Data-Ed Webinar: Design & Manage Data Structures

Data structures enable you to store and organize 
data so that it can be used efficiently. But how do 
you know to apply the correct one? There is a 
difference between structuring master data, 
reference data and analytics data. This webinar  
will discuss the various data structures available  
and when to use each one. We will show how  
data structures should support your organizational 
strategy and how each method can contribute to 
business value.
Learning Objectives:
• Application of correct data structures to fit business needs
• How different structures create different business value  
 
Date: July 8, 2014 
Time: 2:00 PM ET 
Presented by: Dave Marsh & Peter Aiken
Copyright 2013 by Data Blueprint
Welcome: Design/Manage Data Structures
1

Get Social With Us!
Like Us on Facebook
www.facebook.com/
datablueprint
Post questions and comments
Find industry news, insightful
content
and event updates.
Join the Group
Data Management &
Business Intelligence
Ask questions, gain insights
and collaborate with fellow
data management
professionals
Live Twitter Feed
Join the conversation!
Follow us:
@datablueprint
@paiken
Ask questions and submit your
comments: #dataed
3

Presented by Dave Marsh & Peter Aiken, Ph.D.
Design & Manage Data Structures
Marco Level

Your Presenters
Dave Marsh
• Lead Data  
Consultant,  
Data Blueprint
• 30+ Years experience
designing and building
solutions for the private and
public sectors.
• Architecture/Design
experience in:
- Transactional processing
- Shop floor automation
- Data Warehousing
- Identity Management
- Mobile
Peter Aiken
• 30+ years DM  
experience
• 9 books/many articles
• Experienced with 500+ data
management practices
• Multi-year immersions: US
DoD, Nokia, Deutsche
Bank, Wells Fargo, &
Commonwealth of VA
4

• Context: Data Management/DAMA/DM BoK/CDMP?
• What is a data structure?
• Structured data storage, a bit of history and context
• Why are data structures important?
• Data Personas/Usage (interest over time)
• Data Topology and alignment to the data audience
• Internal data structures to fit the needs
• Q & A?
Outline: Design/Manage Data Structures
6
• Q & A?

Maslow's Hierarchy of Needs
7

You can accomplish
Advanced Data Practices
without becoming
proficient in the Basic
Data Management
Practices however this
will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk
Data Management Practices Hierarchy
Basic Data Management Practices
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Data Program Management
Data Stewardship Data Development
Data Support Operations
Organizational Data Integration
8

Data Program  
Coordination
Feedback
Data 
Development
Standard 
Data
Data Management is an Integrated System of Five Practice Areas
Organizational Strategies
Goals
Business 
Data
Business Value
Application  
Models &
Designs
Implementation
Direction
Guidance
9
Organizational 
Data Integration
Data 
Stewardship
Data Support 
Operations
Data  
Asset Use
Integrated 
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable
data access
Achieve sharing of data
within a business area

Five Integrated DM Practice Areas
10
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program  
Coordination
Data 
Development
Organizational
Data Integration
Data 
Stewardship
Data Support 
Operations

DAMA DM BoK & CDMP Data Management Functions
• 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 (more
at dama.org)
– Organized around several environmental
elements
• CDMP
– Certified Data Management Professional
– DAMA International and ICCP
– Membership in a distinct group made up of
your fellow professionals
– Recognition for your specialized knowledge in
a choice of 17 specialty areas
– Series of 3 exams
– For more information, please visit:
• http://www.dama.org/i4a/pages/index.cfm?pageid=3399
• http://iccp.org/certification/designations/cdmp
11

• Q & A?
Outline
13

What is a data structure?
• "An organization of information, usually in memory, for better
algorithm efficiency, such as queue, stack, linked list, heap, dictionary,
and tree, or conceptual unity, such as the name and address of a
person. It may include redundant information, such as length of the
list or number of nodes in a subtree."
• Some data structure characteristics
– Grammar for data objects
• Grammar is the principles  
or rules of an art, science,  
or technique "a grammar  
of the theater"
– Constraints for data  
objects
– Sequential order
– Uniqueness
– Arrangement
• Hierarchical, relational,  
network, other
– Balance
– Optimality
http://www.nist.gov/dads/HTML/datastructur.html
14

