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Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
Data Systems Integration & Business Value Pt. 2: Cloud
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Data Systems Integration & Business Value Pt. 2: Cloud

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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 …

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 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.

Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:

Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
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 and 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.

You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

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  • 1. Copyright 2013 by Data Blueprint Data Systems Integration & Business Value Part 2: Cloud-based Integration All 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; and 3. 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, 2013 Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D. 1
  • 2. Copyright 2013 by Data Blueprint Executive Editor at DATAVERSITY.net 2 Shannon Kempe
  • 3. Copyright 2013 by Data Blueprint Commonly Asked Questions 1) Will I get copies of the slides after the event? 2) Is this being recorded so I can view it afterwards? 3
  • 4. Copyright 2013 by Data Blueprint Get Social With Us! Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed Like Us on Facebook www.facebook.com/datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals 4
  • 5. 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 2 The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman
  • 6. 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 23060 804.521.4056
  • 7. Copyright 2013 by Data Blueprint Anticipated Business Value of Cloud-based Integration 7 • 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
  • 8. Copyright 2013 by Data Blueprint 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 8
  • 9. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 9 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A
  • 10. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 10
  • 11. Data Program Coordination Feedback Data Development Copyright 2013 by Data Blueprint Standard Data Five Integrated DM Practice Areas Organizational Strategies Goals Business Data Business Value Application Models & Designs Implementation Direction Guidance 11 Organizational Data Integration Data Stewardship Data Support Operations Data Asset Use Integrated Models Leverage data in organizational activities Data management processes and infrastructure Combining multiple assets to produce extra value Organizational-entity subject area data integration Provide reliable data access Achieve sharing of data within a business area
  • 12. Copyright 2013 by Data Blueprint Five Integrated DM Practice Areas Manage 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 12
  • 13. Copyright 2013 by Data Blueprint 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 • SOA Cloud
  • 14. • 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 Copyright 2013 by Data Blueprint DAMA DM BoK & CDMP 14
  • 15. Copyright 2013 by Data Blueprint 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. 15
  • 16. Uses Copyright 2013 by Data Blueprint 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 Sources Metadata Governance Metadata Engineering Metadata Delivery Metadata Practices Metadata Storage 16 Specialized Team Skills
  • 17. Data Management functions necessary but insufficient for metadata- based integration Copyright 2013 by Data Blueprint Data Management Body of Knowledge 17 From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 18. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 18
  • 19. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 19
  • 20. Copyright 2013 by Data Blueprint Data Management Body of Knowledge 20 Data Management functions necessary but insufficient for cloud- based integration From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 21. Copyright 2013 by Data Blueprint 21 Data Governance From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 22. Copyright 2013 by Data Blueprint Data Governance 22 From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 23. Copyright 2013 by Data Blueprint 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 23
  • 24. Top Operations Job Copyright 2013 by Data Blueprint Data Governance is a Gateway for IT Projects 24 Top Job Top Finance 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 Chief Data Officer
  • 25. Copyright 2013 by Data Blueprint 25 Data Architecture Management From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 26. Copyright 2013 by Data Blueprint Data Architecture Management 26 From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 27. Copyright 2013 by Data Blueprint Architectural Answers (Adapted from [Allen & Boynton 1991]) Computers Human resources Communication facilities Software Management responsibilities Policies, directives, and rules Data 27 • 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?
  • 28. Zachman Framework 3.0 - the Enterprise Ontology Classification Names Model Names *Horizontal integration lines areshownforexamplepurposes only and are not a complete set. Composite, integrative rela- tionships connecting every cell horizontally potentially exist. Audience Perspectives Enterprise Names Classification Names Audience Perspectives C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t T r a n s f o r m 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 A l i g n m e n t T r a n s f o r m 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 C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t T r a n s f o r m a t i o n s A l i g n m e n t T r a n s f o r m a t i o n s A l i g n m e n t A l i g n m e n t How Where Who WhenWhat Why Process Flows Distribution Networks Responsibility Assignments Timing Cycles Inventory Sets Motivation Intentions Operations Instances (Implementations) The Enterprise The Enterprise Enterprise Perspective (Users) Executive Perspective (Business Context Planners) Business Mgmt Perspective (Business Concept Owners) Architect Perspective (Business Logic Designers) Engineer Perspective (Business Physics Builders) Technician Perspective (Business Component Implementers) Scope Contexts (Scope Identification Lists) Business Concepts (Business Definition Models) System Logic (System Representation Models) Technology Physics (Technology Specification Models) Tool Components (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. 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 Sales Plan Production Sell Products Take Orders Train Employees Assign Territories Develop Markets Maintain Facilities Repair Products Record Transctns Material Supply Ntwk Product Dist. Ntwk Voice Comm. Ntwk Data Comm. Ntwk Manu. Process Ntwk Office
  • 29.  

