Data-Ed Online: Data Architecture Requirements

1. 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. Copyright 2014 by Data Blueprint 1 Welcome: Data Architecture Requirements Date: May 13, 2014 Time: 2:00 PM ET Presented by: Peter Aiken, PhD

3. Copyright 2014 by Data Blueprint 3 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 Get Social With Us! Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed

4. Copyright 2014 by Data Blueprint Meet Your Presenter: Dr. Peter Aiken • Internationally recognized data management thought-leader – 30 years of experience – Recipient of multiple international awards – Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • (Past) Pres. DAMA International (dama.org) • 9 books and dozens of articles • Multi-year immersions with organizations as diverse as the US DoD, Deutsche Bank, Nokia, Wells Fargo, the Commonwealth of Virginia and Walmart 4

5. Presented by Peter Aiken, Ph.D. Data Architecture Requirements

6. • 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 Copyright 2013 by Data Blueprint Data Architecture Requirements 6

8. You can accomplish Advanced Data Practices without becoming proficient in the Basic Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present greater risk Copyright 2014 by Data Blueprint Data Management Practices Hierarchy Basic Data Management Practices Advanced Data Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA 8 Data Program Management Data Stewardship Data Development Data Support Operations Organizational Data Integration

9. Data Program Coordination Feedback Data Development Copyright 2014 by Data Blueprint Standard Data Organizational Strategies Goals Business Data Business Value Application Models & Designs Implementation Direction Guidance 9 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 Organizational DM Practices

10. Copyright 2014 by Data Blueprint 10 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 Five Integrated DM Practices

11. Copyright 2014 by Data Blueprint 11 Data Management Functions DAMA DM BoK & CDMP • 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 (more at dama.org) – Organized around several environmental elements • 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

16. Copyright 2014 by Data Blueprint 16 Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2 Data Modeling for Business Value • 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

17. Copyright 2014 by Data Blueprint 17 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

19. Copyright 2014 by Data Blueprint 19 Architecture 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.

20. Copyright 2014 by Data Blueprint 20 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

21. Copyright 2014 by Data Blueprint 21 Understanding • A specific definition – 'Understanding an architecture' – Documented and articulated as a digital blueprint illustrating the commonalities and interconnections among the architectural components – Ideally the understanding is shared by systems and humans

23. Copyright 2013 by Data Blueprint healthcare.gov 23 • 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_manage ment_and_too_many_cooks.html

24. Copyright 2014 by Data Blueprint 24 • 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 Architectures

25. Copyright 2014 by Data Blueprint Information 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 25

26. Copyright 2014 by Data Blueprint 26 Illustration by murdock23 @ http://designfestival.com/information-architecture-as-part-of-the-web-design-process/ What do you use an information architecture for?

27. Copyright 2014 by Data Blueprint Data Architecture – Better Definition 27 • 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]

31. Copyright 2014 by Data Blueprint How one inventory item proliferates data throughout the chain 31 555 Subassemblies & subcomponents 17,659 Repair parts or Consumables System 1: 18,214 Total items 75 Attributes/ item 1,366,050 Total attributes System 2 47 Total items 15+ Attributes/item 720 Total attributes System 3 16,594 Total items 73 Attributes/item 1,211,362 Total attributes System 4 8,535 Total items 16 Attributes/item 136,560 Total attributes System 5 15,959 Total items 22 Attributes/item 351,098 Total attributes Total for the five systems show above: 59,350 Items 179 Unique attributes 3,065,790 values

32. Copyright 2014 by Data Blueprint 32 • 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 Business Value: Agency units are carrying $1.5 billion worth of expired inventory

33. Copyright 2014 by Data Blueprint 33 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 Architectural Models?

42. database architecture engineering effort DataData DataData Data Data Data Focus of a software architecture engineering effort Program A Program B Program C Program F Program E Program D Program G Program H Program I Application domain 1 Application domain 2Application domain 3 Data Focus of a Data Data Copyright 2014 by Data Blueprint 42 Data Architecture Focus has Greater Potential Business Value • Broader focus than either software architecture or database architecture • Analysis scope is on the system wide use of data • Problems caused by data exchange or interface problems • Architectural goals more strategic than operational

43. Copyright 2013 by Data Blueprint Data Data Data Information Fact Meaning Request Strategic Information Use: Prerequisites [Built on definitions from Dan Appleton 1983] Intelligence Strategic Use 1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING. 5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES. 6. DATA/INFORMATION must formally arranged into an ARCHITECTURE. Wisdom & knowledge are often used synonymously Data Data Data Data 43

44. Copyright 2014 by Data Blueprint 44 A B C D A B C D A D C B How are data structures expressed as architectures? • Details are organized into larger components • Larger components are organized into models • Models are organized into architectures

45. Copyright 2014 by Data Blueprint 45 How are Data Models Expressed as Architectures? • 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? More Granular More Abstract

47. Copyright 2014 by Data Blueprint 47 How do data structures support organizational strategy? • Consider the opposite question? – Were your systems explicitly designed to be integrated or otherwise work together? – If not then what is the likelihood that they will work well together? – In all likelihood your organization is spending between 20-40% of its IT budget compensating for poor data structure integration – They cannot be helpful as long as their structure is unknown • Two answers – Achieving efficiency and effectiveness goals – Providing organizational dexterity for rapid implementation

48. Computers Human resources Communication facilities Software Management responsibilities Policies, directives, and rules Data Copyright 2014 by Data Blueprint 48 What Questions Can Architectures Address? • How and why do the 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?

49. ! ! ! ! Copyright 2014 by Data Blueprint 49 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

52. Copyright 2014 by Data Blueprint 52 Less ROT Technologies Process People 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

54. Copyright 2013 by Data Blueprint Application-Centric Development Original articulation from Doug Bagley @ Walmart 54 Data/ Information Network/ Infrastructure Systems/ Applications Goals/ Objectives Strategy • 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

55. Copyright 2014 by Data Blueprint Data-Centric Development Original articulation from Doug Bagley @ Walmart 55 Systems/ Applications Network/ Infrastructure Data/ Information Goals/ Objectives Strategy • 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

56. Copyright 2014 by Data Blueprint Why is Data Architecture Important? • Poorly understood – Data architecture asset value is not well understood • Inarticulately explained – Little opportunity to obtain learning and experience • Indirectly experienced – Cost organizations millions each year in productivity, redundant and siloed efforts – Example: Poorly thought out software purchases 56

57. Copyright 2014 by Data Blueprint 57 Architectural Work Product 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.

60. Copyright 2014 by Data Blueprint Level 1 Level 2 Level 3 Pay Employment Recruitment and Selection personnel Personnel Employee relations administration Employee compensation changes Salary planning Classification and pay Job evaluation Benefits administration Health insurance plans F lexible spending accounts Group life insurance Retirement plans Payroll Payroll administration Payroll processing Payroll interfaces Development N/A Training administration Career planning and skills inventory Work group activities Health and safety Accidents and workers compensation Health and safety programs A three-level decomposition of the model views from the governmental pay and personnel scenario 60

Data-Ed Online: Data Architecture Requirements

Data-Ed Online: Data Architecture Requirements

Recomendados

Más contenido relacionado

Was ist angesagt?

Was ist angesagt?(20)

Destacado

Destacado(13)

Similar a Data-Ed Online: Data Architecture Requirements

Similar a Data-Ed Online: Data Architecture Requirements(20)

Más de DATAVERSITY

Más de DATAVERSITY(20)

Último

Último(20)

Data-Ed Online: Data Architecture Requirements