Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden

Peter Aiken, Ph.D.

Data Architecture StrategiesConstructing your data garden

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 BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’sMost Important Asset.

The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman

2Copyright 2017 by Data Blueprint Slide #

Bryan Hogan, CDMP• Data Consultant • Certified Data

Management Professional

• Experience in …. – Organizational Data

Management Assessments – Data Strategy Development – ETL Process Development – Reporting Solutions – Software Development

• Worked in …. – Healthcare – Non-Profit – Finance


Data Assets

Financial Assets

RealEstate Assets

Inventory Assets

Non-depletable

Available for subsequent

use

Can be used up

Can be used up

Non-degrading √ √ Can degrade

over timeCan degrade

over time

Durable Non-taxed √ √

Strategic Asset √ √ √ √

We believe ...• Today, data is the most powerful, yet underutilized and poorly

managed organizational asset • Data is your

– Sole – Non-depletable – 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


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]

Data Assets Win!


Data Architecture Requirements

• Context: Data Management/DAMA/DM BoK/CDMP?

• What is Data/Information Architecture?

• Why is Data/Information Architecture Important?

• Data Engineering/Leverage

• NGO Data Architecture Case Study

• Take Aways, References & Q&A

Arc

hite

ctur

e Ja

rgon


You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA

Foundational Data Management Practices

Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

Technologies

Capabilities

Copyright 2017 by Data Blueprint Slide # 7

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


Manage data coherently

Manage data assets professionally

Data life cycle management

Organizational support

Data Quality

The DAMA Guide to the Data Management Body of Knowledge


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

• Organized around several environmental elements

Architecture• Things

– (components)

• The functions of the things – (individually)

• How the things interact – (as a system, towards a goal)


Data Architecture Management• Definition

– Defining the data needs of the enterprise and designing the master blueprints to meet those needs

• Goals

1. To plan with vision and foresight to provide high-quality data

2. To identify and define common data requirements

3. To design conceptual structures and plans to meet the current and long-term data requirements of the enterprise


from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Data Architecture Management


from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International









Architecture is both the process and product of planning, designing and constructing space that reflects functional, social, and aesthetic considerations. A wider definition may comprise all design activity from the macro-level (urban design, landscape architecture) to the micro-level (construction details and furniture). In fact, architecture today may refer to the activity of designing any kind of system and is often used in the IT world.


Architecture

Architectures: here, whether you like it or not


deviantart.com

• All organizations have architectures – Some are better

understood and documented (and therefore more useful to the organization) than others

Architecture Representation

• Architectures are the symbolic representation of the structure, use and reuse of resources

• Common components are represented using standardized notation

• Architectures are sufficiently detailed to permit both business analysts and technical personnel to separately read the same model, and come away with a common understanding


Understanding• A specific definition

– 'Understanding an architecture'

– Documented and articulated as a (digital) blueprint illustrating the commonalities and interconnections among the architectural components

– Ideally the understanding is shared by systems and

humans


OrganizationalArchitectures• Amazon

– Traditional structure

• Google – Team of 3

• Facebook – Do you really have

a structure?

• Microsoft – Eliminate their own

products

• Apple – Everything

revolves around one individual

• Oracle – Buys one company

after another


• Process Architecture – Arrangement of inputs -> transformations = value -> outputs – Typical elements: Functions, activities, workflow, events, cycles, products, procedures

• Systems Architecture – Applications, software components, interfaces, projects

• Business Architecture – Goals, strategies, roles, organizational structure, location(s)

• Security Architecture – Arrangement of security controls relation to IT Architecture

• Technical Architecture/Tarchitecture – Relation of software capabilities/technology stack – Structure of the technology infrastructure of an enterprise, solution or system – Typical elements: Networks, hardware, software platforms, standards/protocols

• Data/Information Architecture – Arrangement of data assets supporting organizational strategy – Typical elements: specifications expressed as entities, relationships, attributes,

definitions, values, vocabularies

Typically Managed Organizational Architectures


• The underlying (information) design principals upon which construction is based

– Source: http://architecturepractitioner.blogspot.com/

• … are plans, guiding the transformation of strategic organizational information needs into specific information systems development projects

– Source: Internet

• A framework providing a structured description of an enterprise’s information assets — including structured data and unstructured or semistructured content — and the relationship of those assets to business processes, business management, and IT systems.