Wrk
  • 30.  

Flow
  • 31.  

Ntwk Parts Dist. Ntwk Personnel Dist. Ntwk etc., etc. General Mgmt Product Mgmt Engineering Design Manu. Engineering Accounting Finance Transportation Distribution Marketing Sales Product Cycle Market Cycle Planning Cycle Order Cycle Employee Cycle Maint. Cycle Production Cycle Sales Cycle Economic Cycle Accounting Cycle Products Product Types Warehouses Parts Bins Customers Territories Orders Employees Vehicles Accounts New Markets Revenue Growth Expns Reduction Cust Convenience Customer Satis. Regulatory Comp. New Capital Social Contribution Increased Yield Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g. Operations Transforms Operations In/Outputs Operations Locations Operations Connections Operations Roles Operations Work Products Operations Intervals Operations Moments Operations Entities Operations Relationships Operations Ends Operations Means Process Instantiations Distribution Instantiations Responsibility Instantiations Timing Instantiations Inventory Instantiations Motivation Instantiations List: Timing Types Business Interval Business Moment List: Responsibility Types Business Role Business Work Product List: Distribution Types Business Location Business Connection List: Process Types Business Transform Business Input/Output System Transform System Input /Output System Location System Connection System Role System Work Product System Interval System Moment Technology Transform Technology Input /Output Technology Location Technology Connection Technology Role Technology Work Product Technology Interval Technology Moment Tool Transform Tool Input /Output Tool Location Tool Connection Tool Role Tool Work Product Tool Interval Tool Moment List: Inventory Types Business Entity Business Relationship System Entity System Relationship Technology Entity Technology Relationship Tool Entity Tool Relationship List: Motivation Types Business End Business Means System End System Means Technology End Technology Means Tool End Tool 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 28 Copyright 2008-2011 John A. Zachman
  • 32. Copyright 2013 by Data Blueprint 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? 29 Classification Names Model Names *Horizontal integration lines areshownforexamplepurposes only and are not a complete set. Composite, integrative rela- tionships connecting every cell horizontally potentially exist. Audience Perspectives Enterprise Names Classification Names Audience Perspectives C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t T r a n s f o r m 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 A l i g n m e n t T r a n s f o r m 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 C o m p o s i t e I n t e g r a t i o n s A l i g n m e n t T r a n s f o r m a t i o n s A l i g n m e n t T r a n s f o r m a t i o n s A l i g n m e n t A l i g n m e n t How Where Who WhenWhat Why Process Flows Distribution Networks Responsibility Assignments Timing Cycles Inventory Sets Motivation Intentions Operations Instances (Implementations) The Enterprise The Enterprise Enterprise Perspective (Users) Executive Perspective (Business Context Planners) Business Mgmt Perspective (Business Concept Owners) Architect Perspective (Business Logic Designers) Engineer Perspective (Business Physics Builders) Technician Perspective (Business Component Implementers) Scope Contexts (Scope Identification Lists) Business Concepts (Business Definition Models) System Logic (System Representation Models) Technology Physics (Technology Specification Models) Tool Components (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. 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 Sales Plan Production Sell Products Take Orders Train Employees Assign Territories Develop Markets Maintain Facilities Repair Products Record Transctns Material Supply Ntwk Product Dist. Ntwk Voice Comm. Ntwk Data Comm. Ntwk Manu. Process Ntwk Office
  • 33.  

