Data-Ed Webinar: Data Architecture Requirements

Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken, will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.

Welcome: Data Architecture Requirements

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Copyright 2015 by Data Blueprint

Program F

Program E

Program DProgram G

Program H

Program I

Applicationdomain 2Application

domain 3

Date: March 9, 2015 Time: 2:00 PM ET Presented by: Peter Aiken, PhD

Shannon Kempe

Executive Editor at DATAVERSITY.net

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Two Most Commonly Asked Questions

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1. Will I get copies of the slides after the event?

2. Is this being recorded so I can view it afterwards?

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Peter Aiken, Ph.D.

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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 - 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’s

Most Important Asset.

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

Peter Aiken andMichael Gorman

We believe ...

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 √ √ √ √

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• 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

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]

Presented by Peter Aiken, Ph.D.

Data Architecture Requirements


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

• What is Data/Information Architecture?

• Why is Data/Information Architecture Important?

• Data Engineering/Leverage

• Example: Software Package Implementation

• Example: Donation Center Processing

• Example: Text Mining/Analytics

• Take Aways, References & Q&A


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Maslow's Hierarchiy of Needs

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

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy

Advanced Data

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

Foundational Data Management Practices

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Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

Technologies

Capabilities

Maintain fit-for-purpose data, efficiently and effectively

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Manage data coherently

Manage data assets professionally

Data architecture implementation

Data lifecycle implementation

Organizational support

DMM℠ Structure of 5 Integrated DM Practice Areas

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

Data Architecture Management

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

What is the 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


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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

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

Understanding

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• 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

• 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

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Information Architecture


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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]

Vocabulary is Important-Tank, Tanks, Tankers, Tanked

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How one inventory item proliferates data throughout an organization's data architecture

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555 Subassemblies & subcomponents

17,659 Repair parts or Consumables

System 1:18,214 Total items

75 Attributes/ item1,366,050 Total attributes

System 2 47 Total items

15+ Attributes/item720 Total attributes

System 3 16,594 Total items 73 Attributes/item

1,211,362 Total attributes

System 4 8,535 Total items

16 Attributes/item136,560 Total attributes

System 5 15,959 Total items

22 Attributes/item351,098 Total attributes

Total for the five systems show above:59,350 Items

179 Unique attributes3,065,790 values

Business Value: Agency units are carrying $1.5 billion worth of expired inventory

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• Generates unnecessary costs & negative impacts on operations, including: – Resources are focused on non-value added tasks of maintaining obsolete inventory, which

creates distractions to the agency’s main mission

• Storage – Physical/real estate needed to house items

• Handling – Includes transportation and human resources

dedicated to moving, maintaining, counting and securing outdated inventory

• Opportunity – Inventory could be returned to manufacturer or

sold to free up financial assets for more needed and critical supplies

• Systemic – Cost of inventorying information and maintaing

paper or electronic records which should be used to support mission-critical acquisitions and distribution

• Maintenance – Repairing of expired items

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?

What do you use an information architecture for?

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

Database Architecture Focus


Program F

Program E

Program DProgram G

Program H

Program I


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


domain 3

Data

Focus of a

Data

Data

Data Architecture Focus has Greater Potential Business Value

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• 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?

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• 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

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healthcare.gov

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• 55 Contractors! • "Anyone who has written a

line of code or built a system from the ground-up cannot be surprised or even mildly concerned that Healthcare.gov did not work out of the gate," Standish Group International Chairman Jim Johnson said in a recent podcast.

• "The real news would have been if it actually did work. The very fact that most of it did work at all is a success in itself."

• Software programmed to access data using traditional data management technologies

• Data components incorporated "big data technologies"http://www.slate.com/articles/technology/bitwise/2013/10/problems_with_healthcare_gov_cronyism_bad_management_and_too_many_cooks.html

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.

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Job Sharing

Running Query

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Optimized Query

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


Death by 1000 Cuts


• 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

Lack of coherent data architecture is a hidden expense

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Data Architecting 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

• 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

Architecture Example


Poor Quality Foundation

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What they think they are purchasing!

