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Trends in Data Modeling
Presented by James Michael Lee and Peter Aiken, Ph.D.
Welcome: Trends in Data Modeling
Date: August 11, 2015
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD

Steven MacLauchlan

Michael Lee
2Copyright 2015 by Data Blueprint Slide #
Businesses cannot compete without data. Every organization produces and
consumes it. Data trends are hitting the mainstream and businesses are adopting
buzzwords such as Big data, Data Vault, Data Scientist, etc., to seek solutions to
their fundamental data issues. Few realize that the importance of any solution,
regardless of platform or technology relies on the data model supporting it. Data
modeling is not an optional task for an organization’s data remediation effort.
Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application
technology, as well as trends around the practice of data modeling itself. We will
discuss abstract models and entity frameworks, as well as the general shift from
data modeling being segmented to becoming more integrated with business
practices.
Takeaways:
• NoSQL, data vault, etc., different and when should I apply them?
• How Data Modeling relates to business process
• Application development (data first, code first, object first?)
Steven MacLauchlan
• 10 years of experience in Application
Development and Data Modeling with a
focus on Healthcare solutions.
• Delivers tailored data management solutions
that provide focus on data’s business value
while enhancing clients’ overall capability to
manage data
• Certified Data Management Professional (CDMP)
• Computer Science degree from Virginia Commonwealth
University
• Most recent focus: Understanding emerging 

data modeling trends and how these can 

best be leveraged for the Enterprise.
3Copyright 2015 by Data Blueprint Slide #
Peter Aiken, Ph.D.
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
4Copyright 2015 by Data Blueprint Slide #
James “Michael” Lee
• Data Consultant certified in a number of areas, including Data
Vault 2.0 Practitioner, Kimball ETL Architecture and Certified
Data Management Professional (CDMP).
• Over 7 years of experience with
– Designing data quality solutions
– improving data management practices
– implementing Data Governance frameworks
– architecting data warehouses
– implementation of system upgrades and migrations
• In the following industries:
– telecommunications
– banking
– insurance
– government (defense)
– commercial manufacturing
– international shipping
5Copyright 2015 by Data Blueprint Slide #
We believe ...
Data 

Assets
Financial 

Assets
Real

Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be 

used up
Can be 

used up
Non-
degrading √ √ Can degrade

over time
Can degrade

over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depleteable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
6Copyright 2015 by Data Blueprint Slide #
Asset: A resource controlled by the organization as a result of past events or transactions and from which
future economic benefits are expected to flow [Wikipedia]
Trends in Data Modeling
Copyright 2015 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• Traditional (3NF, Star Schema, Data Vault)
• NoSQL Technologies (Key-Value/Document,
Graph, Column Family)
• Trends
- Move to the business
- Self Service and Virtualization
- Agile
- Data Sharing World (The API’s)
- Patterns and Reuse
- Metadata Modeling
7
What is a Data Model*?
• A data model organizes data
elements and standardizes how the
data elements relate to one another.
• In “Data Modeling Made Simple” by
Steve Hoberman, he says: "A data
model is a wayfinding tool for both
business and IT professionals,
which uses a set of symbols and
text to precisely explain a subset of
real information to improve
communication within the
organization and thereby lead to a
more flexible and stable application
environment."
8Copyright 2015 by Data Blueprint Slide #
*According to ANSI.
Why should we care about poor data models?
• Poor data modeling up front can cause Data Quality issues “downstream”
• If the model isn’t a true representation of the business concepts, this will impact
confidence in the data, inhibit business insights and innovation
• Potential for poor DB/Application performance for reads/writes. Example: Over-
normalization
• Lack of flexibility can cause difficulty aligning with evolving business requirements
• Difficulty integrating data in the future
• Constrains business agility by complicating reengineering
• Creates operational inefficiencies (ex: poor application performance)
• Limits workflow transparency
• Proliferates system work-arounds, 

including shadow systems 

developed by end users
• Impact Analysis
9Copyright 2015 by Data Blueprint Slide #
How are Data Models Expressed as Architectures?
• Attributes are organized into entities/objects
– Attributes are characteristics of "things"
– Entitles/objects are "things" whose information is
managed in support of strategy
• Entities/objects are organized into models
– Combinations of attributes and entities are structured
to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
• Models are organized into architectures
– When building new systems, architectures are used to
plan development
– More often, data managers do not know what existing
architectures are and - therefore - cannot make use of
them in support of strategy implementation
10Copyright 2015 by Data Blueprint Slide #
More Granular



















