Wrk
  • 34.  

Flow
  • 35.  

Ntwk Parts Dist. Ntwk Personnel Dist. Ntwk etc., etc. General Mgmt Product Mgmt Engineering Design Manu. Engineering Accounting Finance Transportation Distribution Marketing Sales Product Cycle Market Cycle Planning Cycle Order Cycle Employee Cycle Maint. Cycle Production Cycle Sales Cycle Economic Cycle Accounting Cycle Products Product Types Warehouses Parts Bins Customers Territories Orders Employees Vehicles Accounts New Markets Revenue Growth Expns Reduction Cust Convenience Customer Satis. Regulatory Comp. New Capital Social Contribution Increased Yield Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g. Operations Transforms Operations In/Outputs Operations Locations Operations Connections Operations Roles Operations Work Products Operations Intervals Operations Moments Operations Entities Operations Relationships Operations Ends Operations Means Process Instantiations Distribution Instantiations Responsibility Instantiations Timing Instantiations Inventory Instantiations Motivation Instantiations List: Timing Types Business Interval Business Moment List: Responsibility Types Business Role Business Work Product List: Distribution Types Business Location Business Connection List: Process Types Business Transform Business Input/Output System Transform System Input /Output System Location System Connection System Role System Work Product System Interval System Moment Technology Transform Technology Input /Output Technology Location Technology Connection Technology Role Technology Work Product Technology Interval Technology Moment Tool Transform Tool Input /Output Tool Location Tool Connection Tool Role Tool Work Product Tool Interval Tool Moment List: Inventory Types Business Entity Business Relationship System Entity System Relationship Technology Entity Technology Relationship Tool Entity Tool Relationship List: Motivation Types Business End Business Means System End System Means Technology End Technology Means Tool End Tool 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
  • 36. ! ! ! ! Copyright 2013 by Data Blueprint 30 Organizational Needs become instantiated and integrated into an Data/Information Architecture Informa(on)System) Requirements authorizes and articulates satisfyspecificorganizationalneeds Data Architectures produce and are made up of information models that are developed in response to organizational needs
  • 37. Copyright 2013 by Data Blueprint Data Architecture – Better Definition 31 • 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]
  • 38. Copyright 2013 by Data Blueprint 32 Data Development From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 39. Copyright 2013 by Data Blueprint Data Modeling/Data Development 33 From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 40. Copyright 2013 by Data Blueprint 34 #dataed Program F Program E Program D Program G Program H Program I Application domain 2Application domain 3 Data Development Focus
  • 41. Copyright 2013 by Data Blueprint 35 #dataed Data Development has greater Business Value
  • 42. Copyright 2013 by Data Blueprint 36 Conceptual Logical Physical Validated Not Validated Every change can be mapped to a transformation in this framework! Data Development Evolution Framework
  • 43. Copyright 2013 by Data Blueprint 37 Data Quality Management From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 44. Copyright 2013 by Data Blueprint Data Quality Engineering 38 From The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  • 45. Copyright 2013 by Data Blueprint 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! 39 Spinach/Popeye story from http://it.toolbox.com/blogs/infosphere/spinach-how-a-data-quality-mistake-created-a-myth-and-a-cartoon-character-10166
  • 46. Copyright 2013 by Data Blueprint Quality Dimensions 40
  • 47. Copyright 2013 by Data Blueprint Starting point for new system development data performance metadata data architecture data architecture and data models shared data updated data corrected data architecture refinements facts & meanings Metadata & Data Storage Starting point for existing systems 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 41
  • 48. Copyright 2013 by Data Blueprint 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 42
  • 49. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 43
  • 50. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 44
  • 51. Copyright 2013 by Data Blueprint http://visual.ly/amazing-journey-data-cloud 45