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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


Web Developers Understand IA


http://www.jeffkerndesign.com

How are data structures expressed as architectures?

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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?

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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

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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?

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• 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

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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


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Data Leverage

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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

Architecture Evolution

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Conceptual Logical Physical

Validated

Not UnValidated

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

Application-Centric Development

Original articulation from Doug Bagley @ Walmart

Data/Information

Network/Infrastructure

Systems/Applications

Goals/Objectives

Strategy

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• In support of strategy, organizations develop specific goals/objectives

• The goals/objectives drive the development of specific systems/applications

• Development of systems/applications leads to network/infrastructure requirements

• Data/information are typically considered after the systems/applications and network/infrastructure have been articulated

• 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

Systems/Applications

Network/Infrastructure

Data/Information

Goals/Objectives

Strategy

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• In support of strategy, the organization develops specific goals/objectives

• The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage

• Network/infrastructure components are developed supporting organizational data use

• Development of systems/applications is derived from the data/network architecture

• 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

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• 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

USS Midway & Pancakes

What is this?

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

Engineering Standards

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Architectural Work Product

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Components 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

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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 the governmental pay and personnel scenario

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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

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DSS

"Governors"

Taxpayers Clients

Vendors Program Deliver

Data model is comprised of model views

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DSS Strategic Data Model

Taxpayer view

Client view

Governance view

Program Delivery view

Vendor view

Taxpayer viewPayments Taxpayers

SocialServicePrograms

TaxpayerBenefits

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Client viewPayments

Clients ClientBenefits

LocalWellfareAgencies

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Governance viewPayments


GovernmentalResources

Governance Governments

State Boardof SocialServices

PolicyApproval

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Clients

ServiceDeliveryPartners


Program Delivery view

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Payments


Clients


GoodsandServices

Vendors

Vendor view

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GovernmentalResources

Governance Governments Payments Taxpayers

State Boardof SocialServices


Clients ClientBenefits

TaxpayerBenefits

PolicyApproval

ServiceDeliveryPartners


GoodsandServices

Vendors

DSS Strategic Level Data Model

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Challenge

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Package Implementation Example • "Green screen" legacy system to be replaced with Windows Icons

Mice Pointers (WIMP) interface; and • Major changes to operational processes

– 1 screen to 23 screens

• Management didn't think workforce could adjust to simultaneous changes – Question: "How big a change will it be to replace all instances of person_identifier

with social_security_number?"

• Answer: – (from "big" consultants) "Not a very big change." ($5 million budget)

Home Page

Business Process Name

Business Process Component

Business Process Component Step

PeopleSoft Process Metadata

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Home Page Name

(relates to one or more)

Business Process Name


Business Process Component Name


Business Process Component Step Name

Example Query Outputs

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Home Page Name Business Process Name Business Process Component Name Business Process Component Step Name

Peoplesoft Metadata Structureprocesses(39)

homepages(7)

menugroups(8)

components(180)

stepnames(822)

menunames(86)

panels(1421)

menuitems(1149)

menubars(31)

fields(7073)

records(2706)

parents(264)

reports(347)

children(647)

(41) (8)

(182)

(847)

(949)

(86)

(281)

(1259)(1916)

(5873)(264)

(647)(708)(647)

(25906)

(347)

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Peop

leso

ft M

etad

ata

Stru

ctur

e

Quantity

System Component

Time to make change

Labor Hours

1,400 Panels 15 minutes 350

1,500 Tables 15 minutes 375

984Business process component steps

15 minutes 246

Total 971

X $200/hour $194,200

X 5 upgrades $1,000,000

Business Value - Better Decisions

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A National Cancer Institute

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• This cancer center is a leader in shaping the fight against cancer

• Over 500 researchers and staff tend to over 12,000 patients annually

• This requires robust information management and analytical services

• The problem: It takes 1 month to run a report on an incident, i.e. a patient’s hospital visit that shows all touch points

Other Departments

SQLSQLSAS

Cancer Registry

Claims Database

File Export

Physician Invoices

Patient (Hospital)

Patient (Physician)

Patient (Registry)

Billing Data (Hospital)