More Abstract

The Conceptual Data Model
• Represents entities and relationships
• Should Identify the domain and scope of data
• Should be easily understood by business users in order to
communicate core data concepts, and drive application
requirements
11Copyright 2015 by Data Blueprint Slide #
Example:
We need to model customer
address data. A customer may have
many addresses, and many
customers may share one address.
“many to many”
DISPOSITION Data Map
12Copyright 2015 by Data Blueprint Slide #
Data map of DISPOSITION
• At least one but possibly more system USERS enter the DISPOSITION facts into the system.
• An ADMISSION is associated with one and only one DISCHARGE.
• An ADMISSION is associated with zero or more FACILITIES.
• An ADMISSION is associated with zero or more PROVIDERS.
• An ADMISSION is associated with one or more ENCOUNTERS.
• An ENCOUNTER may be recorded by a system USER.
• An ENCOUNTER may be associated with a PROVIDER.
• An ENCOUNTER may be associated with one or more DIAGNOSES.
13Copyright 2015 by Data Blueprint Slide #
ADMISSION Contains information about patient admission history
related to one or more inpatient episodes
DIAGNOSIS Contains the International Disease Classification (IDC) of
code representation and/or description of a patient's health
related to an inpatient code
DISCHARGE A table of codes describing disposition types available for
an inpatient at a FACILITY
ENCOUNTER Tracking information related to inpatient episodes
FACILITY File containing a list of all facilities in regional health care
system
PROVIDER Full name of a member of the FACILITY team providing
services to the patient
USER Any user with access to create, read, update, and delete
DISPOSITION data
A sample data entity and associated metadata
Entity: BED
Data Asset Type: Principal Data Entity
Purpose: This is a substructure within the Room

substructure of the Facility Location. It contains 

information about beds within rooms.
Source: Maintenance Manual for File and Table

Data (Software Version 3.0, Release 3.1)
Attributes: Bed.Description

Bed.Status

Bed.Sex.To.Be.Assigned

Bed.Reserve.Reason
Associations: >0-+ Room
Status: Validated
14Copyright 2015 by Data Blueprint Slide #
• A purpose statement describing why the organization is maintaining information
about this business concept;
• Sources of information about it;
• A partial list of the attributes or characteristics of the entity; and
• Associations with other data items; this one is read as "One room contains zero or
many beds."
The Logical Data Model
• Should represent the Conceptual Data model more
thoroughly, but be otherwise very similar
• Will include attributes, names, relationships, and other
metadata
• Will be developed using Data Modeling notation (ex: UML)
15Copyright 2015 by Data Blueprint Slide #
The Physical Data Model
• Describes the specific database implementation of the
data
• Attributes will be named according to naming conventions
• Displays data types, accurate table names, Key
information, etc
16Copyright 2015 by Data Blueprint Slide #
CM2 Component Evolution is technology derived but technology independent
17Copyright 2015 by Data Blueprint Slide #
Data Reengineering for More Shareable Data
18Copyright 2015 by Data Blueprint Slide #
Other logical as-is
data architecture
components
Data Modeling Framework
Conceptual Logical Physical