  • 52. Copyright 2013 by Data Blueprint http://visual.ly/amazing-journey-data-cloud 46
  • 53. Copyright 2013 by Data Blueprint http://visual.ly/amazing-journey-data-cloud 47
  • 54. Copyright 2013 by Data Blueprint Gartner Five-phase Hype Cycle http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp 48 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.
  • 55. Copyright 2013 by Data Blueprint Gartner Cloud Hype Cycle “While clearly maturing, cloud computing continues to be the most hyped subject in IT today.” 49
  • 56. Copyright 2013 by Data Blueprint 50 • 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
  • 57. Copyright 2013 by Data Blueprint 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 51
  • 58. Copyright 2013 by Data Blueprint 52 Cloud Scalability
  • 59. Copyright 2013 by Data Blueprint Cloud Rendering 53
  • 60. Copyright 2013 by Data Blueprint Cisco's Ladder to the Cloud 54
  • 61. Copyright 2013 by Data Blueprint Cloud Options 55
  • 62. Copyright 2013 by Data Blueprint Solving the Big Data Puzzle 56 http://damfoundation.org/2012/06/whats-the-big-deal-about-big-data/
  • 63. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline 57
  • 64. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline 58
  • 65. Copyright 2013 by Data Blueprint Benefits 59
  • 66. Copyright 2013 by Data Blueprint Benefits 60
  • 67. Copyright 2013 by Data Blueprint Anticipated Benefits 61 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
  • 68. Copyright 2013 by Data Blueprint 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. 62
  • 69. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 63
  • 70. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 64
  • 71. Copyright 2013 by Data Blueprint Data in the cloud should have three attributes that data outside the cloud should not have. It should be: 65 Sharable-er Cleaner Smaller
  • 72. Copyright 2013 by Data Blueprint Aspirational Data in the Cloud 66
  • 73. Copyright 2013 by Data Blueprint 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. 67
  • 74. Copyright 2013 by Data Blueprint Transform 68 Problems with forklifting 1. no basis for decisions made 2. no inclusion of architecture/ engineering concepts 3. no idea that these concepts are missing from the process Less Cleaner More shareable ... data Getting into the Cloud
  • 75. Copyright 2013 by Data Blueprint 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 69 Less ROT Technologies Process People
  • 76. Copyright 2013 by Data Blueprint The Cloud as a Data Quality Tool Enterprise Portal Data DeliveryData Analysis Quality Technology Continuous Improvement Data Baselining Statistical Data Control Cost of Quality Model Empowerment Data Reduction Pattern Analysis Mathematical Analysis Schema Validation Reusability Logic & Logic Programming Relational DB Technology Data Migration Technologies Statistical Programming Languages 70
  • 77. Copyright 2013 by Data Blueprint Fixing Data in the Cloud Using A Glovebox 71
  • 78. Copyright 2013 by Data Blueprint 72 Conceptual Logical Physical Validated Not Validated Every change can be mapped to a transformation in this framework! Data Development Evolution Framework
  • 79. Copyright 2013 by Data Blueprint 73 Data Reengineering for More Shareable Data As-is To-be Technology Independent/ Logical Technology Dependent/ Physical abstraction Other logical as-is data architecture components
  • 80. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 74
  • 81. Copyright 2013 by Data Blueprint 1. Data Management: Contextual Overview 2. Necessary Data Management Functions (Prerequisites) - Data Governance - Data Architecture - Data Development - Data Quality 3. Understanding Cloud-based Technologies 4. Cloud-based Benefits 5. Cloud-based Integration - Cleaner - Smaller - Shareable 6. Take Aways, References and Q&A Tweeting now: #dataed Outline: Cloud-based Integration 75
  • 82. Copyright 2013 by Data Blueprint 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
  • 83. Copyright 2013 by Data Blueprint Questions? It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. 77 + =
  • 84. Data Systems Integration & Business Value Pt. 3: Warehousing September 10, 2013 @ 2:00 PM ET/11:00 AM PT Show me the Money: Monetizing Data Management October 8, 2013 @ 2:00 PM ET/11:00 AM PT Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net Copyright 2013 by Data Blueprint Upcoming Events 78

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