data systems integration & business value pt. 2: cloud

Copyright 2013 by Data Blueprint

Data Systems Integration & Business Value Part 2: Cloud-based IntegrationAll organizations are prepared to benefit from aspects of the cloud. These benefits accrue when cloud-hosted datasets share three attributes. They must be of: 1. Higher quality data than those data residing outside of the cloud;2. Lower volume (1/5 the size of data collections) than similar

collections residing outside of the cloud; and3. Increased share-ability than data residing outside the cloud.Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties & decision-making. This webinar will show you how to recognize the opportunities, ‘size up’ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.

Date: August 13, 2013Time: 2:00 PM ET/11:00 AM PTPresenter: Peter Aiken, Ph.D.

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Executive Editor at DATAVERSITY.net

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Shannon Kempe


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Copyright 2013 by Data Blueprint 5

Peter Aiken, PhD• 25+ years of experience in data

management• Multiple international awards &

recognition• Founder, Data Blueprint (datablueprint.com)

• Associate Professor of IS, VCU (vcu.edu)

• President, DAMA International (dama.org)

• 8 books and dozens of articles• Experienced w/ 500+ data

management practices in 20 countries• Multi-year immersions with

organizations as diverse as the US DoD, Nokia, Deutsche Bank, Wells Fargo, and the Commonwealth of Virginia

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The Case for theChief Data O!cerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman

Data Systems Integration & Business Value Part 2: Cloud-based Integration

Presented by Peter Aiken, Ph.D.10124 W. Broad Street, Suite C

Glen Allen, Virginia 23060804.521.4056


Anticipated Business Value of Cloud-based Integration

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• Increased Automation and Storage Capacity– Virtually unlimited capacity & flexible storage– Easy to upgrade & Up-to-date software – Automated file synching & backups

• Affordability– Pay as you go– Usage is scaled to fit needs

• Agility, Scalability and Flexibility– Access from anywhere & collaborate– Data is always current

• Free up IT Hours & Staff– Cloud provider takes care of maintenance

• Ease of Use– Easy to use & automated


Prerequisites to Cloud-based Integration• Organizational investments in the cloud will be useless from

a data perspective unless:– Data governance, architecture, quality, development practices are

sufficiently mature– You must understand your data architecture and strategy in order to

evaluate various cloud options– Data must be reengineered to be

• Less• Better quality• More shareable

– for the cloud– Failure to do these will

result in more business value for the cloud vendors/service providers and less for your organization

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1. Data Management: Contextual Overview2. Necessary Data Management Functions

(Prerequisites)- Data Governance- Data Architecture- Data Development- Data Quality

3. Understanding Cloud-based Technologies

4. Cloud-based Benefits5. Cloud-based Integration

- Cleaner- Smaller- Shareable

6. Take Aways, References and Q&ATweeting now:

#dataed

Outline: Cloud-based Integration

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6. Take Aways, References and Q&A








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Data Program Coordination

Feedback

DataDevelopment


StandardData

Five Integrated DM Practice AreasOrganizational Strategies

Goals

BusinessData

Business Value

Application Models & Designs

Implementation

Direction

Guidance

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OrganizationalData Integration

DataStewardship

Data SupportOperations

Data Asset Use

IntegratedModels

Leverage data in organizational activities

Data management processes andinfrastructure

Combining multipleassets to produceextra value

Organizational-entity subject area data

integration

Provide reliable data access

Achieve sharing of data within a business area


Five Integrated DM Practice AreasManage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.

Data Program Coordination

Organizational Data Integration

Data Stewardship Data Development

Data Support Operations

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Hierarchy of Data Management Practices (after Maslow)

• 5 Data management practices areas / data management basics ...

• ... are necessary but insufficient prerequisites to organizational data leveraging applications that is self actualizing data or advanced data practices Basic Data Management Practices

– Data Program Management– Organizational Data Integration– Data Stewardship– Data Development– Data Support Operations

http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png

Advanced Data Practices• MDM• Mining• Big Data• Analytics• Warehousing• SOAClo

ud

• Data Management Body of Knowledge (DMBOK)– Published by DAMA International, the

professional association for Data Managers (40 chapters worldwide)

– Organized around primary data management functions focused around data delivery to the organization and several environmental elements

• Certified Data Management Professional (CDMP)– Series of 3 exams by DAMA International and

ICCP– Membership in a distinct group of

fellow professionals– Recognition for specialized knowledge in a

choice of 17 specialty areas– For more information, please visit:

• www.dama.org, www.iccp.org


DAMA DM BoK & CDMP

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Series Context• Certain systems are more data

focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single technological pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.