– Source: Gene Leganza, Forrester 2009

• "Information architecture is a foundation discipline describing the theory, principles, guidelines, standards, conventions, and factors for managing information as a resource. It produces drawings, charts, plans, documents, designs, blueprints, and templates, helping everyone make efficient, effective, productive and innovative use of all types of information."

– Source: Information First by Roger & Elaine Evernden, 2003 ISBN 0 7506 5858 4 p.1.

• Defining the data needs of the enterprise and designing the master blueprints to meet those needs

– Source: DM BoK


Information Architecture









Data Architecture – A Useful Definition


• Common vocabulary expressing integrated requirements ensuring that data assets are stored, arranged, managed, and used in systems in support of organizational strategy [Aiken 2010]

Data Architecture – A More Useful Definition


• A structure of data-based information assets supporting implementation of organizational strategy (or strategies) [Aiken 2010]

• Most organizations have data assets that are not supportive of strategies - i.e., information architectures that are not helpful

• The really important question is: how can organizations more effectively use their information architectures to support strategy implementation?

Database Architecture Focus


Program F

Program E

Program DProgram G

Program H

Program I

Applicationdomain 2Application

domain 3

databasearchitecture

engineeringeffort

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


Why is Data Architecture Important?• Poorly understood

– Data architecture asset value is not well understood

• Inarticulately explained

– Little opportunity to obtain learning and experience

• Indirectly experienced

– Cost organizations millions each year in productivity, redundant and siloed efforts

– Example: Poorly thought out software purchases


Higher res image available?

Moon Lighting

Practical Application of Data Architecting

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 JOB CLASS;

BR3) Zero, one, or more EMPLOYEES can be associated with one POSITION

BR4) One or more POSITIONS can be associated with one JOB CLASS.


Job Sharing

Running Query


Optimized Query


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


Death by 1000 Cuts


Lack of coherent data architecture is a hidden expense• How does poor data architecture cost money? • 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?

– They cannot be helpful as long as their structure is unknown

• Organizations spend between 20 - 40% of their IT budget evolving their data - including: – Data migration

• Changing the location from one place to another

– Data conversion • Changing data into another form, state, or product

– Data improving • "Inspecting and manipulating, or re-keying data to prepare it for

subsequent use" - Source: John Zachman


PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’sMost Important Asset.

• 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 – The more easily it adapts to change, the resource utilization

• 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 Architecting for Business Value


Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2

Good Architectural Foundation?


Poor Quality Foundation


What they think they are purchasing!


Levels of Abstraction, Completeness and Utility


• Models more downward facing - detail

• Architecture is higher level of abstraction - integration

• In the past architecture attempted to gain complete (perfect) understanding

– Not timely

– Not feasible

• Focus instead on architectural components

– Governed by a framework

– More immediate utility

• http://www.architecturalcomponentsinc.com

Too Much Detail


What do you use an information architecture for?


Illustration by murdock23 @ http://designfestival.com/information-architecture-as-part-of-the-web-design-process/

Web Developers Understand IA


http://www.jeffkerndesign.com

Web Developers Understand IA


http://www.jeffkerndesign.com

How are data structures expressed as architectures?


A B

C D

A B

C D

A

D

C

B

• Details are organized into larger components

• Larger components are organized into models

• Models are organized into architectures

How are Data Models Expressed as Architectures?


More Granular

More Abstract

• 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?

Data Data

Data

Information

Fact Meaning

Request

Data must be Architected to Deliver Value

[Built on definitions from 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 STRATEGIC USES. 6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.


Wisdom & knowledge are often used synonymously

Data

Data

Data Data

How do data structures support organizational strategy?