How are data structures expressed as architectures?
• Details are
organized into  
larger
components
• Larger
components are
organized into
models
• Models are
organized into
architectures
A B
C D
A B
C D
A
D
C
B
15

How are data structures 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
– Examples
• 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
– Examples
• 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
– Why no examples?
16

Data
Data
Data
Information
Fact Meaning
Request
A Model Specifying Relationships Among Important Terms
[Built on definition by Dan Appleton 1983]
Intelligence
Strategic Use
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one
MEANING.
5. INTELLIGENCE is INFORMATION associated with its USES.
Wisdom & knowledge are  
often used synonymously
Data
Data
Data Data
17

• Q & A?
Outline
19

History (such as it is)
• Automate existing manual  
processing
• Data management was:
– Running millions of punched  
cards through banks of sorting,  
collating & tabulating machines
– Results printed on paper or  
punched onto more cards
– Data management meant physically storing and hauling around
punched cards
• Tasks (check signing, calculating, and machine control)
were implemented to provide automated support for
departmental-based processing
• Creating information silos
• Data Processing Manager
20

Chief Information Officer
21

CFO Necessary Prerequisites/Qualifications
• CPA
• CMA
• Masters of Accountancy
• Other recognized
degrees/certifications
• These are necessary
but insufficient
prerequisites/
qualifications
22

CIO Qualifications
• No specific qualifications
• Typically technological fields:
– Computer science
– Software engineering
– Information systems
• Business
– Master of Business Administration
– Master of Science in Management
• Business acumen and strategic perspectives have taken
precedence over technical skills.
– CIOs appointed from the business side of the organization
• Especially if they have project management skills.
23

What do we teach knowledge workers about data?
What percentage of the deal with it daily?
24

• Q & A?
Outline
26

Data Leverage
Less ROT
Technologies
Process
People
• Permits organizations to better manage their sole non-
depleteable, non-degrading, durable, strategic asset - data
– within the organization, and
– with organizational data exchange partners
• Leverage
– Obtained by implementation of data-centric technologies, processes, and
human skill sets
– Increased by elimination of data ROT (redundant, obsolete, or trivial)
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
1. lowers organizational IT costs and
2. increases organizational knowledge worker productivity
27

Data Structure Questions
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
• Who makes decisions about the range and scope of
common data usage?
28

Running Query
29

Optimized Query
30

Repeat 100s, thousands, millions of times ...
31

Death by 1000 Cuts
32

5 Basic Data Structures
Indexed Sequential File: Built-in index permits location of
records of persons with last names starting with "T"
Index
Program: Where is the record for person
"Townsend?"
Index: Start looking here where the
"Ts" are stored
Relational Database: Records are related to
each other using relationships describable using relational
algebra
Flat File: Records are typically sorted
according to some criteria and must be
searched from the beginning for each access
Program: Must start at the beginning
and read each record when looking for
person "Townsend?"
Network Database: Records are related to each
other using arranged master records associated with
multiple detail records using linked lists and pointers Associative
Concept-oriented
Multi-dimensional
XML database 
3NF 
Star schema 
Data Vault
Hierarchical Database: Records are related to each other
hierarchically using 'parent child' relationships
33

Data structures organized into an Architecture
• How do data structures support
organizational strategy?
• Consider the opposite question?
– Were your systems explicitly designed to be
integrated or otherwise work together?
– If not, then what is the likelihood that they will
work well together?
– In all likelihood your organization is spending
between 20-40% of its IT budget compensating
for poor data structure integration
– They cannot be helpful as long as their structure
is unknown
• Two answers/two separate strategies
– Achieving efficiency and  
effectiveness goals
– Providing organizational dexterity for rapid
implementation
34