• Data Systems Integration & Business Value – Pt. 1: Metadata Practices– Pt. 2: Cloud-based Integration– Pt. 3: Warehousing, et al.

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Uses


Part 1: Metadata Take Aways• Metadata unlocks the value of data, and therefore requires

management attention [Gartner 2011]

• Metadata is the language of data governance• Metadata defines the essence of integration challenges

SourcesMetadata Governance

Metadata Engineering

Metadata Delivery

Metadata Practices

MetadataStorage

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Specialized Team Skills

Data Management functions necessary but insufficient for metadata-basedintegration


Data Management Body of

Knowledge

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








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Data Management Body of

Knowledge

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Data Management functionsnecessary but insufficientfor cloud-basedintegration



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Data Governance



Data Governance

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Organizational Data Governance Purpose Statement

• What does data governance mean to my organization?

– Getting some individuals (whose opinions matter)

– To form a body (needs a formal purpose/authority)

– Who will advocate/evangelize for (not dictate, enforce, rule)

– Increasing scope and rigor of

– Data-centric development practices

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Top Operations

Job


Data Governance is a Gateway for IT Projects

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Top Job

TopFinance

Job

Top Information Technology

Job

Top Marketing

Job

• Data assets are better foundational building block for IT projects• CDO coordinates IT investment priorities with Top IT Job• CDO determines when proposed IT projects are "ready"

Data Governance Organization

ChiefData

Officer


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Data Architecture Management



Data Architecture Management

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Architectural Answers

(Adapted from [Allen & Boynton 1991])

Computers

Human resources

Communication facilities

Software

Managementresponsibilities

Policies,directives,and rules

Data

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• Where do they go?• When are they needed?• What standards

should be adopted?• What vendors

should be chosen? • What rules should govern

the decisions? • What policies should guide

the process? • How and why do the components interact?• Why and how will the changes be implemented?• What should be managed organization-wide and what should

be managed locally?

Zachman Framework 3.0 - the Enterprise OntologyClassificationNames

ModelNames

*Horizontal integration lines are shown for example purposes only and are not a complete set. Composite, integrative rela-tionships connecting every cell horizontally potentially exist.

AudiencePerspectives

EnterpriseNames

ClassificationNames


C o m p o s i t e I n t e g r a t i o n s

Alignment

Transformations


Alignment

Transformations

C o m p o s i t e I n t e g r a t i o n s C o m p o s i t e I n t e g r a t i o n s

Alignment

Transformations

Alignment

Transformations

A l i g n m e n t

A l i g n m e n t

How Where Who WhenWhat Why

ProcessFlows

DistributionNetworks

ResponsibilityAssignments

TimingCycles

InventorySets

MotivationIntentions

OperationsInstances

(Implementations)

TheEnterprise

TheEnterprise

EnterprisePerspective

(Users)

ExecutivePerspective(Business Context

Planners)

Business MgmtPerspective(Business Concept

Owners)

ArchitectPerspective(Business LogicDesigners)

EngineerPerspective(Business Physics

Builders)

TechnicianPerspective

(Business ComponentImplementers)

ScopeContexts

(Scope Identification Lists)

BusinessConcepts

(Business Definition Models)

SystemLogic(System

Representation Models)

TechnologyPhysics(Technology

Specification Models)

ToolComponents(Tool Configuration

Models)

e.g. e.g. e.g. e.g. e.g. e.g.



e.g. e.g. e.g. e.g. e.g. e.g.e.g.: primitive e.g.: composite model:

model:

Forecast SalesPlan ProductionSell ProductsTake OrdersTrain EmployeesAssign TerritoriesDevelop MarketsMaintain FacilitiesRepair ProductsRecord Transctns

Material Supply NtwkProduct Dist. NtwkVoice Comm. NtwkData Comm. Ntwk Manu. Process NtwkOffice� Wrk� Flow� Ntwk

Parts Dist. NtwkPersonnel Dist. Ntwketc., etc.

General MgmtProduct MgmtEngineering DesignManu. EngineeringAccountingFinanceTransportationDistributionMarketingSales

Product CycleMarket CyclePlanning CycleOrder CycleEmployee CycleMaint. CycleProduction CycleSales CycleEconomic CycleAccounting Cycle

ProductsProduct TypesWarehouses

Parts BinsCustomersTerritoriesOrdersEmployeesVehiclesAccounts

New MarketsRevenue GrowthExpns ReductionCust ConvenienceCustomer Satis.Regulatory Comp.New CapitalSocial ContributionIncreased YieldIncreased Qualitye.g. e.g. e.g. e.g. e.g. e.g.