• Two answers

– Achieving efficiency and effectiveness goals

– Providing organizational dexterity for rapid implementation


Computers

Human resources

Communication facilities

Software

Managementresponsibilities

Policies,directives,and rules

Data

What Questions Can Data Architectures Address?• How and why do the data

components interact? • Where do they go? • When are they needed? • Why and how will the

changes be implemented? • What should be managed

organization-wide and what should be managed locally?

• What standards should be adopted?

• What vendors should be chosen?

• What rules should govern the decisions?

• What policies should guide the process?


! ! ! !

Data Architectures produce and are made up of information models that are developed in response to organizational needs


Organizational Needs

become instantiated and integrated into an Data/Information

Architecture

Informa(on)System)Requirements

authorizes and articulates sa

tisfy

spe

cific

org

aniz

atio

nal n

eeds









Data Leverage

• Permits organizations to better manage their sole non-depletable, 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


Less ROT

Technologies

Process

People

Architecture Evolution


Conceptual Logical Physical

Validated

Not UnValidated

Every change can be mapped to a transformation in this framework!

IT Project or Application-Centric Development

Original articulation from Doug Bagley @ Walmart


Data/Information

ITProjects

Strategy

• In support of strategy, organizations implement IT projects

• Data/information are typically considered within the scope of IT projects

• Problems with this approach: – Ensures data is formed to the

applications and not around the organizational-wide information requirements

– Process are narrowly formed around applications

– Very little data reuse is possible

Data-Centric Development

Original articulation from Doug Bagley @ Walmart


ITProjects

Data/Information

Strategy

• In support of strategy, the organization develops specific, shared data-based goals/objectives

• These organizational data goals/objectives drive the development of specific IT projects with an eye to organization-wide usage

• Advantages of this approach: – Data/information assets are developed from an

organization-wide perspective

– Systems support organizational data needs and compliment organizational process flows

– Maximum data/information reuse

Engineering

Architecture

Engineering/Architecting Relationship• Architecting is used to

create and build systems too complex to be treated by engineering analysis alone

• Architects require technical details as the exception

• Engineers develop the technical designs

• Craftsman deliver components supervised by: – Building Contractor

– Manufacturer

Copyright 2017 by Data Blueprint Slide # 53

USS Midway & Pancakes

What is this?


• It is tall • It has a clutch • It was built in 1942 • It is still in regular use!

Engineering Standards


Architectural Work ProductComponents may be defined as:

• The intersection of common business functionality and the subsets of the organizational technology and data architectures used to implement that functionality

• Component definition is an important activity because CM2 component engineering is focused on an entire component as an analysis unit. A concrete example of a component might be

– The business processes, the technology and the data supporting organizational human resource benefits operations. This same component could be described simply as the "PeopleSoft™ version 7.5 benefits module implemented on Windows 95." illustrates the integration of the three primary PeopleSoft metadata structures describing the: business processes used to organization the work flow, menu navigation required to access system functionality, and data which when combined with meanings provided by the panels provided information to the knowledge workers.


SystemProcess

Process2

Process1

Process3

Subprocess1.1

Subprocess1.2

Subprocess1.3

Hierarchical System Functional Decomposition


Level 1 Level 2 Level 3Pay Employment Recruitmentand Selectionpersonnel Personnel Employee relations

administration Employee compensation changesSalary planningClassification and payJob evaluationBenefits administrationHealth insurance plansF lexible spending accountsGroup life insurance

Retirement plansPayroll Payroll administration

Payroll processingPayroll interfaces

Development N/ATrainingadministration

Career planning and skillsinventoryWork group activities

Health andsafety

Accidents and workerscompensationHealth and safety programs

A three-level decomposition of the model views from a governmental pay and personnel scenario


H ealth car e system1 Patient administration 1.1 R egistration1.2 Admission1.3 Disposition1.4 Transfer1.5 M edical record1.6 Administration1.7 Patient bi l l ing1.8 Patient affairs1.9 Patient management2 Patient appointments

and sche d ul ing 2.1 Create or maintain

schedules2.2 Appoint patients2.3 R ecord patient encounter2.4 I dentify patient2.5 I dentify health care

provider3 Nursing 3.1 Patient care3.2 Unit management4 Laboratory 4.1 R esults reporting4.2 Specimen processing4.3 R esult entry processing4.4 Laboratory management4.5 Workload support5 Pharmacy 5.1 Unit dose dispensing5.2 Control led Drug

