Data-Ed: Data Architecture Requirements

Data-Ed: Data Architecture Requirements
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken, will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business. Welcome: Data Architecture Requirements 1 Copyright 2015 by Data Blueprint Program F Program E Program D Program G Program H Program I Application domain 2Application domain 3 Date: March 9, 2015 Time: 2:00 PM ET Presented by: Peter Aiken, PhD
Shannon Kempe Executive Editor at DATAVERSITY.net 2 Copyright 2015 by Data Blueprint
Two Most Commonly Asked Questions 3 Copyright 2015 by Data Blueprint 1. Will I get copies of the slides after the event? 2. Is this being recorded so I can view it afterwards?
Get Social With Us! 4Copyright 2015 by Data Blueprint 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 Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed
Peter Aiken, Ph.D. 5 Copyright 2015 by Data Blueprint • 30+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 9 books and dozens of articles • Experienced w/ 500+ data management practices • Multi-year immersions:  - US DoD  - Nokia  - Deutsche Bank  - Wells Fargo  - Walmart • DAMA International President 2009-2013 • DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman
We believe ... Data   Assets Financial   Assets Real  Estate Assets Inventory Assets Non- depletable Available for subsequent use Can be   used up Can be   used up Non- degrading √ √ Can degrade  over time Can degrade  over time Durable Non-taxed √ √ Strategic Asset √ √ √ √ 6 Copyright 2015 by Data Blueprint • Today, data is the most powerful, yet underutilized and poorly managed organizational asset • Data is your – Sole – Non-depleteable – Non-degrading – Durable – Strategic • Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon! • Our mission is to unlock business value by – Strengthening your data management capabilities – Providing tailored solutions, and – Building lasting partnerships Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]
Presented by Peter Aiken, Ph.D. Data Architecture Requirements
Data Architecture Requirements 8 Copyright 2015 by Data Blueprint • 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
Data Architecture Requirements 9 Copy right 2015by Data Blueprint • 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
Maslow's Hierarchiy of Needs 10 Copyright 2015 by Data Blueprint
You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present   greater  risk (with thanks to Tom DeMarco) Data Management Practices Hierarchy Advanced   Data   Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices 11 Copyright 2015 by Data Blueprint Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities
Maintain fit-for-purpose data, efficiently and effectively 12 Copyright 2015 by Data Blueprint Manage data coherently Manage data assets professionally Data architecture implementation Data lifecycle implementation Organizational support DMM℠ Structure of   5 Integrated   DM Practice Areas
The DAMA Guide to the Data Management Body of Knowledge 13Copyright 2015 by Data Blueprint Data Management Functions Published by DAMA International • The professional association for Data Managers (40 chapters worldwide) DMBoK organized around • Primary data management functions focused around data delivery to the organization • Organized around several environmental elements
Data Architecture Management 14 Copyright 2015 by Data Blueprint from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
What is the CDMP? 15Copyright 2015 by Data Blueprint • 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
18 Copyright 2015 by Data Blueprint Architecture is both the process and product of planning, designing and constructing space that reflects functional, social, and aesthetic considerations. A wider definition may comprise all design activity from the macro-level (urban design, landscape architecture) to the micro-level (construction details and furniture). In fact, architecture today may refer to the activity of designing any kind of system and is often used in the IT world. Architecture
Architectures: here, whether you like it or not 19Copyright 2015 by Data Blueprint deviantart.com • All organizations have architectures – Some are better understood and documented (and therefore more useful to the organization) than others
Architecture Representation 20Copyright 2015 by Data Blueprint • Architectures are the symbolic   representation of the structure,   use and reuse of resources • Common components are   represented using standardized notation • Are sufficiently detailed to permit both business analysts and technical personnel to separately read the same model, and come away with a common understanding and yet they are developed effectively
Understanding 21 Copyright 2015 by Data Blueprint • A specific definition – 'Understanding an architecture' – Documented and articulated as a (digital) blueprint illustrating the   commonalities and   interconnections   among the   architectural   components – Ideally the understanding   is shared by systems and humans
• Process Architecture – Arrangement of inputs -> transformations = value -> outputs – Typical elements: Functions, activities, workflow, events, cycles, products, procedures • Systems Architecture – Applications, software components, interfaces, projects • Business Architecture – Goals, strategies, roles, organizational structure, location(s) • Security Architecture – Arrangement of security controls relation to IT Architecture • Technical Architecture/Tarchitecture – Relation of software capabilities/technology stack – Structure of the technology infrastructure of an enterprise, solution or system – Typical elements: Networks, hardware, software platforms, standards/protocols • Data/Information Architecture – Arrangement of data assets supporting organizational strategy – Typical elements: specifications expressed as entities, relationships, attributes, definitions, values, vocabularies Typically Managed Organizational Architectures 22Copyright 2015 by Data Blueprint
