Peter Aiken, Ph.D.
Data Modeling Fundamentals10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060 804.521.4056
Data Modeling Fundamentals
2Copyright 2016 by Data Blueprint Slide #
This presentation provides you with an understanding of the data modeling and data development components of data management. Participants will understand how the analysis, design, implementation, deployment, and maintenance of data solutions should be approached in order to maximize the full value of the enterprise data resources and activities. Architecting in quality is imperative at this level and complements a subset of project activities within the system development lifecycle (SDLC) focused on defining data requirements, designing data solution components, and implementing these components. Participants will understand the difficulties organizations experience when interacting with data development efforts and how to best incorporate these efforts into specific data projects.
Date: June 14, 2016
Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D. & John Sells
Executive Editor at DATAVERSITY.net
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Shannon Kempe
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• 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 – …
Peter Aiken, Ph.D.
• 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 BILLINGSFOREWORD BY JOHN BOTTEGA
MONETIZINGDATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset
Peter Aiken andMichael Gorman
6Copyright 2016 by Data Blueprint Slide #
John Sells• Data consultant with a background in Project
Management, Data Management, Verification and Validation, as well as Application Development
• Certified Data Management Professional • Experience working with large global clients
across many business functions • Skill-set includes in-depth analysis of clients’
business processes, analysis of data and data sources, and development and communication of data-centric tailored solutions that add business value
• Expertise focuses on eliciting business and technical requirements and facilitating communication between the business users and technical experts, including all levels of management
• Helped clients improve data flow logistics, develop data quality programs, implement data governance programs, and design and implement data warehouses and BI platforms for organizational divisions.
7Copyright 2016 by Data Blueprint Slide #
8Copyright 2016 by Data Blueprint Slide #
Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
UsesUsesReuses
What is data management?
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Sources
Data Engineering
Data Delivery
DataStorage
Specialized Team Skills
Data Governance
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 • Engineering • Storage • Delivery • Governance
When executed, engineering, storage, and delivery implement governance
Note: does not well-depict data reuse
What is data management?
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Sources
Data Engineering
Data Delivery
DataStorage
Specialized Team Skills
Resources
(optimized for reuse)
Data Governance
Ana
lytic
Insi
ght
Specialized Team Skills
Data$Management$Strategy
Data Management GoalsCorporate CultureData Management FundingData Requirements Lifecycle
DataGovernance
Governance ManagementBusiness GlossaryMetadata Management
DataQuality
Data Quality FrameworkData Quality Assurance
DataOperations
Standards and ProceduresData Sourcing
Platform$&$Architecture
Architectural FrameworkPlatforms & Integration
Supporting$Processes
Measurement & AnalysisProcess ManagementProcess Quality AssuranceRisk ManagementConfiguration Management
Component Process$Areas
DMM℠ Structure of 5 Integrated DM Practice Areas
Data architecture implementation
Data Governance
Data Management
Strategy
Data Operations
PlatformArchitecture
SupportingProcesses
Maintain fit-for-purpose data, efficiently and effectively
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Manage data coherently
Manage data assets professionally
Data life cycle management
Organizational support
Data Quality
You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data 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 Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
Copyright 2016 by Data Blueprint Slide # 12
Data Management Body of Knowledge
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Data Management
Functions
DAMA DM BoK: Data Development
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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
16Copyright 2016 by Data Blueprint Slide #
Why Modeling
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• 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!
Use Models to
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• Store and formalize information
• Filter out extraneous detail
• Define an essential set of information
• Help understand complex system behavior
• Gain information from the process of developing and interacting with the model
• Evaluate various scenarios or other outcomes indicated by the model
• Monitor and predict system responses to changing environmental conditions
• Goal must be shared IT/business understanding – No disagreements = insufficient communication
• Data sharing/exchange is largely and highly automated and thus dependent on successful engineering – It is critical to engineer a sound foundation of data modeling basics
(the essence) on which to build advantageous data technologies
• Modeling characteristics change over the course of analysis – Different model instances may be useful to different analytical problems
• Incorporate motivation (purpose statements) in all modeling – Modeling is a problem defining as well as a problem solving activity - both are inherent to
architecture
• Use of modeling is much more important than selection of a specific modeling method • Models are often living documents
– It easily adapts to change
• Models must have modern access/interface/search technologies – Models need to be available in an easily searchable manner
• Utility is paramount – Adding color and diagramming objects customizes models and allows for a more engaging and
enjoyable user review process
Data Modeling for Business Value
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Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2
Data Modeling Ensures Interoperability• Who makes decisions about the range and scope of
common data usage?