Goal
Validated
Not Validated
Copyright 2015 by Data Blueprint Slide # 19
Trends in Data Modeling
Copyright 2015 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• Traditional (3NF, Star Schema, Data Vault)
• NoSQL Technologies (Key-Value/Document,
Graph, Column Family)
• Trends
- Move to the business
- Self Service and Virtualization
- Agile
- Data Sharing World (The API’s)
- Patterns and Reuse
- Metadata Modeling
20
Normalization Rules Overview
• 1st Normal Form - no repeating non-
key attributes for a given primary key
• 2nd Normal Form - no non-key
attributes that depend on only a
portion of the primary key
• 3rd Normal Form - no attributes
depend on something other than the
primary key
• 4th Normal Form - attributes depend
on not only key but the value of the
key
• 5th Normal Form - an entity is in
5NBF if its dependencies on
occurrences of the same entity of
entity type have been moved into a
structured entity
21Copyright 2015 by Data Blueprint Slide #
The row in every table is
dependent on the key, the whole
key and northern but the key
Third Normal Form
• Each attribute in the relationship is a fact about a key
• Highly normalized structure
22Copyright 2015 by Data Blueprint Slide #
• Use Cases:
– Transactional Systems.
– Operational Data Stores.
Third Normal Form: Pros and Cons
• 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
23Copyright 2015 by Data Blueprint Slide #
Neo4j.com
Star Schema
24Copyright 2015 by Data Blueprint Slide #
• 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
Wikipedia
Star Schema Pros and Cons
• 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
25Copyright 2015 by Data Blueprint Slide #
Data Vault
• 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)
26Copyright 2015 by Data Blueprint Slide #
• Use Cases:
– Data Warehousing
– Complete Auditability
Bukhantsov.org
Data Vault Pros and Cons
• 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
27Copyright 2015 by Data Blueprint Slide #
Model Comparison Matrix
28Copyright 2015 by Data Blueprint Slide #
3NF Dimensional Vault
Scalability ☑ ☑ ☑
Flexibility ☒ ☒ ☑
Reengineering ☒ ☒ ☑
Auditability ☑
Business Interpretable ☑ ☑ ☒
Presentation Layer ☒ ☑ ☒
Performance ☒ ☑ ☑
Support ☑ ☑
29Copyright 2015 by Data Blueprint Slide #
Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest
trigger significant publicity. Often no usable products exist and commercial viability is unproven.
Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the
technology shake out or fail. Investments continue only if the surviving providers improve their products to the
satisfaction of early adopters.
Peak of Inflated Expectations: Early publicity produces a number of
success stories—often accompanied by scores of failures. Some
companies take action; many do not.
Slope of Enlightenment: More instances of how the technology can benefit the
enterprise start to crystallize and become more widely understood. Second- and third-
generation products appear from technology providers. More enterprises fund pilots;
conservative companies remain cautious.
Plateau of Productivity: Mainstream adoption starts to
take off. Criteria for assessing provider viability are more
clearly defined. The technology’s broad market
applicability and relevance are clearly paying off.
Gartner Five-phase Hype Cycle
30Copyright 2015 by Data Blueprint Slide #
2012 Hype Cycle
2012 Big Data in Hype Cycle
31Copyright 2015 by Data Blueprint Slide #
2013 Big Data in Hype Cycle
32Copyright 2015 by Data Blueprint Slide #
2014 Big Data in Hype Cycle
33Copyright 2015 by Data Blueprint Slide #
"A focus on big data is not a substitute for the
fundamentals of information management."
NoSQL Solutions*
• Document/Key Value
– “Schema-less” design empowers developers*
– Scalable
– High availability
– Economically viable (scale out not up!)
• RDF/Triple Store
– Purpose-built to store triples (“bob likes football”)
– SPARQL is a query language specific to RDF.
– One of the pillars of “Semantic Web”
• Graph
– Structure comprised of “nodes”, “edges”, and “properties”
– Focused on the interconnection between entities
– Fast queries to find associative data
• Column Family
– Columns are stored individually (but clustered by “family” unlike traditional
columnar databases)
– By only querying specific column families, we can have nearly unlimited
numbers of columns without causing expensive queries
34Copyright 2015 by Data Blueprint Slide #
*not exhaustive!
NoSQL Data Models
35Copyright 2015 by Data Blueprint Slide #
RDF/Triple Store
Graph (Source: Neo4J)
Document Store (Source: MongoDB)
Column Store (Source: Toadworld)
NoSQL providers
36Copyright 2015 by Data Blueprint Slide #
Wikibon.org
Example: Marvel’s Data Model
37Copyright 2015 by Data Blueprint Slide #
Trends in Data Modeling
Copyright 2015 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• Traditional (3NF, Star Schema, Data Vault)
• NoSQL Technologies (Key-Value/Document,
Graph, Column Family)
• Trends
- Move to the business
- Self Service and Virtualization
- Agile
- Data Sharing World (The API’s)
- Patterns and Reuse
- Metadata Modeling
38
Move it to the Business
• Models need to add value
• Models need to be part of the process
– (Not a documentation of the process)
• Models need to assist in improving capabilities, not
hindering them
– Self Service BI
39Copyright 2015 by Data Blueprint Slide #
Self Service and Virtualization
• Self Service BI requires end user understanding of
the system
• Presentation Data Models
40Copyright 2015 by Data Blueprint Slide #
Agile
• Incremental build of models
– Not an excuse to create bad models
• 80/20 Rule
• The problem with code first
– Rules exist in code
– Reengineering concerns
– Governance concerns
– Lack Business Insights
• Database First
– Creates value in modeling
– Enforced integrity and lineage of the data
– Integrates the model into the process
– Used to generate code
41Copyright 2015 by Data Blueprint Slide #
A Data Sharing World
• Adding structure to information allows us to obtain
exactly what we want, when we want it.
• Allows applications to serve up data to external
sources in a structured way- “Post-schema”.
42Copyright 2015 by Data Blueprint Slide #
Design Patterns
• Why are the restrooms generally in the same place in each building?
• What about the electrical wiring?
• HVAC? Floorplans? ...
• Architecture design patterns (spoke and hub, 