• 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 23 Copyright 2015 by Data Blueprint Information Architecture
Data Architecture – A Useful Definition 26Copyright 2015 by Data Blueprint • Common vocabulary expressing integrated requirements ensuring that data assets are stored, arranged, managed, and used in systems in support of organizational strategy [Aiken 2010]
Vocabulary is Important-Tank, Tanks, Tankers, Tanked 27 Copyright 2015 by Data Blueprint
How one inventory item proliferates data throughout an organization's data architecture 28 Copyright 2015 by Data Blueprint 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
Business Value: Agency units are carrying $1.5 billion worth of expired inventory 29 Copyright 2015 by Data Blueprint • Generates unnecessary costs & negative impacts on operations, including: – Resources are focused on non-value added tasks of maintaining obsolete inventory, which creates distractions to the agency’s main mission • Storage – Physical/real estate needed to house items • Handling – Includes transportation and human resources   dedicated to moving, maintaining, counting   and securing outdated inventory • Opportunity – Inventory could be returned to manufacturer or   sold to free up financial assets for more needed   and critical supplies • Systemic – Cost of inventorying information and maintaing   paper or electronic records which should be used to   support mission-critical acquisitions and distribution • Maintenance – Repairing of expired items
Data Architecture – A More Useful Definition 30Copyright 2015 by Data Blueprint • A structure of data-based information assets supporting implementation of organizational strategy (or strategies) [Aiken 2010] • Most organizations have data assets that are not supportive of strategies - i.e., information architectures that are not helpful • The really important question is: how can organizations more effectively use their information architectures to support strategy implementation?
What do you use an information architecture for? 31 Copyright 2015 by Data Blueprint Illustration by murdock23 @ http://designfestival.com/information-architecture-as-part-of-the-web-design-process/
Database Architecture Focus 32Copyright 2015 by Data Blueprint Program F Program E Program D Program G Program H Program I Application domain 2Application domain 3
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 Data Architecture Focus has Greater Potential Business Value 33 Copyright 2015 by Data Blueprint • Broader focus than either software architecture or database architecture • Analysis scope is on the system wide use of data • Problems caused by data exchange or interface problems • Architectural goals more strategic than operational
Why is Data Architecture Important? 34 Copyright 2015 by Data Blueprint • 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
35 Copyright 2015 by Data Blueprint
healthcare.gov 36 Copyright 2015 by Data Blueprint • 55 Contractors! • "Anyone who has written a line of code or built a system from the ground-up cannot be surprised or even mildly concerned that Healthcare.gov did not work out of the gate,"     Standish Group International Chairman Jim Johnson said in a recent podcast.   • "The real news would have been if it actually did work. The very fact that most of it did work at all is a success in itself." • Software programmed to access data using traditional data management technologies • Data components incorporated "big data technologies"  http://www.slate.com/articles/technology/bitwise/2013/10/ problems_with_healthcare_gov_cronyism_bad_management _and_too_many_cooks.html
Moon Lighting Practical Application of Data Architecting Person Job Class Employee Position BR1) Zero, one, or more EMPLOYEES can be associated with one PERSON BR2) Zero, one, or more EMPLOYEES can be associated with one JOB CLASS; BR3) Zero, one, or more EMPLOYEES can be associated with one POSITION BR4) One or more POSITIONS can be associated with one JOB CLASS. 37 Copyright 2015 by Data Blueprint Job Sharing
Running Query 38 Copyright 2015 by Data Blueprint
Optimized Query 39 Copyright 2015 by Data Blueprint
Repeat 100s, thousands, millions of times ... 40 Copyright 2015 by Data Blueprint
Death by 1000 Cuts 41 Copyright 2015 by Data Blueprint
• How does poor data architecture cost money? • Consider the opposite question: – Were your systems explicitly designed to   be integrated or otherwise work together? – If not then what is the likelihood that they   will work well together? – They cannot be helpful as long as their structure is unknown • Organizations spend between 20 - 40%   of their IT budget evolving their data - including: – Data migration • Changing the location from one place to another – Data conversion • Changing data into another form, state, or product – Data improving • Inspecting and manipulating, or re-keying data to prepare it for   subsequent use - Source: John Zachman Lack of coherent data architecture is a hidden expense 42 Copyright 2015 by Data Blueprint PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