Single
Data Store
No Single Data Store
• The thought of a single monolithic data
store which can service all of an
organization’s information needs has long
since been abandoned. In the modern
data management topology, multiple data
stores are created to service specific
processing needs and user groups within
the organization.
• Implications:
• The needs characteristics of the multitude
of the audiences served by the data
structures
• Data lifecycle
• The design styles (old and new) utilized to
organize the data to service the audiences
• A breakdown of the various stores
• The resultant store characteristics
35

Conclusions
• 1 is not enough
• Most
organizations
have far to many
different data
structures and
they become
barriers to
progress and
integration
• Not much
expertise to figure
out these
challenges
36

• Q & A?
Outline
38

Data Personas (The Requirements)
Operational
Performer
Interested in alerts,
notifications and
reporting based on
current values (real-
time) data. They use the
information to make
decisions and changes
in the transactional
systems. These
changes are targeted to
improve the
organizations ability to
deliver in the short term.
Operational Analyst
(Manager)
Interested in aggregated
real-time data for their
domain of responsibility.
The data is displayed
using visualization
techniques of
scorecards, charts and
reports, preferably within
a single dashboard. The
searching is for
favorable/unfavorable
trends to indicate
adjustments are needed
in the staff & resource
allocations.
Data Analyst
Responsible to support
detailed and typically
complex analysis
requests from business
users/consumers of
data. The analyst role
span both the
operational and
historical time windows
and thus they need to be
versed in both the
operational and analytic
environments.
Data Miner/
Scientist
Responsible for using
statistical and machine
learning techniques to
identify patterns from
the data. These patterns
are correlated into
insights and actions for
better business
outcomes. The miner
may use operational
and historical data for
research.
Executive Consumer
Receives the data
through summary
dashboards with drill
down/through
capabilities. Request
detailed analysis and
reporting on High Value
Question from the Data
Analyst and Data
Miners. These
consumers are looking
at the data to make
short and long term
decisions to improve the
organizational efficiency
and customer
experience.
Operational Analytic
39

• Operational interest is high when data is introduced to the
operational stores. This interest wanes over time.
• Analytic interest is low when data is first introduced. The
interest increases as the data is collected and combined
with other enterprise data.
Persona Data Interest
Operational
Interest
Analytic
Interest
Interest
Time
40

• Q & A?
Outline
42

Data Topology Today
43

Data Store Purpose a review of the Data Topology
• Master Data
– Master Data is the term used to describe the data domains that drive
business activities. Master data is the data that must first be in
place before business transactions can occur. Master data is often
shared across the organizational business units and it is typically at
the center of business strategies. The transaction defines the
business/process event (order, dispatch, sales) while the Master
Data describes the ‘who’ (customers, drivers, account reps), the
‘what’ (load), the ‘when’ (date, time) and the ‘where’ (origin and
destination location).
• Online Transaction Processing (OLTP)
– “Transactional data” is the term used to describe the data involved in
the execution of the business activities. Transactional data
associates master data (i.e. customers and products) to a business
activity that often represents a unit or work, such as the creation of
an order.
• The Master Data and OLTP stores are where data is initially created and
persisted within the organization’s data and thus carry a special
classification of System of Record (SOR). They are created to capture
the transactional data as it arrives and makes the data available for the
processes and services. The data arrives into these databases through
manual entry or automated feeds. These data stores are logically (and
sometimes physically) separated by the transactional subject area they
are created to serve.
OLTP1
OLTP2
OLTPn...
Master
Data
44

• Operational Data Store (ODS)
– An Operational Data Store (ODS) is created to integrate data from
two or more SORs for the purposes of data integration. The ODS is
normally created to satisfy reporting needs across functional SOR
boundaries. The ODS should hold very little historical information and
should focus on maintaining the most up-to-date data needed by the
organization for daily operations. Depending on the application
requirements, the ODS may institute a near real-time data feed from
the source applications. The ODS is expected to be technically
accurate and is considered to be an Authoritative Source. The data it
contains can be used for non-critical needs instead of having to
access the SOR. The more frequently the data is pushed into the
ODS environment, the less reliance there will be on direct access to
SORs for data reporting needs.
• Enterprise Data Warehouse (EDW)
– An Enterprise Data Warehouse (EDW) is responsible for collection
and integration of data from either SORs or from the Operational
Data Store. An EDW has an enterprise scope as it will pull from many
(if not all) SORs. The focus of the data warehouse is to be historical
in nature and in many instances is loaded with a latency (every 24
hours). The data warehouse is created to support historical analytics.
The expectation of the data warehouse is to be exhaustive in the data
it collects with a focus being on collecting and storing of the data.
EnterpriseData
Warehouse
(EDW)
Operational
DataStore
(ODS)
45