hub of hubs, warehouse, cloud, MDM, 

changing tires, portal)
43Copyright 2015 by Data Blueprint Slide #
Patterns and Reuse
• Common rule of thumb:
– One third of a data model
contains fields common to all
business.
– One third contains fields common
to the industry, and the
– Other third is specific to the
organization.
• Patterns should theoretically provide
an organization with a base-line to
quickly develop data infrastructure.
• Off-the-shelf solutions may require
in-depth customization or
specialization.
44Copyright 2015 by Data Blueprint Slide #
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
Meta Data Models
45Copyright 2015 by Data Blueprint Slide #
Marco & Jennings's Metadata Model
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
46Copyright 2015 by Data Blueprint Slide #
Trends in Data Modeling
Copyright 2015 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• Traditional (3NF, Star Schema, Data Vault)
• NoSQL Technologies (Key-Value/Document,
Graph, Column Family)
• Trends
- Move to the business
- Self Service and Virtualization
- Agile
- Data Sharing World (The API’s)
- Patterns and Reuse
- Metadata Modeling
47
Conclusions
• Data Modeling is
important to get right.
• Getting it “right” is
hugely dependent on
the business case,
maturity of the
organization,
flexibility for future
growth, and so much
more.
• There are many
technologies and
ideas available to
help solve a number
of problems.
• Don't try any of this
without considering
the various
architectures involved
48Copyright 2015 by Data Blueprint Slide #
Questions?
49Copyright 2015 by Data Blueprint Slide #
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter, Michael and Steven now.
Upcoming Events
Data Quality Success Stories
September 8, 2015
@ 2:00 PM ET/11:00 AM PT
Design & Manage Data Structures
October 13, 2015 

@ 2:00 PM ET/11:00 AM PT
Sign up here:
• www.datablueprint.com/webinar-schedule
• or www.dataversity.net
50Copyright 2015 by Data Blueprint Slide #
Sources
• Data model. (2014, October 7). In Wikipedia, The Free
Encyclopedia. Retrieved October 7, 2014, from http://
en.wikipedia.org/w/index.php?
title=Data_model&oldid=628639882
• Data Modeling 101. (2006). In Agile Data. Retrieved
October 7, 2014, from http://www.agiledata.org/essays/
dataModeling101.html
51Copyright 2015 by Data Blueprint Slide #
Trends in Data Modeling