Data Architecting for Business Value 43 Copyright 2015 by Data Blueprint Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2 • Goal must be shared IT/business understanding – No disagreements = insufficient communication • Data sharing/exchange is largely and highly automated and   thus dependent on successful engineering – It is critical to engineer a sound foundation of data modeling basics   (the essence) on which to build advantageous data technologies • Modeling characteristics change over the course of analysis – Different model instances may be useful to different analytical problems • Incorporate motivation (purpose statements) in all modeling – Modeling is a problem defining as well as a problem solving activity - both are inherent to architecture • Use of modeling is much more important than selection of a specific modeling method • Models are often living documents – The more easily it adapts to change, the resource utilization • Models must have modern access/interface/search technologies – Models need to be available in an easily searchable manner • Utility is paramount – Adding color and diagramming objects customizes models and allows for a more engaging and enjoyable user review process
Architecture Example 44Copyright 2015 by Data Blueprint
Poor Quality Foundation 45 Copyright 2015 by Data Blueprint
What they think they are purchasing! 46 Copyright 2015 by Data Blueprint
Levels of Abstraction, Completeness and Utility 47Copyright 2015 by Data Blueprint • Models more downward facing - detail • Architecture is higher level of abstraction - integration • In the past architecture attempted to gain complete (perfect) understanding – Not timely – Not feasible • Focus instead on   architectural components – Governed by a framework – More immediate utility • http://www.architecturalcomponentsinc.com
Too Much Detail 48Copyright 2015 by Data Blueprint
Web Developers Understand IA 49Copyright 2015 by Data Blueprint http://www.jeffkerndesign.com
Web Developers Understand IA 50Copyright 2015 by Data Blueprint http://www.jeffkerndesign.com
How are data structures expressed as architectures? 51 Copyright 2015 by Data Blueprint A B C D A B C D A D C B • Details are organized into   larger components • Larger components are organized into models • Models are organized into architectures
How are Data Models Expressed as Architectures? 52 Copyright 2015 by Data Blueprint More Granular                                                More Abstract  • Attributes are organized into entities/objects – Attributes are characteristics of "things" – Entitles/objects are "things" whose information is managed in support of strategy – Examples • Entities/objects are organized into models – Combinations of attributes and entities are structured to represent information requirements – Poorly structured data, constrains organizational information delivery capabilities – Examples • Models are organized into architectures – When building new systems, architectures are used to plan development – More often, data managers do not know what existing architectures are and - therefore - cannot make use of them in support of strategy implementation – Why no examples?
Data Data Data Information Fact Meaning Request Data must be Architected to Deliver Value [Built on definitions from Dan Appleton 1983] Intelligence Strategic Use 53 Copyright 2015 by Data Blueprint 1. Each FACT combines with one or more MEANINGS. 2. Each specific FACT and MEANING combination is referred to as a DATUM. 3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST 4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING. 5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES. 6. DATA/INFORMATION must formally arranged into an ARCHITECTURE. Wisdom & knowledge are   often used synonymously Data Data Data Data
How do data structures support organizational strategy? 54 Copyright 2015 by Data Blueprint • Two answers – Achieving efficiency and effectiveness goals – Providing organizational dexterity for rapid implementation
Computers Human resources Communication facilities Software Management responsibilities Policies, directives, and rules Data What Questions Can Data Architectures Address? 55Copyright 2015 by Data Blueprint • How and why do the data components interact? • Where do they go? • When are they needed? • Why and how will the   changes be implemented? • What should be managed organization- wide and what should be managed locally? • What standards should be adopted? • What vendors should be chosen? • What rules should govern the decisions? • What policies should guide the process?
! ! ! ! Data Architectures produce and are made up of information models that are developed in response to organizational needs 56 Copyright 2015 by Data Blueprint Organizational Needs become instantiated   and integrated into an Data/Information  Architecture Informa(on)System) Requirements authorizes and   articulates satisfyspecificorganizationalneeds
Data Leverage 59 Copyright 2015 by Data Blueprint Less ROT Technologies Process People • Permits organizations to better manage their sole non-depleteable, non- degrading, durable, strategic asset - data – within the organization, and – with organizational data exchange partners • Leverage – Obtained by implementation of data-centric technologies, processes, and human skill sets – Increased by elimination of data ROT (redundant, obsolete, or trivial) • The bigger the organization, the greater potential leverage exists • Treating data more asset-like simultaneously 1. lowers organizational IT costs and 2. increases organizational knowledge worker productivity
Architecture Evolution 60 Copyright 2015 by Data Blueprint Conceptual Logical Physical Validated Not UnValidated Every change can be mapped to a transformation in this framework!