Operations TransformsOperations In/Outputs

Operations LocationsOperations Connections

Operations RolesOperations Work Products

Operations IntervalsOperations Moments

Operations EntitiesOperations Relationships

Operations EndsOperations Means

ProcessInstantiations

DistributionInstantiations

ResponsibilityInstantiations

TimingInstantiations

Inventory Instantiations

MotivationInstantiations

List: Timing Types

Business IntervalBusiness Moment

List: Responsibility Types

Business RoleBusiness Work Product

List: Distribution Types

Business LocationBusiness Connection

List: Process Types

Business TransformBusiness Input/Output

System TransformSystem Input /Output

System LocationSystem Connection

System RoleSystem Work Product

System IntervalSystem Moment

Technology TransformTechnology Input /Output

Technology LocationTechnology Connection

Technology RoleTechnology Work Product

Technology IntervalTechnology Moment

Tool TransformTool Input /Output

Tool LocationTool Connection

Tool RoleTool Work Product

Tool IntervalTool Moment

List: Inventory Types

Business EntityBusiness Relationship

System EntitySystem Relationship

Technology EntityTechnology Relationship

Tool EntityTool Relationship

List: Motivation Types

Business EndBusiness Means

System EndSystem Means

Technology EndTechnology Means

Tool EndTool Means

Timing IdentificationResponsibility IdentificationDistribution IdentificationProcess Identification

Timing DefinitionResponsibility DefinitionDistribution DefinitionProcess Definition

Process Representation Distribution Representation Responsibility Representation Timing Representation

Process Specification Distribution Specification Responsibility Specification Timing Specification

Inventory Identification

Inventory Definition

Inventory Representation

Inventory Specification

Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration

Motivation Identification

Motivation Definition

Motivation Representation

Motivation Specification

Motivation Configuration


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Copyright 2008-2011 John A. Zachman


What is an information architecture?• A structure of data-based information

assets supporting implementation of organizational strategy (or strategies)

• 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?

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ClassificationNames

ModelNames

*Horizontal integration lines are shown for example purposes only and are not a complete set. Composite, integrative rela-tionships connecting every cell horizontally potentially exist.


EnterpriseNames

ClassificationNames



Alignment

Transformations


Alignment

Transformations

C o m p o s i t e I n t e g r a t i o n s C o m p o s i t e I n t e g r a t i o n s

Alignment

Transformations

Alignment

Transformations

A l i g n m e n t

A l i g n m e n t

How Where Who WhenWhat Why

ProcessFlows

DistributionNetworks

ResponsibilityAssignments

TimingCycles

InventorySets

MotivationIntentions

OperationsInstances

(Implementations)

TheEnterprise

TheEnterprise

EnterprisePerspective

(Users)

ExecutivePerspective(Business Context

Planners)

Business MgmtPerspective(Business Concept

Owners)

ArchitectPerspective(Business LogicDesigners)

EngineerPerspective(Business Physics

Builders)

TechnicianPerspective

(Business ComponentImplementers)

ScopeContexts

(Scope Identification Lists)

BusinessConcepts

(Business Definition Models)

SystemLogic(System

Representation Models)

TechnologyPhysics(Technology

Specification Models)

ToolComponents(Tool Configuration

Models)




e.g. e.g. e.g. e.g. e.g. e.g.e.g.: primitive e.g.: composite model:

model:

Forecast SalesPlan ProductionSell ProductsTake OrdersTrain EmployeesAssign TerritoriesDevelop MarketsMaintain FacilitiesRepair ProductsRecord Transctns

Material Supply NtwkProduct Dist. NtwkVoice Comm. NtwkData Comm. Ntwk Manu. Process NtwkOffice� Wrk� Flow� Ntwk

Parts Dist. NtwkPersonnel Dist. Ntwketc., etc.

General MgmtProduct MgmtEngineering DesignManu. EngineeringAccountingFinanceTransportationDistributionMarketingSales

Product CycleMarket CyclePlanning CycleOrder CycleEmployee CycleMaint. CycleProduction CycleSales CycleEconomic CycleAccounting Cycle

ProductsProduct TypesWarehouses

Parts BinsCustomersTerritoriesOrdersEmployeesVehiclesAccounts

New MarketsRevenue GrowthExpns ReductionCust ConvenienceCustomer Satis.Regulatory Comp.New CapitalSocial ContributionIncreased YieldIncreased Qualitye.g. e.g. e.g. e.g. e.g. e.g.