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Trends in Data Modeling

  • 1. Trends in Data Modeling Presented by James Michael Lee and Peter Aiken, Ph.D.
  • 2. Welcome: Trends in Data Modeling Date: August 11, 2015 Time: 2:00 PM ET Presented by: Peter Aiken, PhD
 Steven MacLauchlan
 Michael Lee 2Copyright 2015 by Data Blueprint Slide # Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big data, Data Vault, Data Scientist, etc., to seek solutions to their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business. This webinar will address emerging trends around data model application technology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices. Takeaways: • NoSQL, data vault, etc., different and when should I apply them? • How Data Modeling relates to business process • Application development (data first, code first, object first?)
  • 3. Steven MacLauchlan • 10 years of experience in Application Development and Data Modeling with a focus on Healthcare solutions. • Delivers tailored data management solutions that provide focus on data’s business value while enhancing clients’ overall capability to manage data • Certified Data Management Professional (CDMP) • Computer Science degree from Virginia Commonwealth University • Most recent focus: Understanding emerging 
 data modeling trends and how these can 
 best be leveraged for the Enterprise. 3Copyright 2015 by Data Blueprint Slide #
  • 4. Peter Aiken, Ph.D. • 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data management practices • Multi-year immersions: – US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – … • DAMA International President 2009-2013 • DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman 4Copyright 2015 by Data Blueprint Slide #
  • 5. James “Michael” Lee • Data Consultant certified in a number of areas, including Data Vault 2.0 Practitioner, Kimball ETL Architecture and Certified Data Management Professional (CDMP). • Over 7 years of experience with – Designing data quality solutions – improving data management practices – implementing Data Governance frameworks – architecting data warehouses – implementation of system upgrades and migrations • In the following industries: – telecommunications – banking – insurance – government (defense) – commercial manufacturing – international shipping 5Copyright 2015 by Data Blueprint Slide #
  • 6. We believe ... Data 
 Assets Financial 
 Assets Real
 Estate Assets Inventory Assets Non- depletable Available for subsequent use Can be 
 used up Can be 
 used up Non- degrading √ √ Can degrade
 over time Can degrade
 over time Durable Non-taxed √ √ Strategic Asset √ √ √ √ • Today, data is the most powerful, yet underutilized and poorly managed organizational asset • Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic • Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon! • Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships 6Copyright 2015 by Data Blueprint Slide # Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
  • 7. Trends in Data Modeling Copyright 2015 by Data Blueprint • Business to Data: the Relationship • What is a Data Model? • Conceptual, Logical, Physical • What issues can poor data modeling introduce? • Different Models, Different Uses • Traditional (3NF, Star Schema, Data Vault) • NoSQL Technologies (Key-Value/Document, Graph, Column Family) • Trends - Move to the business - Self Service and Virtualization - Agile - Data Sharing World (The API’s) - Patterns and Reuse - Metadata Modeling 7
  • 8. What is a Data Model*? • A data model organizes data elements and standardizes how the data elements relate to one another. • In “Data Modeling Made Simple” by Steve Hoberman, he says: "A data model is a wayfinding tool for both business and IT professionals, which uses a set of symbols and text to precisely explain a subset of real information to improve communication within the organization and thereby lead to a more flexible and stable application environment." 8Copyright 2015 by Data Blueprint Slide # *According to ANSI.
  • 9. Why should we care about poor data models? • Poor data modeling up front can cause Data Quality issues “downstream” • If the model isn’t a true representation of the business concepts, this will impact confidence in the data, inhibit business insights and innovation • Potential for poor DB/Application performance for reads/writes. Example: Over- normalization • Lack of flexibility can cause difficulty aligning with evolving business requirements • Difficulty integrating data in the future • Constrains business agility by complicating reengineering • Creates operational inefficiencies (ex: poor application performance) • Limits workflow transparency • Proliferates system work-arounds, 
 including shadow systems 
 developed by end users • Impact Analysis 9Copyright 2015 by Data Blueprint Slide #
  • 10. How are Data Models Expressed as Architectures? • Attributes are organized into entities/objects – Attributes are characteristics of "things" – Entitles/objects are "things" whose information is managed in support of strategy • Entities/objects are organized into models – Combinations of attributes and entities are structured to represent information requirements – Poorly structured data, constrains organizational information delivery capabilities • Models are organized into architectures – When building new systems, architectures are used to plan development – More often, data managers do not know what existing architectures are and - therefore - cannot make use of them in support of strategy implementation 10Copyright 2015 by Data Blueprint Slide # More Granular
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 More Abstract