Application-Centric Development Original articulation from Doug Bagley @ Walmart Data/ Information Network/ Infrastructure Systems/ Applications Goals/ Objectives Strategy 61 Copyright 2015 by Data Blueprint • In support of strategy, organizations develop specific goals/objectives • The goals/objectives drive the development of specific systems/applications • Development of systems/applications leads to network/infrastructure requirements • Data/information are typically considered after the systems/applications and network/ infrastructure have been articulated • Problems with this approach: – Ensures data is formed to the applications and not around the organizational-wide information   requirements – Process are narrowly formed around applications – Very little data reuse is possible
Data-Centric Development Original articulation from Doug Bagley @ Walmart Systems/ Applications Network/ Infrastructure Data/ Information Goals/ Objectives Strategy 62 Copyright 2015 by Data Blueprint • In support of strategy, the organization develops specific goals/objectives • The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage • Network/infrastructure components are developed supporting organizational data use • Development of systems/applications is derived from the data/network architecture • Advantages of this approach: – Data/information assets are developed from an organization-wide perspective – Systems support organizational data needs and compliment organizational process flows – Maximum data/information reuse
Engineering Architecture Engineering/Architecting Relationship 63 Copyright 2015 by Data Blueprint • Architecting is used to create and build systems too complex to be treated by engineering analysis alone • Architects require technical details as the exception • Engineers develop the technical designs • Craftsman deliver components supervised by: – Building Contractor – Manufacturer
USS Midway & Pancakes What is this? 64 Copyright 2015 by Data Blueprint • It is tall • It has a clutch • It was built in 1942 • It is still in regular use!
Engineering Standards 65 Copyright 2015 by Data Blueprint
Architectural Work Product 66 Copyright 2015 by Data Blueprint 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.
System Process Process 2 Process 1 Process 3 Subprocess 1.1 Subprocess 1.2 Subprocess 1.3 Hierarchical System Functional Decomposition 67 Copyright 2015 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 68 Copyright 2015 by Data Blueprint
H ealth car e system 1 Patient administration 1.1 R egistration 1.2 Admission 1.3 Disposition 1.4 Transfer 1.5 M edical record 1.6 Administration 1.7 Patient billing 1.8 Patient affairs 1.9 Patient management 2 Patient appointments and scheduling 2.1 Create or maintain schedules 2.2 Appoint patients 2.3 R ecord patient encounter 2.4 I dentify patient 2.5 I dentify health care provider 3 Nursing 3.1 Patient care 3.2 Unit management 4 Laboratory 4.1 R esults reporting 4.2 Specimen processing 4.3 R esult entry processing 4.4 Laboratory management 4.5 Workload support 5 Pharmacy 5.1 Unit dose dispensing 5.2 Controlled Drug I nventory 5.3 Outpatient 6 R adiology 6.1 Scheduling 6.2 E xam processing 6.3 E xam reporting 6.4 Special interest and teaching 6.5 R adiology workload reporting 7 Clinical dietetics 7.1 E stablish parameters 7.2 R eceive diet orders 8 Order entry and results 8.1 R eporting 8.2 E nter and maintain orders 8.3 Obtain results 8.4 R eview patient information 8.5 Clinical desktop 9 System management 9.1 Logon and security management 9.2 Archive run M anagement 9.3 Communication software 9.4 M anagement 9.5 Site management 10 Facility quality assurance 10.1 Provider credentialing 10.2 M onitor and evaluation A relatively complex model view decomposition 69 Copyright 2015 by Data Blueprint
DSS "Governors" Taxpayers Clients Vendors Program Deliver Data model is comprised of model views 70 Copyright 2015 by Data Blueprint DSS Strategic Data Model Taxpayer view Client view Governance view Program Delivery view Vendor view
Taxpayer view Payments Taxpayers Social Service Programs Taxpayer Benefits 71 Copyright 2015 by Data Blueprint
Client view Payments Clients Client Benefits Local Wellfare Agencies 72 Copyright 2015 by Data Blueprint
