Data
(database)
... Then Integrate
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OrganizationalData
Payroll Data (database)
R & D Data (raw)
Mfg. Data (home grown
database)
Finance
Data (indexed)
Marketing Data
(external database)
Personnel Data
(database)
... Then Re-architect
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OrganizationalData
An organization's integration needs ...
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Software Package 1
Software Package 2
Software Package 3
Software Package 4
Software Package 5
Software Package 6
Data Architecture
... map between and across software packages
Defining Data Warehousing, BI/Analytics
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• Data Warehousing – A technology solution supporting … business capabilities
such as: query, analysis, reporting and development of these capabilities
– Analysis of information not previously integrated – Another, often new, set of organizational capabilities
• Business Intelligence (aka. decision support) – Dates at least to 1958 – Support better business decision making – Technologies, applications and practices for the collection, integration, analysis, and
presentation of business information – Understanding historical patterns in data to improve future performance – Use of mathematics in business
• Analytics (aka.) enterprise decision management, marketing analytics, predictive science, strategy science, credit risk analysis. fraud analytics - often based on computational modeling
• Reframing the question … – From: what data warehouse should we build? – To: how can data warehouse-based integration address challenges?
From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Descriptive
Ask: What happened? What is happening?
Find: Structured data
Show: Profiles, Bar/pie charts, Narrative
Predictive Ask: What will happen? Why will it happen?
Find: Structured/unstructured data
Show: Risk Profiles, Pros/Cons, Care Recs
Prescriptive Ask: What should I do? Why should I do it?
Find: Unstructured/structured data
Show: Strategic Goals, Support Recs
Hemophilia Management Analytics
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Target Isn't Just Predicting Pregnancies
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http://rmportal.performedia.com/node/1373 and http://www.predictiveanalyticsworld.com/patimes/target-really-predict-teens-pregnancy-inside-story/ http://rmportal.performedia.com/rm/paw10/gallery_01#1373
Basics
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• Users can "drill" anywhere
• Entire collection "cube" is accessible
• Summaries to transaction-level detail
Sample questions …
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Cancer patient revenue across all facilities
Revenue for diseases this year versus last year in the NE region
Total costs and revenue at top 10 facilities
• Emphasis on the "cube"
– N dimensions
• Permits different users to "slice and dice" subsets of data
• Viewing from different perspectives
Portfolio Analysis
23Copyright 2014 by Data Blueprint
• Bank accounts are of varying value and risk
• Cube by – Social status – Geographical location – Net value, etc.
• Strategy or goal: 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
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--developing the fundamental skills for a rewarding business careerIf 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 [email protected]
- datablueprint.com
CarMax Example Job Posting
24Copyright 2014 by Data Blueprint
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own an area of the business and will be expected to improve it
--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--developing the fundamental skills for a rewarding business career
Polling Question #1
25Copyright 2014 by Data Blueprint
• Do you have/have you started data warehousing, marts and/or other warehousing forms of integration? a. Last year (2014) b. This year (2015) c. Next Year (2016) d. Nope
Data Warehousing Strategies
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Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
Technology from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
27Copyright 2014 by Data Blueprint
• 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
Warehousing Definitions
28Copyright 2014 by Data Blueprint
• 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
Courtesy of: http://www.infosys.com/industries/healthcare/industryofferings/Pages/healthcare - data-warehousing.aspx
Warehousing applied to a specific challenge
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Oracle
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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Corporate Information Factory Architecture
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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
MetaMatrix Integration Example
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• EII Enterprise Information Integration
– between ETL and EAI - delivers tailored views of information to users at the time that it is required
Linked Data
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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
Health Care Provider Data Warehouse
34Copyright 2014 by Data Blueprint
"A roomful of MBAs can accomplish this
analysis faster!"
• 1.8 million members • 1.4 million providers • 800,000 providers no key • 29% prov_ssn ≠ 9 digits • 2.2% prov_number = 9 digits (required) • 1 User • $30 million
Causes of Data Warehouse Failure
36Copyright 2014 by Data Blueprint
1. The project is over budget 2. Slipped schedule 3. Unimplemented functions
and capabilities 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 11. Poor quality data 12. Many more values of gender code than (M/F) 13. Incorrectly structured data 14. Provides correct answer to wrong question 15. Bad warehouse design 16. Overly complex
from The Data Administration Newsletter, www.tdan.com and The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Reframing the question
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• From: How shall we build this data warehouse? – (Worse) … What should go into this warehouse?
• To: How can warehousing capabilities solve this specific business challenge? – (Better still) … How can warehousing capabilities
solve this class of business challenges? • Other examples
– Are you ready for a data warehouse? ✓ Foundational practices
– Will you get it right the first time? ✓ Is the business environment constantly evolving? ✓ Do you have an agreed upon enterprise-wide vocabulary?
– Is your data warehouse intended to be the enterprise audit-able system of record? ✓ Extract, transform and load requirements ✓ Data transformation requirements
– How fast do you need results? ✓ Performance of inserts vs reads ✓ Project deliverables
Data Warehousing Strategies
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Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
Copyright 2013 by Data Blueprint
Inmon Implementation/3NF
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OPERATIONAL SYSTEM
OPERATIONAL SYSTEM
FLAT FILES
SUM MARYDATA RAW DATA
M ETADATA
PURCHASING
SALES
INVENTORY
ANALYSIS
REPORTING
MINING
Third Normal Form
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• Each attribute in the relationship is a fact about a key
– Highly normalized structure
• Use Cases
– Transactional Systems
– Operational Data Stores
Third Normal Form: Pros and Cons
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Neo4j.com
• Pros
– Easily understood by business and end users
– Reduced data redundancy
– Enforced referential integrity
– Indexed attributes/flexible querying
• Cons
– Joins can be expensive
– Does not scale
• Comprised of “fact tables” that contain quantitative data, and any number of adjoining “dimension” tables
• Optimized for business reporting • Use Cases
– OLAP (Online Analytic Processing) – BI
Star Schema
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Wikipedia
Star Schema Pros and Cons
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• Pros
– Simple Design
– Fast Queries
– Most major DBMS are optimized for Star Schema Designs
• Cons
– Questions must be built into the design
– Data marts are often centralized on one fact table
Data Vault
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Bukhantsov.org
• Designed to facilitate long-term historical storage, focusing on ease of implementation
• Retains data lineage information (source/date)
• “All the data, all the time” - hybrid approach of Inmon and Kimball.