  • 11. The Conceptual Data Model • Represents entities and relationships • Should Identify the domain and scope of data • Should be easily understood by business users in order to communicate core data concepts, and drive application requirements 11Copyright 2015 by Data Blueprint Slide # Example: We need to model customer address data. A customer may have many addresses, and many customers may share one address. “many to many”
  • 12. DISPOSITION Data Map 12Copyright 2015 by Data Blueprint Slide #
  • 13. Data map of DISPOSITION • At least one but possibly more system USERS enter the DISPOSITION facts into the system. • An ADMISSION is associated with one and only one DISCHARGE. • An ADMISSION is associated with zero or more FACILITIES. • An ADMISSION is associated with zero or more PROVIDERS. • An ADMISSION is associated with one or more ENCOUNTERS. • An ENCOUNTER may be recorded by a system USER. • An ENCOUNTER may be associated with a PROVIDER. • An ENCOUNTER may be associated with one or more DIAGNOSES. 13Copyright 2015 by Data Blueprint Slide # ADMISSION Contains information about patient admission history related to one or more inpatient episodes DIAGNOSIS Contains the International Disease Classification (IDC) of code representation and/or description of a patient's health related to an inpatient code DISCHARGE A table of codes describing disposition types available for an inpatient at a FACILITY ENCOUNTER Tracking information related to inpatient episodes FACILITY File containing a list of all facilities in regional health care system PROVIDER Full name of a member of the FACILITY team providing services to the patient USER Any user with access to create, read, update, and delete DISPOSITION data
  • 14. A sample data entity and associated metadata Entity: BED Data Asset Type: Principal Data Entity Purpose: This is a substructure within the Room
 substructure of the Facility Location. It contains 
 information about beds within rooms. Source: Maintenance Manual for File and Table
 Data (Software Version 3.0, Release 3.1) Attributes: Bed.Description
 Bed.Status
 Bed.Sex.To.Be.Assigned
 Bed.Reserve.Reason Associations: >0-+ Room Status: Validated 14Copyright 2015 by Data Blueprint Slide # • A purpose statement describing why the organization is maintaining information about this business concept; • Sources of information about it; • A partial list of the attributes or characteristics of the entity; and • Associations with other data items; this one is read as "One room contains zero or many beds."
  • 15. The Logical Data Model • Should represent the Conceptual Data model more thoroughly, but be otherwise very similar • Will include attributes, names, relationships, and other metadata • Will be developed using Data Modeling notation (ex: UML) 15Copyright 2015 by Data Blueprint Slide #
  • 16. The Physical Data Model • Describes the specific database implementation of the data • Attributes will be named according to naming conventions • Displays data types, accurate table names, Key information, etc 16Copyright 2015 by Data Blueprint Slide #
  • 17. CM2 Component Evolution is technology derived but technology independent 17Copyright 2015 by Data Blueprint Slide #
  • 18. Data Reengineering for More Shareable Data 18Copyright 2015 by Data Blueprint Slide # Other logical as-is data architecture components
  • 19. Data Modeling Framework Conceptual Logical Physical 
 
 
 