Governance view Payments Social Service Programs Governmental Resources Governance Governments State Board of Social Services Policy Approval 73 Copyright 2015 by Data Blueprint
Social Service Programs Clients Service Delivery Partners Local Wellfare Agencies Program Delivery view 74 Copyright 2015 by Data Blueprint
Payments Social Service Programs Clients Local Wellfare Agencies Goods and Services Vendors Vendor view 75 Copyright 2015 by Data Blueprint
Governmental Resources Governance Governments Payments Taxpayers State Board of Social Services Social Service Programs Clients Client Benefits Taxpayer Benefits Policy Approval Service Delivery Partners Local Wellfare Agencies Goods and Services Vendors DSS Strategic Level Data Model 76 Copyright 2015 by Data Blueprint
Challenge 79 Copyright 2015 by Data Blueprint Package Implementation Example • "Green screen" legacy system to be replaced with Windows Icons Mice Pointers (WIMP) interface; and • Major changes to operational processes – 1 screen to 23 screens • Management didn't think workforce could adjust to simultaneous changes – Question: "How big a change will it be to replace all instances of person_identifier with social_security_number?" • Answer: – (from "big" consultants) "Not a very big change." ($5 million budget)
Home Page Business Process   Name Business Process   Component Business Process   Component Step PeopleSoft Process Metadata 80 Copyright 2015 by Data Blueprint Home Page Name (relates to one or more) Business Process Name (relates to one or more) Business Process Component Name (relates to one or more) Business Process Component Step Name
Example Query Outputs 81 Copyright 2015 by Data Blueprint
Home Page Name Business Process Name Business Process Component Name Business Process Component Step Name Peoplesoft Metadata Structureprocesses (39) homepages (7) menugroups (8) components (180) stepnames (822) menunames (86) panels (1421) menuitems (1149) menubars (31) fields (7073) records (2706) parents (264) reports (347) children (647) (41) (8) (182) (847) (949) (86) (281) (1259)(1916) (5873) (264) (647)(708) (647) (25906) (347) 82 Copyright 2015 by Data Blueprint PeoplesoftMetadataStructure
  Quantity System Component Time to make change   Labor Hours 1,400 Panels 15 minutes 350 1,500 Tables 15 minutes 375 984 Business process component steps 15 minutes 246 Total 971 X $200/hour $194,200 X 5 upgrades $1,000,000 Business Value - Better Decisions 83 Copyright 2015 by Data Blueprint
A National Cancer Institute 86 Copyright 2015 by Data Blueprint • This cancer center is a leader in shaping the fight against cancer • Over 500 researchers and staff tend to over 12,000 patients annually • This requires robust information management and analytical services • The problem: It takes 1 month to run a report on an incident, i.e. a patient’s hospital visit that shows all touch points
Other Departments SQL SQLSAS Cancer Registry Claims Database File Export Physician Invoices Patient (Hospital) Patient (Physician) Patient (Registry) Billing Data (Hospital) Billing Data (Physician) Diagnoses (Hospital) Diagnoses (Physician) Diagnoses (Registry) Physicians (Hospital) Physicians (Physician) Access SQL SQL SAS SQL Excel Excel Hospital Claims Text Files FTP FTP Text Files FTP or Email Word Word Word Current State Assessment 87 Copyright 2015 by Data Blueprint
Other Departments SSIS Cancer Registry Hospital Claims Staging SSIS Physician Invoices Patient Demographics Billing Data (Hospital) Billing Data (Physician) Diagnoses (Hospital) Diagnoses (Physician) Diagnoses (Registry) Physicians (Hospital) Physicians (Physician) SSIS SSIS Consolidated/ Sandbox SSIS SSAS Patient (Consolidated) RPT Physicians (Consolidated) Diagnoses (Consolidated) SSR S SharePoint Excel Email One-off reports Reusable reports Conceptual Target Architecture 88 Copyright 2015 by Data Blueprint
0 25 50 75 100 Current Improved Manipulation Analysis Reversing The Measures 89 Copyright 2015 by Data Blueprint • Currently: – Analysts spend 80% of their time manipulating data and 20% of their time analyzing data – Hidden productivity bottlenecks • After rearchitecting: – Analysts spend less time manipulating data and more of their time analyzing data – Significant improvements in knowledge worker productivity A 20% improvement results in a doubling of productivity!