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Program F
Program E
Program DProgram G
Program H
Program I
Applicationdomain 2Application
domain 3
databasearchitectureengineering
effort
Data
DataData
Data
Data Data
Data
Focus of asoftware
architectureengineering
effort Program A
Program B
Program C
Program F
Program E
Program DProgram G
Program H
Program I
Applicationdomain 1
Applicationdomain 2Application
domain 3
Data
Focus of a
Data
Data
Data Architecture Focus has Greater Potential Business Value• Broader focus
than either software architecture or database architecture
• Analysis scope is on the system wide use of data
• Problems caused by data exchange or interface problems
• Architectural goals more strategic than operational
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Primary Deliverables become Reference Material
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from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Data Modeling Definition• Modeling = Analysis and design
method used to – Define and analyze data requirements – Design data structures that support these
requirements • Model = set of data specifications
and related diagrams that reflect requirements and designs
– Representation of something in our environment
– Employs standardized text/symbols to represent data attributes (grouped into data elements) and the relationships among them
– Integrated collection of specifications and related diagrams that represent data requirements and design
23Copyright 2016 by Data Blueprint Slide #
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Data Modeling and Data Architecture• Data modeling is used to articulate data architecture
components • Data architectures are comprised of components – usually
expressed as models • Styles of data modeling exist – this is a challenge
– IE or information engineering
– IDEF1X used by DoD
– ORM or object role modeling
– UML or unified modeling language
• Data models are useful – In stand-alone mode
– As components of a larger information architecture
24Copyright 2016 by Data Blueprint Slide #
25Copyright 2016 by Data Blueprint Slide #
Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
Standard definition reporting does not provide conceptual context
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Bed
Something you sleep in
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
The Power of the Purpose Statement
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• 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 • Associations with other data
items; this one is read as "One room contains zero or many beds"
11
DISPOSITION Data Map
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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.
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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
DISCHARGEA 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
30Copyright 2016 by Data Blueprint Slide #
Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
• Models
• Are usually for the purpose of understanding
• Can be
– Equations
– Simulations including video games
– Physical models
– Mental models
Models as an Aid to Understanding
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What is a model?
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drawcritique
testdialog
select decidefilter
summarizedesign
rankreview cluster
generate evaluate
list
visible to participants
Structure for organizing things
Framework for decision making
Requires tools for problem solving and decision making
Easy to review and validate
graphic
text
Prototype and mockupFramework for understanding and design
Source: Ellen Gottesdiener www.ebgconsulting.com
Don’t Tell Them You Are Modeling!
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• Just write some stuff down
• Then arrange it • Then make
some appropriate connections between your objects
Keep them focused on the purpose
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• The reason we are locked in this room is to: – Mission: Review proposal from
voice over IP providers • Outcome: Walk out the door with the
top two proposals selected and scheduled personal presentations from each.
– Mission: Discuss logo ideas for the Bore No More movement • Outcome: We will walk out the door
when we identify the top three traits that represent the Bore No More brand.
– Mission: Update all employees on the retirement plan options • Outcomes: Confirm that all team
members took part in the meeting and have access to review their plans privately with a financial consultant.