I nventory5.3 Outpatient

6 R adiology 6.1 Schedul ing6.2 E xam processing6.3 E xam reporting6.4 Special interest and

teaching6.5 R adiology workload

reporting7 C l inical dietetics 7.1 E stabl ish parameters7.2 R eceive diet orders8 Order entry and r e sults 8.1 R eporting8.2 E nter and maintain

orders8.3 Obtain results8.4 R eview patient

information8.5 C l inical desktop9 System management 9.1 Logon and security

management9.2 Archive run

M anagement9.3 Communication software9.4 M anagement9.5 Site management10 Faci l ity qual ity assurance 10.1 Provider credential ing10.2 M onitor and evaluation

A relatively complex model view decomposition


DSS

"Governors"

Taxpayers Clients

Vendors Program Deliver

Data model is comprised of model views

DSS Strategic Data Model Taxpayer view

Client view

Governance view

Program Delivery view

Vendor view


Taxpayer view

Payments Taxpayers

SocialServicePrograms

TaxpayerBenefits


Client view


Payments

Clients ClientBenefits

LocalWellfareAgencies

Governance view


Payments


GovernmentalResources

Governance Governments

State Boardof SocialServices

PolicyApproval


Clients

ServiceDeliveryPartners


Program Delivery view


Payments


Clients


GoodsandServices

Vendors

Vendor view



Governance Governments Payments Taxpayers




TaxpayerBenefits

PolicyApproval

ServiceDeliveryPartners


GoodsandServices

Vendors

DSS Strategic Level Data Model


Payments



Governance Governments


PolicyApproval

Payments











• Non-Governmental Organization (NGO) • Non-Profit • Industry

– Address Priority Health Concerns for Developing Countries • HIV & AIDS • Malaria • Etc…

– Provide Leadership Training – Health Information System Management

• Function – Project Management and Design for

Health Care Implementations

• Operates – Globally (30 + Countries)

Background


Problem• Data needed to make key business decisions was not

accessible across the Enterprise

– Timeliness

– Accuracy

– Data Isolation


Root Cause• No Enterprise-Wide understanding of its data assets

– Conceptual Data Model

• NGO does not have a common vocabulary

– Enterprise-Wide Taxonomy

• NGO lacks existing System and Data Architecture

– Vision

– Not Aligned with Business Model

– “Shiny Object Syndrome”

– Minimal Integration


Solution• Vision and Purpose

– Data Architecture

• Business Glossary

• Enterprise Conceptual Data Model


Vision and Purpose


TARGET STATE VISIONCOLLABORATION & WIP DOCUMENTS

Talent Management

Business Development

Project Management

CAPTURE DATA

INTEGRATE DATA

Talent Management

Financial Management

Business Development

Project Management

CREATE REPORTS AND PERFORM BI

STORE CORPORATE DATA

MANAGE CONTENT

Financial Management

DATA GOVERNANCE

• 100,000 ft. View • Represents the processes,

procedures, and technologies that make up the Components

• Federated Data Architecture (FDA) • FDA supports the business

strategy • Set of entities (Projects)

that have a level of autonomy to support its goal while a unifying entity (Shared Services from Corporate) provides a framework and definition on how data is to managed and captured

Business Glossary


Entity Description Domain AreaDonor Funder Business DevelopmentSolicitations Need for Work Business DevelopmentSolicitations Proposal Response to Need for Work Business DevelopmentPre-Positioning Intelligence Gathering Business DevelopmentAward/Sub-Award Funding Vehicle Business DevelopmentTerms Conditions Details about a Funding Vehicle Business DevelopmentBudget Amount of Money Available Business DevelopmentWork Plan Set of Activities to Complete Business DevelopmentPMP Monitoring Plan for Activities Business Development