Operations TransformsOperations In/Outputs

Operations LocationsOperations Connections

Operations RolesOperations Work Products

Operations IntervalsOperations Moments

Operations EntitiesOperations Relationships

Operations EndsOperations Means

ProcessInstantiations

DistributionInstantiations

ResponsibilityInstantiations

TimingInstantiations

Inventory Instantiations

MotivationInstantiations

List: Timing Types

Business IntervalBusiness Moment

List: Responsibility Types

Business RoleBusiness Work Product

List: Distribution Types

Business LocationBusiness Connection

List: Process Types

Business TransformBusiness Input/Output

System TransformSystem Input /Output

System LocationSystem Connection

System RoleSystem Work Product

System IntervalSystem Moment

Technology TransformTechnology Input /Output

Technology LocationTechnology Connection

Technology RoleTechnology Work Product

Technology IntervalTechnology Moment

Tool TransformTool Input /Output

Tool LocationTool Connection

Tool RoleTool Work Product

Tool IntervalTool Moment

List: Inventory Types

Business EntityBusiness Relationship

System EntitySystem Relationship

Technology EntityTechnology Relationship

Tool EntityTool Relationship

List: Motivation Types

Business EndBusiness Means

System EndSystem Means

Technology EndTechnology Means

Tool EndTool Means

Timing IdentificationResponsibility IdentificationDistribution IdentificationProcess Identification

Timing DefinitionResponsibility DefinitionDistribution DefinitionProcess Definition

Process Representation Distribution Representation Responsibility Representation Timing Representation

Process Specification Distribution Specification Responsibility Specification Timing Specification

Inventory Identification

Inventory Definition

Inventory Representation

Inventory Specification

Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration

Motivation Identification

Motivation Definition

Motivation Representation

Motivation Specification

Motivation Configuration

! ! ! !

Copyright 2013 by Data Blueprint 30

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


Data Architecture – Better Definition

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• All organizations have information architectures– Some are better understood and

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

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


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Data Development



Data Modeling/Data Development

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#dataed

Program F

Program E

Program DProgram G

Program H

Program I

Applicationdomain 2Application

domain 3

Data Development Focus


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#dataed

Data Development has greater Business Value


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Conceptual Logical Physical

Validated

Not Validated

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

Data Development Evolution Framework


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DataQualityManagement



Data Quality Engineering

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Definitions• Quality Data

– Fit for use meets the requirements of its authors, users, and administrators (adapted from Martin Eppler)

– Synonymous with information quality, since poor data quality results in inaccurate information and poor business performance

• Data Quality Management– Planning, implementation and control activities that apply quality

management techniques to measure, assess, improve, and ensure data quality

– Entails the "establishment and deployment of roles, responsibilities concerning the acquisition, maintenance, dissemination, and disposition of data" http://www2.sas.com/proceedings/sugi29/098-29.pdf

✓ Critical supporting process from change management✓ Continuous process for defining acceptable levels of data quality to meet business

needs and for ensuring that data quality meets these levels• Data Quality Engineering

– Recognition that data quality solutions cannot not managed but must be engineered– Engineering is the application of scientific, economic, social, and practical knowledge in

order to design, build, and maintain solutions to data quality challenges– Engineering concepts are generally not known and understood within IT or business!

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Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166


Quality Dimensions

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Startingpointfor newsystemdevelopment

data performance metadata

data architecture

dataarchitecture and

data models

shared data updated data

correcteddata

architecturerefinements

facts &meanings

Metadata &Data Storage

Starting pointfor existingsystems

Metadata Refinement• Correct Structural Defects• Update Implementation

Metadata Creation• Define Data Architecture• Define Data Model Structures

Metadata Structuring• Implement Data Model Views• Populate Data Model Views

Data Refinement• Correct Data Value Defects• Re-store Data Values

Data Manipulation• Manipulate Data• Updata Data

Data Utilization• Inspect Data• Present Data

Data Creation• Create Data• Verify Data Values

Data Assessment• Assess Data Values• Assess Metadata

Extended data life cycle model with metadata sources and uses

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DQE Context & Engineering Concepts • Can rules be implemented stating that no data can be

corrected unless the source of the error has been discovered and addressed?

• All data must be 100% perfect?

• Pareto – 80/20 rule– Not all data

is of equal Importance

• Scientific, economic, social, and practical knowledge

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http://visual.ly/amazing-journey-data-cloud

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Gartner Five-phase Hype Cycle

http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp48

Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.

Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.

Peak of Inflated Expectations: Early publicity produces a number of success stories—often accompanied by scores of failures. Some companies take action; many do not.

Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.

Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off.


Gartner Cloud Hype Cycle “While clearly maturing, cloud

computing continues to be the most hyped subject

in IT today.”