Billing Data (Physician)

Diagnoses (Hospital)

Diagnoses (Physician)

Diagnoses (Registry)

Physicians (Hospital)

Physicians (Physician)

Access

SQL

SQL

SAS

SQL

Excel

Excel

Hospital Claims

Text Files FTP FTP

Text Files

FTP or Email

WordWordWord

Current State Assessment

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Other Departments

SSIS

Cancer Registry

Hospital Claims

Staging

SSIS

Physician Invoices

Patient Demographics

Billing Data (Hospital)

Billing Data (Physician)

Diagnoses (Hospital)

Diagnoses (Physician)

Diagnoses (Registry)

Physicians (Hospital)

Physicians (Physician)

SSIS SSIS Consolidated/ Sandbox

SSISSSAS

Patient (Consolidated)

RPT

Physicians (Consolidated)

Diagnoses (Consolidated)

SSRS

SharePoint

Excel

Email

One-off reports

Reusable reports

Conceptual Target Architecture

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0

25

50

75

100

Current Improved

Manipulation AnalysisReversing The Measures

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• Currently: – Analysts spend 80% of their time manipulating data and 20% of their time

analyzing data – Hidden productivity bottlenecks

• After rearchitecting: – Analysts spend less time manipulating data and more of their time analyzing data – Significant improvements in knowledge worker productivity

A 20% improvement results in a doubling of productivity!

Results: It is not always about money

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• Solution: – Integrate multiple databases into

one to create holistic view of data

– Automation of manual process

• Results: – Data is passed safely and

effectively – Eliminate inconsistencies,

redundancies, and corruption – Ability to cross-analyze – Significantly reduced turnaround

time for matching patients with potential donor -> increased potential to make life-saving connection in a manner that is faster, safer and more reliable

– Increased safe matches from 3 out of 10 to 6 out of 10


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Improving Data Quality during System Migration

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• Challenge – Millions of NSN/SKUs

maintained in a catalog – Key and other data stored in

clear text/comment fields – Original suggestion was manual

approach to text extraction – Left the data structuring problem unsolved

• Solution – Proprietary, improvable text extraction process – Converted non-tabular data into tabular data – Saved a minimum of $5 million

– Literally person centuries of work

Unmatched Items Ignorable Items Items Matched

Week # (% Total) (% Total) (% Total)

1 31.47% 1.34% N/A

2 21.22% 6.97% N/A

3 20.66% 7.49% N/A

4 32.48% 11.99% 55.53%

… … … …

14 9.02% 22.62% 68.36%

15 9.06% 22.62% 68.33%

16 9.53% 22.62% 67.85%

17 9.5% 22.62% 67.88%

18 7.46% 22.62% 69.92%


Architecture Derived: Diminishing Returns Determination

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Time needed to review all NSNs once over the life of the project:NSNs 2,000,000Average time to review & cleanse (in minutes) 5Total Time (in minutes) 10,000,000

Time available per resource over a one year period of time:Work weeks in a year 48Work days in a week 5Work hours in a day 7.5Work minutes in a day 450Total Work minutes/year 108,000

Person years required to cleanse each NSN once prior to migration:Minutes needed 10,000,000Minutes available person/year 108,000Total Person-Years 92.6

Resource Cost to cleanse NSN's prior to migration:Avg Salary for SME year (not including overhead) $60,000.00Projected Years Required to Cleanse/Total DLA Person Year Saved

93Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million

Copyright 2014 by Data Blueprint 95

Quantitative Benefits


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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!

Why Architect Data?

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Take Aways

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• 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

Upcoming Events


EDW 2015

Developing Data Strategy and Roadmap March 29, 2015 @ 5:00 PM ET

Addressing Data Challenges with the (DMM) Data Management Maturity March 30, 2015 @ 2:00 PM ET/11:00 AM PT

April Webinar: Data Governance Strategies April 14, 2015 @ 2:00 PM ET/11:00 AM PT

Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net Brought to you by:





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Data-Ed Webinar: Data Architecture Requirements

Technology

Transcript of Data-Ed Webinar: Data Architecture Requirements