 
 Goal Validated Not Validated Copyright 2015 by Data Blueprint Slide # 19
  • 20. Trends in Data Modeling Copyright 2015 by Data Blueprint • Business to Data: the Relationship • What is a Data Model? • Conceptual, Logical, Physical • What issues can poor data modeling introduce? • Different Models, Different Uses • Traditional (3NF, Star Schema, Data Vault) • NoSQL Technologies (Key-Value/Document, Graph, Column Family) • Trends - Move to the business - Self Service and Virtualization - Agile - Data Sharing World (The API’s) - Patterns and Reuse - Metadata Modeling 20
  • 21. Normalization Rules Overview • 1st Normal Form - no repeating non- key attributes for a given primary key • 2nd Normal Form - no non-key attributes that depend on only a portion of the primary key • 3rd Normal Form - no attributes depend on something other than the primary key • 4th Normal Form - attributes depend on not only key but the value of the key • 5th Normal Form - an entity is in 5NBF if its dependencies on occurrences of the same entity of entity type have been moved into a structured entity 21Copyright 2015 by Data Blueprint Slide # The row in every table is dependent on the key, the whole key and northern but the key
  • 22. Third Normal Form • Each attribute in the relationship is a fact about a key • Highly normalized structure 22Copyright 2015 by Data Blueprint Slide # • Use Cases: – Transactional Systems. – Operational Data Stores.
  • 23. Third Normal Form: Pros and Cons • 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 23Copyright 2015 by Data Blueprint Slide # Neo4j.com
  • 24. Star Schema 24Copyright 2015 by Data Blueprint Slide # • 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 Wikipedia
  • 25. Star Schema Pros and Cons • 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 25Copyright 2015 by Data Blueprint Slide #
  • 26. Data Vault • 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) 26Copyright 2015 by Data Blueprint Slide # • Use Cases: – Data Warehousing – Complete Auditability Bukhantsov.org
  • 27. Data Vault Pros and Cons • 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 27Copyright 2015 by Data Blueprint Slide #
  • 28. Model Comparison Matrix 28Copyright 2015 by Data Blueprint Slide # 3NF Dimensional Vault Scalability ☑ ☑ ☑ Flexibility ☒ ☒ ☑ Reengineering ☒ ☒ ☑ Auditability ☑ Business Interpretable ☑ ☑ ☒ Presentation Layer ☒ ☑ ☒ Performance ☒ ☑ ☑ Support ☑ ☑
  • 29. 29Copyright 2015 by Data Blueprint Slide # Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven. Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters. Peak of Inflated Expectations: Early publicity produces a number of success stories—often accompanied by scores of failures. Some companies take action; many do not. Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third- generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious. Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off. Gartner Five-phase Hype Cycle
  • 30. 30Copyright 2015 by Data Blueprint Slide # 2012 Hype Cycle
  • 31. 2012 Big Data in Hype Cycle 31Copyright 2015 by Data Blueprint Slide #
  • 32. 2013 Big Data in Hype Cycle 32Copyright 2015 by Data Blueprint Slide #
  • 33. 2014 Big Data in Hype Cycle 33Copyright 2015 by Data Blueprint Slide # "A focus on big data is not a substitute for the fundamentals of information management."
  • 34. NoSQL Solutions* • Document/Key Value – “Schema-less” design empowers developers* – Scalable – High availability – Economically viable (scale out not up!) • RDF/Triple Store – Purpose-built to store triples (“bob likes football”) – SPARQL is a query language specific to RDF. – One of the pillars of “Semantic Web” • Graph – Structure comprised of “nodes”, “edges”, and “properties” – Focused on the interconnection between entities – Fast queries to find associative data • Column Family – Columns are stored individually (but clustered by “family” unlike traditional columnar databases) – By only querying specific column families, we can have nearly unlimited numbers of columns without causing expensive queries 34Copyright 2015 by Data Blueprint Slide # *not exhaustive!
  • 35. NoSQL Data Models 35Copyright 2015 by Data Blueprint Slide # RDF/Triple Store Graph (Source: Neo4J) Document Store (Source: MongoDB) Column Store (Source: Toadworld)
  • 36. NoSQL providers 36Copyright 2015 by Data Blueprint Slide # Wikibon.org