• Data Marts
– A Data Mart is a subset of a data warehouse, it
is created to address specific questions and/or
subject area of questions. A Data Mart is built
and tuned to deliver the data to the end users,
it exists to get the data out from the data
warehouse.
Data Mart
46

• Event Data Store
– Is the data store which logs, stores and reports the
discrete business and technical events which occur within
the process. This data store is a critical, and often
overlooked data domain for managing, controlling and
creating transparency into the business processes. The
events are used to report out the overall health of the
processes in both business and technical terms. This
consolidated solution is key to obtaining a 360 view of the
processes.
• Metadata Store
– Metadata is a broad term which includes descriptive
elements in both business and technical terms. It covers:
business terms, data elements descriptions, element
display formats, element valid values, element quality
targets, etc. Metadata is critical to an organization as it
describes the organization’s business and processing
infrastructure in detail. Metadata is entertainingly defined
as “data about the data”. That is, Metadata characterizes
other data and makes it easier to retrieve, interpret and
use information.
Technical
Metadata
Metadata
Store
Business
Metadata
Event
Data
Store
BusOPS
Events
TechOPS
Events
47

Operational i 
n  
 
c 
o 
n 
t 
r 
a 
s 
t  
 
w 
i 
t 
h
Analytic
Subject-Oriented
Databases which are focused on
a single or small set of business
functions
Integrated
Collecting and semantically
aligning data from disparate
sources to achieve a
homogeneous viewVolatile
Data which may change
frequently
Non-Volatile
Data for which entered into the
database will not change
Atomic
Low grain data, each transaction,
each order with all of the
attributes
Aggregate
A summary of multiple orders or
transactions performed to
transform the atomic detail into
more comprehensible information
Current Valued: The data and
the system represents what is
current in this moment; not
yesterday, not last week --- now
Time Variant Data: is marked
and stored with a date/time
element where questions of what
was it yesterday and last week
can be answered
Data Store Characteristics
48

• Q & A?
Outline
50

• 3rd Normal Form (3NF)
– Inmon
• Dimensional
– Kimball
• Data Vault
– Lindstad
Data Structure Design Styles
51

• 3rd Normal Form Modeling
• A mathematical data design  
technique founded in the early  
70s by E.F. Codd.
• Organizes data in simple rows  
and columns - Entities
• Creates connections between the  
entities called relationships to show how the data is inter-related
• It is purest form 3NF removes all data redundancies – a piece of
data is stored only once
• 3NF is based on mathematics, give the same facts to different
modelers; the model should be the same.
• Creates a visual (Entity Relation Diagram - ERD) which may be
understood by less technical personnel
• 3NF is the modeling style most popularly used for operationally
focused data stores.
Design Styles – 3NF
52

Design Styles – Dimensional Modeling
• A data design approach create and
refined by Ralph Kimball in the 80s
• Organizes data in Facts and
Dimensions
– Fact tables record the events (what)
within the  
business domain
– Dimension tables describing who,
when, how and where
• Created to exploit the capabilities
of the relational database to
retrieve and report against large
volumes of data.
• There are 2 variations to
Dimensional Modeling:
– Star Schema
– Snowflake
53

Design Styles – Data Vault
• Newest of the relational database modeling techniques.
• Conceived in the 1990s by Dan Linstedt
• Focuses on linking the data from multiple disparate
locations without forcing the data to be semantically
aligned
NOTE:
There is a Data Ed presentation schedule for 14 October
2014 to cover the details of Data Vault designs!
54