Results: It is not always about money 90 Copyright 2015 by Data Blueprint • Solution: – Integrate multiple databases into one to create holistic view of data – Automation of manual process • Results: – Data is passed safely and effectively – Eliminate inconsistencies, redundancies, and corruption – Ability to cross-analyze – Significantly reduced turnaround time for matching patients with potential donor -> increased potential to make life-saving connection in a manner that is faster, safer and more reliable – Increased safe matches from 3 out of 10 to 6 out of 10
Improving Data Quality during System Migration 93 Copyright 2015 by Data Blueprint • Challenge – Millions of NSN/SKUs   maintained in a catalog – Key and other data stored in   clear text/comment fields – Original suggestion was manual   approach to text extraction – Left the data structuring problem unsolved • Solution – Proprietary, improvable text extraction process – Converted non-tabular data into tabular data – Saved a minimum of $5 million – Literally person centuries of work
Unmatched Items Ignorable Items Items Matched Week # (% Total) (% Total) (% Total) 1 31.47% 1.34% N/A 2 21.22% 6.97% N/A 3 20.66% 7.49% N/A 4 32.48% 11.99% 55.53% … … … … 14 9.02% 22.62% 68.36% 15 9.06% 22.62% 68.33% 16 9.53% 22.62% 67.85% 17 9.5% 22.62% 67.88% 18 7.46% 22.62% 69.92% Copyright 2014 by Data Blueprint Architecture Derived: Diminishing Returns Determination 94
Time needed to review all NSNs once over the life of the project: NSNs 2,000,000 Average time to review & cleanse (in minutes) 5 Total Time (in minutes) 10,000,000 Time available per resource over a one year period of time: Work weeks in a year 48 Work days in a week 5 Work hours in a day 7.5 Work minutes in a day 450 Total Work minutes/year 108,000 Person years required to cleanse each NSN once prior to migration: Minutes needed 10,000,000 Minutes available person/year 108,000 Total Person-Years 92.6 Resource Cost to cleanse NSN's prior to migration: Avg Salary for SME year (not including overhead) $60,000.00 Projected Years Required to Cleanse/Total DLA Person Year Saved 93 Total Cost to Cleanse/Total DLA Savings to Cleanse NSN's: $5.5 million Copyright 2014 by Data Blueprint 95 Quantitative Benefits
Would you build a house without an architecture sketch? Model is the sketch of the system to be built in a project. Would you like to have an estimate how much your new house is going to cost? Your model gives you a very good idea of how demanding the implementation work is going to be! If you hired a set of constructors from all over the world to build your house, would you like them to have a common language? Model is the common language for the project team. Would you like to verify the proposals of the construction team before the work gets started? Models can be reviewed before thousands of hours of implementation work will be done. If it was a great house, would you like to build something rather similar again, in another place? It is possible to implement the system to various platforms using the same model. Would you drill into a wall of your house without a map of the plumbing and electric lines? Models document the system built in a project. This makes life easier for the support and maintenance! Why Architect Data? 98 Copyright 2015 by Data Blueprint
Take Aways 99 Copyright 2015 by Data Blueprint • What is an information architecture? – A structure of data-based information assets   supporting implementation of organizational strategy – Most organizations have data assets that are not supportive of strategies -   i.e., information architectures that are not helpful – The really important question is: how can organizations more effectively use their information architectures to support strategy implementation? • What is meant by use of an information architecture? – Application of data assets towards organizational strategic objectives – Assessed by the maturity of organizational data management practices – Results in increased capabilities, dexterity, and self awareness – Accomplished through use of data-centric development practices (including taxonomies, stewardship, and repository use) • How does an organization achieve better use of its information architecture? – Continuous re-development; the starting point isn't the beginning – Information architecture components must typically be reengineered – Using an iterative, incremental approach, typically focusing on one component at a time and applying formal transformations
Upcoming Events 100Copyright 2015 by Data Blueprint EDW 2015    Developing Data Strategy and Roadmap March 29, 2015 @ 5:00 PM ET Addressing Data Challenges   with the (DMM) Data Management Maturity March 30, 2015 @ 2:00 PM ET/11:00 AM PT  April Webinar: Data Governance Strategies April 14, 2015 @ 2:00 PM ET/11:00 AM PT Sign up here: • www.datablueprint.com/webinar-schedule • www.Dataversity.net Brought to you by: PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
Questions? 101Copyright 2015 by Data Blueprint + =

Check these out next

Data-Ed: Data Architecture Requirements

Recomendados

Más contenido relacionado

Presentaciones para ti(19)

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

Más de Data Blueprint(20)

Último(20)

Data-Ed: Data Architecture Requirements