  • 37. Example: Marvel’s Data Model 37Copyright 2015 by Data Blueprint Slide #
  • 38. Trends in Data Modeling Copyright 2015 by Data Blueprint • Business to Data: the Relationship • What is a Data Model? • Conceptual, Logical, Physical • What issues can poor data modeling introduce? • Different Models, Different Uses • Traditional (3NF, Star Schema, Data Vault) • NoSQL Technologies (Key-Value/Document, Graph, Column Family) • Trends - Move to the business - Self Service and Virtualization - Agile - Data Sharing World (The API’s) - Patterns and Reuse - Metadata Modeling 38
  • 39. Move it to the Business • Models need to add value • Models need to be part of the process – (Not a documentation of the process) • Models need to assist in improving capabilities, not hindering them – Self Service BI 39Copyright 2015 by Data Blueprint Slide #
  • 40. Self Service and Virtualization • Self Service BI requires end user understanding of the system • Presentation Data Models 40Copyright 2015 by Data Blueprint Slide #
  • 41. Agile • Incremental build of models – Not an excuse to create bad models • 80/20 Rule • The problem with code first – Rules exist in code – Reengineering concerns – Governance concerns – Lack Business Insights • Database First – Creates value in modeling – Enforced integrity and lineage of the data – Integrates the model into the process – Used to generate code 41Copyright 2015 by Data Blueprint Slide #
  • 42. A Data Sharing World • Adding structure to information allows us to obtain exactly what we want, when we want it. • Allows applications to serve up data to external sources in a structured way- “Post-schema”. 42Copyright 2015 by Data Blueprint Slide #
  • 43. Design Patterns • Why are the restrooms generally in the same place in each building? • What about the electrical wiring? • HVAC? Floorplans? ... • Architecture design patterns (spoke and hub, 
 hub of hubs, warehouse, cloud, MDM, 
 changing tires, portal) 43Copyright 2015 by Data Blueprint Slide #
  • 44. Patterns and Reuse • Common rule of thumb: – One third of a data model contains fields common to all business. – One third contains fields common to the industry, and the – Other third is specific to the organization. • Patterns should theoretically provide an organization with a base-line to quickly develop data infrastructure. • Off-the-shelf solutions may require in-depth customization or specialization. 44Copyright 2015 by Data Blueprint Slide #
  • 45. Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission Meta Data Models 45Copyright 2015 by Data Blueprint Slide #
  • 46. Marco & Jennings's Metadata Model Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission 46Copyright 2015 by Data Blueprint Slide #
  • 47. Trends in Data Modeling Copyright 2015 by Data Blueprint • Business to Data: the Relationship • What is a Data Model? • Conceptual, Logical, Physical • What issues can poor data modeling introduce? • Different Models, Different Uses • Traditional (3NF, Star Schema, Data Vault) • NoSQL Technologies (Key-Value/Document, Graph, Column Family) • Trends - Move to the business - Self Service and Virtualization - Agile - Data Sharing World (The API’s) - Patterns and Reuse - Metadata Modeling 47
  • 48. Conclusions • Data Modeling is important to get right. • Getting it “right” is hugely dependent on the business case, maturity of the organization, flexibility for future growth, and so much more. • There are many technologies and ideas available to help solve a number of problems. • Don't try any of this without considering the various architectures involved 48Copyright 2015 by Data Blueprint Slide #
  • 49. Questions? 49Copyright 2015 by Data Blueprint Slide # It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter, Michael and Steven now.
  • 50. Upcoming Events Data Quality Success Stories September 8, 2015 @ 2:00 PM ET/11:00 AM PT Design & Manage Data Structures October 13, 2015 
 @ 2:00 PM ET/11:00 AM PT Sign up here: • www.datablueprint.com/webinar-schedule • or www.dataversity.net 50Copyright 2015 by Data Blueprint Slide #
  • 51. Sources • Data model. (2014, October 7). In Wikipedia, The Free Encyclopedia. Retrieved October 7, 2014, from http:// en.wikipedia.org/w/index.php? title=Data_model&oldid=628639882 • Data Modeling 101. (2006). In Agile Data. Retrieved October 7, 2014, from http://www.agiledata.org/essays/ dataModeling101.html 51Copyright 2015 by Data Blueprint Slide #