35Copyright 2016 by Data Blueprint Slide #
Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
Entity Relationship View
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C U S T O M E R
coins
soda
machine
(adapted from [Davis 1990])
Entity Relationship View
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(adapted from [Davis 1990])
entity thing about which we maintain information
object entity encapsulated with attributes and functions
C U S T O M E R soda
machine
coinreturn
deposits
selects
given to
dispenses
coins
Modeling In Support of Requirements
Person Job Class
Employee Position
BR1) Zero, one, or more EMPLOYEES can be associated
with one PERSON
BR2) Zero, one, or more EMPLOYEES can be associated with one POSITION
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Job Sharing
Moon Lighting
39Copyright 2016 by Data Blueprint Slide #
Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
Data Modeling• Modeling = complex process involving interaction
between people and with technology that don’t compromise the integrity or security of the data
– Good data models accurately express and effectively communicate data requirements and quality solution design
• Modeling approach (guided by 2 formulas):
– Purpose + audience = deliverables
– Deliverables + resources + time = approach
40Copyright 2016 by Data Blueprint Slide #
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Data Models Facilitate• Formalization
– Data model documents a single, precise definition of data requirements and data-related business rules
• Communication – Data model is a bridge to understanding data
between people with different levels and types of experience.
– Helps understand business area, existing application, or impact of modifying an existing structure
– May also facilitate training new business and/or technical staff
• Scope – Data model can help explain the data concept and scope of
purchased application packages
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ANSI-SPARK 3-Layer Schema
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For example, a changeover to a new DBMS technology. The database administrator should be able to change the conceptual or global structure of the database without affecting the users.
1. Conceptual - Allows independent customized user views:
– Each should be able to access the same data, but have a different customized view of the data.
2. Logical - This hides the physical storage details from users:
– Users should not have to deal with physical database storage details. They should be allowed to work with the data itself, without concern for how it is physically stored.
3. Physical - The database administrator should be able to change the database storage structures without affecting the users’ views:
– Changes to the structure of an organization's data will be required. The internal structure of the database should be unaffected by changes to the physical aspects of the storage.
Conceptual Models• Business
focused • Entity level • Provides focus,
scope, and guidance to modeling effort
• Sometimes thrown away - rarely maintained
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Logical Models• Required to achieve the transition
from conceptual to physical • Developed to the attribute level via
3rd normal form - to a define level of understandability
• Logical models are developed to be refined to until it becomes a solution - sometimes purchased (as in EDW) always requires tailoring
• Used to guarantee the rigor of the data structures by formally describing the relationship between data items in a strong fashion - more often maintained
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Physical Models• Becomes the blueprints for
physical construction of the solution
• Blueprints are used for future maintenance of the solution
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Model Evolution (better explanation)
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As-is To-be
TechnologyIndependent/Logical
TechnologyDependent/Physical
abstraction
Other logical as-is data architecture components
As Is InformationRequirements Assets
As Is Data Design Assets As Is Data Implementation Assets
Exi
stin
gN
ew
Modeling in Various Contexts
O2 Recreate Data Design
Reverse Engineering
Forward engineering
O5 Reconstitute Requirements
O9 Reimplement
Data
To Be Data Implementation Assets
O8 RedesignData
O4 Recon-stitute Data
Design
O3 Recreate Requirements
O6 Redesign Data
To Be Design Assets
O7 Re- developRequire-ments
To Be Requirements Assets
O1 Recreate Data Implementation
Metadata
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Model Evolution Framework
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Conceptual Logical Physical
Goal
Validated
Not Validated
Every change can be mapped to a transformation in this framework!