• Comprised of Hubs (which contain a list of business keys that do not change often), Links (Associations/transactions between hubs), and Satellites (descriptive attributes associated with hubs and links)
• Use Cases – Data Warehousing
– Complete Audit-ability
Data Vault Pros and Cons
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• Pros
– Simple integration
– Houses immense amounts of data with excellent performance
– Full data lineage captured
• Cons
– Complication is pushed to the “back end”
– Can be difficult to setup for many data workers
– No widespread support for ETL tools yet
Polling Question #2
49Copyright 2014 by Data Blueprint
• Do you have? a. A single enterprise data warehouse b. Coordinated data marts c. Both d. Uncoordinated efforts e. None
Data Warehousing Strategies
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Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
Meta Data Models
51Copyright 2014 by Data Blueprint
Metadata Data ModelSCREENELEMENTscreen element id #data item id #screen element descr.
INTERFACEELEMENTinterface element id #data item id #interface element descr.
INPUTELEMENTinput element id #data item id #input element descr.
OUTPUTELEMENToutput element id #data item id #output element descr.
MODELVIEWmodel view element id #data item id #model view element des.
DEPENDENCYdependency elem id #data item id #process id #dependency description
CODEcode id #data item id #stored data item #code location
INFORMATIONinformation id #data item id #information descr.information request
PROCESSprocess id #data item id #process description
USER TYPEuser type id #data item id #information id #user type description
LOCATIONlocation id #information id #printout element id #process id #stored data items id #user type id #location description
PRINTOUTELEMENTprintout element id #data item id #printout element descr.
STORED DATA ITEMstored data item id #data item id #location id #stored data description
DATA ITEMdata item id #data item description
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Warehouse Process
Warehouse Opera.on
Transforma.on
XMLRecord-‐ Oriented
Mul. Dimensional
Rela.onal
Business Informa.on
So@ware Deployment
ObjectModel (Core, Behavioral, Rela.onships, Instance)
Warehouse Management
Resources
Analysis
Object-‐ Oriented
(ObjectModel)
Foundation
OLAPData Mining
Informa.on Visualiza.on
Business Nomenclature
Data Types
ExpressionsKeys Index
Type Mapping
Overview of CWM Metamodel
http://www.omg.org/technology/documents/modeling_spec_catalog.htm
53Copyright 2014 by Data Blueprint
Marco & Jennings's Complete Meta Data Model
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
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Data Warehousing Strategies
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Copyright 2014 by Data Blueprint
1. Warehousing data in the context of data management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
Guiding Principles
56Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
1. Obtain executive commitment and support 2. Secure business SMEs 3. Let the business drive the priorities 4. Demonstrate data quality is essential 5. Provide incremental value 6. Transparency and self service 7. One size does not fit all: Secure 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
Data Reengineering Leverage
57Copyright 2014 by Data Blueprint
Data Management Practices
Duplicated but ETLed Data(quality & transformations applied)
"Warehoused" Data
Learning/Feedback
Marts
Analytics Practices
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Data Warehousing Strategies1. Warehousing data in the context of data
management
2. Motivation for integration technologies (reporting->BI->Analytics)
3. Warehouse integration technologies
4. Three warehousing architecture foci
5. The use of meta models
6. Guiding principles & best practices
Questions?
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It’s your turn! Use the chat feature or Twitter (#dataed) to submit
your questions to Peter and Steven now.
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61Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Goals and Principles
63Copyright 2014 by Data Blueprint
1. To support and enableeffective business analysisand decision making byknowledgeable workers
2. To build and maintain theenvironment/infrastructure tosupport business intelligenceactivities, specificallyleveraging all the other datamanagement functions tocost effectively deliverconsistent integrated datafor all BI activities
Activities
64Copyright 2014 by Data Blueprint
• 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Primary Deliverables
65Copyright 2014 by Data Blueprint
• 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Roles and Responsibilities
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
66Copyright 2014 by Data Blueprint
Suppliers:• Executives/managers• Subject Matter Experts• Data governance council• Information consumers• Data producers• Data architects/analysts
Participants: • Executives/managers• Data Stewards• Subject Matter Experts• Data Architects• Data Analysts• Application Architects• Data Governance Council• Data Providers• Other BI Professionals
Consumers:
• Application Users
• BI and ReportingUsers
• ApplicationDevelopers andArchitects
• Data integrationDevelopers andArchitects
• BI Vendors andArchitects
• Vendors, Customersand Partners
6 Best Practices for Data Warehousing
67Copyright 2014 by Data Blueprint
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
Kimball's DW Chess Pieces
68Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
5 Key Business Intelligence Trends
69Copyright 2014 by Data Blueprint
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
Corporate Information Factory Architecture
70Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Corporate Information Factory Architecture
71Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Corporate Information Factory Architecture
72Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Corporate Information Factory Architecture
73Copyright 2014 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Additional References
76Copyright 2014 by Data Blueprint
• 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