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• Cloud computing is location-independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand, as with the electricity grid. • Cloud computing is a natural evolution of the

widespread adoption of virtualization, service-oriented architecture and utility computing. • Details are abstracted from consumers, who no

longer have need for expertise in, or control over, the technology infrastructure "in the cloud" that supports them.

Cloud Computing


Five Essential Characteristics of Data Cloud Infrastructure

• Gartner defines "cloud computing" as the set of disciplines, technologies, and business models used to deliver IT capabilities (software, platforms, hardware) as an on-demand, scalable, elastic service.

• Five essential characteristics of cloud computing:

– It uses shared infrastructure

– It provides on-demand self-service

– It is elastic and scalable

– It is priced by consumption

– It is dynamic and virtualized

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Cloud Scalability


Cloud Rendering

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Cisco's Ladder to the Cloud

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Cloud Options

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Solving the Big Data Puzzle

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http://damfoundation.org/2012/06/whats-the-big-deal-about-big-data/








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Outline

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#dataed

Outline

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Benefits

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Benefits

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Anticipated Benefits

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0% 13% 25% 38% 50%

Improve data quality

Reduce installation and maintenance efforts

Reduce implementation efforts

Eliminate manual processes

Reduce time require to collect and prepare data

Apply data governance policies


Similar Opportunity

• IT Infrastructure. Your submission should include funding for the timely execution of agency plans to consolidate data centers developed in FY 2010 (reference FY 2011 passback guidance). In coordination with the data center consolidations, agencies should evaluate the potential to adopt cloud computing solutions by analyzing computing alternatives for IT investments in FY 2012. Agencies will be expected to adopt cloud computing solutions where they represent the best value at an acceptable level of risk.

• Adopt Light Technologies and Shared Solutions. We are reducing our data center footprint by 40 percent by 2015 and shifting the agency default approach to IT to a cloud-first policy as part of the 2012 budget process. Consolidating more than 2,000 government data centers will save money, increase security and improve performance.

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#dataed


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Data in the cloud should have three attributes that data outside the cloud should not have. It should be:

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Sharable-er

Cleaner

Smaller


Aspirational Data in the Cloud

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Effective Cloud Transformation

• Transformation into cloud computing cannot be done in a manner that benefits organizations unless data is re-architected – formally with two goals: – Maximizing effective, organization-wide data sharing; and – Minimizing organizational data ROT.

• Resulting data volume reduction should be 1/5 what is currently is – A significant economic motivator.

• All existing organizations have data collections that possess unique strengths and weaknesses– Strengths that should be leveraged– Weaknesses must be addressed

• Neither of these can be accomplished without formal data rearchitecting prior to cloud loading.

• There are very few who work in the area for a living but my team has achieved some remarkable successes.

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Transform

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Problems with forklifting 1. no basis for

decisions made2. no inclusion of

architecture/engineering concepts

3. no idea that these concepts are missing from the process

LessCleanerMore shareable ... data

Getting into the Cloud


Data Leverage

• 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

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Less ROT

Technologies

Process

People


The Cloud as a Data Quality Tool

Enterprise PortalData DeliveryData Analysis

Quality

Technology

Continuous ImprovementData BaseliningStatistical Data ControlCost of Quality ModelEmpowerment

Data ReductionPattern AnalysisMathematical AnalysisSchema Validation

ReusabilityLogic & Logic ProgrammingRelational DB TechnologyData Migration TechnologiesStatistical Programming Languages

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Fixing Data in the Cloud Using A Glovebox

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Conceptual Logical Physical

Validated

Not Validated

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

Data Development Evolution Framework


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Data Reengineering for More Shareable Data

As-is To-be

TechnologyIndependent/Logical

TechnologyDependent/Physical

abstraction

Other logical as-is data architecture components








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Part 2: Take Aways• Data governance, architecture,

quality, development maturity are necessary but insufficient prerequisites to successful data cloud implementation

• A variety of cloud options will influence cloud and data architectures in general– You must understand your architecture

and strategy in order to evaluate the options

• Data must be reengineered to be – Less– Better quality– More shareable – for the cloud

• Failure to do these will result in more business value for the cloud vendors/service providers and less for your organization


Questions?

It’s your turn! Use the chat feature or Twitter (#dataed) to submit

your questions to Peter now.

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+ =

Data Systems Integration & Business Value Pt. 3: WarehousingSeptember 10, 2013 @ 2:00 PM ET/11:00 AM PT

Show me the Money: Monetizing Data ManagementOctober 8, 2013 @ 2:00 PM ET/11:00 AM PT

Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net


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data systems integration & business value pt. 2: cloud

Technology

data residing

data engineering

data management practices

size of data collections

chief data officer

higher quality data

cloudbased integration

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