Preliminaryactivities Modeling
cyclesWrapupactivities
Evidencecollection &
analysis
Projectcoordinationrequirements
Targetsystemanalysis
Modelingcyclefocus
Activity
Refinement
Collection
Analysis
Validation
Declining coordination requirements
Increasing amounts of target system analysis
Preliminaryactivities Modeling
cyclesWrapupactivities
Evidencecollection &
analysis
Projectcoordinationrequirements
Targetsystemanalysis
Modelingcyclefocus
Activity
Refinement
Collection
Analysis
Validation
Declining coordination requirements
Increasing amounts of target system analysis
Preliminaryactivities Modeling
cyclesWrapupactivities
Evidencecollection &
analysis
Projectcoordinationrequirements
Targetsystemanalysis
Modelingcyclefocus
Activity
Refinement
Collection
Analysis
Validation
Declining coordination requirements
Increasing amounts of target system analysis
Preliminaryactivities Modeling
cyclesWrapupactivities
Evidencecollection &
analysis
Projectcoordinationrequirements
Targetsystemanalysis
Modelingcyclefocus
Activity
Refinement
Collection
Analysis
Validation
Declining coordination requirements
Increasing amounts of target system analysis
Relative use of time allocated to tasks during ModelingPreliminary
activities Modelingcycles
Wrapupactivities
Evidencecollection &
analysis
Projectcoordinationrequirements
Targetsystemanalysis
Modelingcyclefocus
Activity
Refinement
Collection
Analysis
Validation
Declining coordination requirements
Increasing amounts of target system analysis
49Copyright 2016 by Data Blueprint Slide #
50Copyright 2016 by Data Blueprint Slide #
Data Modeling Fundamentals1. Data Management Overview 2. Why data modeling & what is it? 3. The power of the purpose statement 4. Understanding how to contribute to
organizational challenges beyond traditional data modeling
5. Guiding problem analyses using data analysis
6. Using data modeling in conjunction with architecture/engineering techniques
7. How to utilize data modeling in support of business strategy
8. Take Aways, References & Q&A
Tweeting now: #dataed
How do Data Models 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
– Achieving efficiency and effectiveness goals
– Providing organizational dexterity for rapid implementation
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Design Styles – 3NF• 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
• 3NF removes data redundancies – a piece of data is stored only once
• 3NF is based on mathematics, give the same facts to different modelers; the models they produce should be very similar.
• 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.
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Design Styles – Dimensional• Created and refined by Ralph
Kimball in the 80s. • Organizes data in Facts
and Dimensions. Fact tables record the events (what) within the business domain and the Dimension tables describe who, when, how and where.
• The data design style was created to exploit the capabilities of the relational database to retrieve and report against large volumes of data.
• Dimensional modeling sacrifices storage efficiency for analytical processing speed
• There are 2 variations to Dimensional Modeling: Star Schema and Snowflake
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Design Styles – Data Vault• One of the newer relational database modeling techniques • Data Vault modeling was conceived in the 1990s by Dan
Linstedt • Data Vault models are designed for central data
warehouses that store non-volatile, time-variant, atomic data
• Relationships are defined through Link structures which promote flexibility and extensibility
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Data Models Used to Support Strategy• Flexible, adaptable data structures • Cleaner, less complex code • Ensure strategy effectiveness measurement • Build in future capabilities • Form/assess merger and acquisitions strategies
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EmployeeType Employee
SalesPerson Manager Manager
Type
StaffManager
Line Manager
Adapted from Clive Finkelstein Information Engineering Strategic Systems Development 1992
Mission and Purpose• Develop, deliver and support products and services which
satisfy the needs of customers in markets where we can achieve a return on investment at least 20% annually within two years of market entry
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Mission Model Analysis
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Identify Potential Goals
G1. Market Analysis G2. Market Share G3. Innovation G4. Customer Satisfaction G5. Product Quality G6. Product Development G7. Staff Productivity G8. Asset Growth G9. Profitability
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Mission Model Analysis
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Next Step
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Market
MarketCustomer
ProductNeed
Need
CustomerProduct
MarketNeed
ProductCustomer
CustomerNeed
MarketProduct
Subsequent Step for Business Value
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Market
MarketPerformance
ProductPerformance
Need
CustomerPerformance
Need Performance
ProductCustomer
Performance
Questions?
It’s your turn! Use the chat feature or Twitter (#dataed) to submit
your questions to Peter & John now!
+ =
62Copyright 2016 by Data Blueprint Slide #
Upcoming EventsGoverning the Business Vocabulary – aligning the requirements of the business and IT to achieve a shared understanding of data across an organization June 27, 2016 @ 8:30 AM ETSan Diego, CAhttp://www.debtechint.com
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Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net
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