DATA STORE AUDIENCE SERVED BUILD CHARACTERISTICS DESIGN STYLE
O 
P 
E 
R 
A 
T 
I 
O 
N 
A 
L
Master Data
OLTP
ODS
Event
A 
N 
A 
L 
Y 
T 
I 
C
Data Warehouse
Data Mart
Summary/Take Aways
DATA STORE AUDIENCE SERVED BUILD CHARACTERISTICS DESIGN STYLE
O 
P 
E 
R 
A 
T 
I 
O 
N 
A 
L
Master Data
Operations Manager
Operational Analyst
Subject Oriented
Volatile
Atomic
Current Valued
3NF
OLTP
Operational Performer
Operations Manager
Subject Oriented
Volatile
Atomic
Current Valued
3NF
ODS
Operational Manager
Operational Analyst
Executive Consumer
Integrated
Volatile
Atomic
Current Valued
3NF
Event All Personas
Integrated
Volatile
Atomic
Current Valued
3NF
A 
N 
A 
L 
Y 
T 
I 
C
Data Warehouse Data Miner/Scientist
Integrated
Non-volatile
Atomic
Time Variant
3NF trending to
Data Vault
Data Mart
Operational Analyst
Data Analyst
Executive Consumer
Subject Oriented
Non-volatile
Atomic -or- Aggregated
Time Variant
Dimensional
55

• Q & A?
Outline: Design/Manage Data Structures
56

Questions to Ask
• Are you ready for a data
warehouse?
• Foundational Practices
• Is the business environment
constantly evolving?
• Will you get it right the first time?
• 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
• Data Transformations
• How fast do you need results?
• Performance of inserts vs reads
• Project deliverables
57

Upcoming Events
August Webinar:
Data Management Maturity
August 12, 2014 @ 2:00 PM ET/11:00 AM PT
!
Sign up here:
• www.datablueprint.com/webinar-schedule
• www.Dataversity.net
!
!
!
!
!
!
!
!
!
!
!
Brought to you by:
58

Questions?
+ =
59

Why Architectural Models?
• Would you build a house without an architecture sketch?
• Would you like to have an estimate how much your new house is going to cost?
• If you hired a set of constructors from all over the world to build your house, would you
like them to have a common language?
• Would you like to verify the proposals of the construction team before the work gets
started?
• If it was a great house, would you like to build something rather similar again, in
another place?
• Would you drill into a wall of your house without a map of the plumbing and electric
lines?
• Model is the sketch of the system to be built in a project.
• Your model gives you a very good idea of how demanding the implementation work is
going to be!
• Model is the common language for the project team.
• Models can be reviewed before thousands of hours of implementation work will be
done.
• It is possible to implement the system to various platforms using the same model.
• Models document the system built in a project. This makes life easier for the support
and maintenance!
Would you build a house without an architecture
sketch?
Model is the sketch of the system to be built in a
project.
Would you like to have an estimate how much your
new house is going to cost?
Your model gives you a very good idea of how
demanding the implementation work is going to be!
If you hired a set of constructors from all over the
world to build your house, would you like them to
have a common language?
Model is the common language for the project team.
Would you like to verify the proposals of the
construction team before the work gets started?
Models can be reviewed before thousands of hours
of implementation work will be done.
If it was a great house, would you like to build
something rather similar again, in another place?
It is possible to implement the system to various
platforms using the same model.
Would you drill into a wall of your house without a
map of the plumbing and electric lines?
Models document the system built in a project. This
makes life easier for the support and maintenance!
60

Inmon Implementation
61

Kimball Implementation
62

Data Vault Implementation
63

Hybrid Approach
• (http://www.kimballgroup.com/2004/03/03/differences-of-opinion/)
• Learn Data Vault – “dv-in-kimball-bus-architecture”
64

10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

Data-Ed Webinar: Design & Manage Data Structures

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Data-Ed Webinar: Design & Manage Data Structures

Semelhante a Data-Ed Webinar: Design & Manage Data Structures (20)

Mais de DATAVERSITY

Mais de DATAVERSITY (20)

Último

Último (20)

Data-Ed Webinar: Design & Manage Data Structures