Project

An NGO Project is defined as a self-contained set of interventions or activities with the following characteristics:a) an external client;b) purchase order, contract or agreement;c) expected deliverables, outcomes and results;d) a beginning and end date of implementation; e) an approved budget; and full and/or part time NGO staff Project Management

Geographic Area Project Management

Office LocationsLocation in which a Central Office resides Project Management

Project Roles Project ManagementProject Artifacts Project ManagementProject Budget Project ManagementProject Work Plan Project ManagementMilestones Schedule of completed activities Project ManagementMonitoring Plan to measure Activities Project ManagementEvaluation Assessment of Activities Project ManagementIndicators Target of Outcome Project Management

OutcomesStatement of what needs to be accomplished Project Management

Acct Receivable Payments to NGO Financial ManagementChart of Accounts Defined Accounts Financial ManagementPayroll Process to Pay Worker Financial ManagementSupplier Provider of Goods or Service Financial ManagementContract Binding Agreement Financial ManagementPurchase Order Statement of Good or Service Financial ManagementPerformance Level of Success Talent ManagementBenefits Talent ManagementSkills Talent Management

WorkerPerson who has been hired by NGO Talent Management

Candidate Potential hire of NGO Talent Management

• Start of Enterprise Taxonomy

• Defines Initial Entities for Conceptual Data Model

• Engages the Business Community to Validate Entities and provide meaningful business definitions

Ente

rpris

e C

once

ptua

l Dat

a M

odel • Linkages

across Business Functions

• How Data flows throughout Enterprise

• Impact from Data Changes

• Defines Common Vocabulary

• Aligning the Data to support the Organizational Strategy


Business Value• Supports Organizational Strategy

• Reduced IT Costs

• Data Asset Knowledge and Reuse

• Accurate and Timely Reporting


Supports Organizational Strategy• Defining a common vocabulary across the enterprise

increases cohesion between the Business and IT.

• Cohesion allows IT to effectively support the Organizational Strategy

• Understanding the business’s needs


Understanding

Resized & moved "Understanding"

Reduced IT Costs• Data Architecture guides IT on software implementations

– Mitigates “poor” software purchases

– Reduces cost of implementations

• Maintaining and Managing the Data Landscape

– A defined Data Architecture allows IT to manage and maintain the critical pieces of the Data Landscape

– Reduces cost of trying to manage and maintain everything


Data Asset Knowledge and Reuse• Knowledge of how the Organization’s Data can be

leveraged

– Increased Organizational Learning

• Identified Key Integration Points

– Allows IT to focus on the critical Data Assets

– Increases Re-Use of Data Assets for future Integrations

• Identified Impact to Data Flows

– Allows IT to plan for future implementations

– Reduces impact to the Organizational existing Data Assets


• Reduce Time Building Reports

– Faster Decision Making

– Single Source of Truth

• Less “Massaging” of Data

– Increased Productivity from Knowledge Workers

– Decreased Errors from compiling redundant data

Accurate and Timely Reporting


DATA









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!

Why Architect Data?


Take Aways• What is an information architecture?

– A structure of data-based information assets supporting implementation of organizational strategy

– Most organizations have data assets that are not supportive of strategies - i.e., information architectures that are not helpful

– The really important question is: how can organizations more effectively use their information architectures to support strategy implementation?

• What is meant by use of an information architecture? – Application of data assets towards organizational strategic objectives – Assessed by the maturity of organizational data management practices – Results in increased capabilities, dexterity, and self awareness – Accomplished through use of data-centric development practices (including

taxonomies, stewardship, and repository use)

• How does an organization achieve better use of its information architecture? – Continuous re-development; the starting point isn't the beginning – Information architecture components must typically be reengineered – Using an iterative, incremental approach, typically focusing on one component at a time

and applying formal transformations



OrganizationalStrategy

Data Strategy

IT Projects

Organizational Operations

Data Governance

Data Strategy and Data Governance in ContextData

asset support for organizational

strategy

What the data assets do to support strategy

How well the data strategy is working

Operational feedback

How data is delivered by IT

How IT supports strategy

Other aspects of

organizational strategy

Questions?


+ =

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Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden