Healthcare Analytics 101 Workshop

Healthcare Analytics 101 Workshop The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your Most Valuable Asset Necessary Pre-requisites Peter Aiken and Michael Gorman Peter Aiken, Ph.D. 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

With thanks to: J. Brian Cassel, PhD Senior Analyst Oncology Administration VCU Health System Lisa Shickle, MS, former director Massey Data Analytics (now at Wellpoint) Gordon Ginder, MD & Mary Ann Hager, MSN, Massey / VCUHS Kathleen Kerr, Kerr Healthcare Analytics Tom Smith, Johns Hopkins Massey / VCUHS palliative care team 3 Slide 39 4

IBM's Data Baby 5 Bills of Mortality by Captain John Graunt 6

of Mortality 7 Where is it happening? Mortality Geocoding 8

Plague Peak ("Whereas of the Plague") When is it happening? 9 Black Rats or Rattus Rattus Black Rats or Rattus Rattus Why is it happening? Why is it happening? 10

What will happen? 11 John Snow's 1854 Cholera Map of London 12

101 Workshop: Necessary Pre-requisites 1. Adopting a crawl, walk, run strategy 2. Understanding current and potential organizational maturity and corresponding capabilities 3. Achieving an appropriate technology/human capability balance 4. Implementing useful IT systems development practices 5. Installing necessary non-it leadership 13 IT Project Failure Rates Recent IT project failure rates statistics can be summarized as follows: Carr 1994 16% of IT Projects completed on time, within budget, with full functionality OASIG Study (1995) 7 out of 10 IT projects "fail" in some respect The Chaos Report (1995) 75% blew their schedules by 30% or more 31% of projects will be canceled before they ever get completed 53% of projects will cost over 189% of their original estimates 16% for projects are completed on-time and on-budget KPMG Canada Survey (1997) 61% of IT projects were deemed to have failed Conference Board Survey (2001) Only 1 in 3 large IT project customers were very satisfied" Robbins-Gioia Survey (2001) 1 in 3 IT projects suffers on Price Schedule Functionality 51% of respondents viewed their large IT implementation project as unsuccessful MacDonalds Innovate (2002) Automate fast food network from fry temperature to # of burgers sold-$180m USD write-off Ford Everest (2004) Replacing internal purchasing systems-$200 million over budget FBI (2005) Blew $170M USD on suspected terrorist database-"start over from scratch" http://www.it-cortex.com/stat_failure_rate.htm (accessed 9/14/02) New York Times 1/22/05 pa31 14

60% IT Project Failure Rates (moving average) 45% 53% 46% 49% 51% 53% 44% 40% 30% 31% 33% 27% 28% 26% 28% 23% 34% 29% 32% 24% 15% 16% 15% 18% Failed Challenged Succeeded 0% 1994 1993 1998 2000 2002 2004 2009 15 Source: Standish Chaos Reports as reported at: http://www.galorath.com/wp/software-project-failure-costs-billions-better-estimation-planning-can-help.php % of DM organizations labeled "successful" 1981 2007 0.45 0.36 0.27 0.18 0.09 Successful In 25 years: Partial Success Don't know/too soon to tell "Successful" DM organizations fell from 43% to 15% "Unsuccessful" increased from 5% to 21%. Unsuccessful Does not exist 0 16

Why Data Projects Fail by Joseph R. Hudicka $0 $125,000 $250,000 $375,000 Assessed 1200 migration projects! Surveyed only experienced migration specialists who have done at least four migration projects Median Project Expense Median Project Cost The median project costs over 10 times the amount planned! Biggest Challenges: Bad Data; Missing Data; Duplicate Data The survey did not consider projects that were cancelled largely due to data migration difficulties " problems are encountered rather than discovered" Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-31 17 Not Enough Data Management Involvement Data Warehousing XML Data Quality Customer Relationship Management Master Data Management Customer Data Integration Enterprise Resource Planning Enterprise Application Integration Initiative Leader Initiative Involvement Not Involved 18

12bn NHS computer system is scrapped The biggest civilian IT project of its kind in the world, it has already squandered at least 12.7billion. Some estimates put the cost far higher. Following an official review, the one size fits all IT project will be replaced by much cheaper regional initiatives, with hospitals and GPs choosing the IT system they need. Read more: http://www.dailymail.co.uk/news/article-2040259/nhs-itproject-failure-labours-12bn-scheme-scrapped.html#ixzz2r1yb9f1i 19 Data Strategy in Context Organizational IT Strategy Data Strategy Only 1 is 10 organizations has a board approved data strategy! 20

What does it mean to treat data as an organizational asset? Assets are economic resources Must own or control Must use to produce value Value can be converted into cash An asset is a resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow to the organization [Wikipedia] Data are an organization's Sole, non-depletable, non-degrading, durable, strategic asset With assets: Formalize the care and feeding of data Cash management - HR planning Put data to work in unique/significant ways Identify data the organization will need [Redman 2008] 21 Reduce-Reuse-Recycle Data? Reduce the amount of organizational data ROT Redundant, obsolete, trivial Reuse the remainder Fewer vocabulary items to resolve Greater quality engineering leverage Integration is impossible without information architecture components (for mapping) Maintenance of these components promotes greater reuse Shared data is typified by organizational ability to use information as a strategic asset However, assets are useless without knowledge of the asset characteristics 22

Leverage is an Engineering Concept 23 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) 24

Innovation Process Less ROT Data Leverage Technologies People Permits organizations to better manage their sole non-depletable, nondegrading, 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 and the pace of innovation 25 Data Strategy Choices Q3 Using data to create strategic opportunities Q4 Both (Cash Cow) Only 1 is 10 organizations has a board approved data strategy! Q1 Keeping the doors open (little or no proactive data management) Q2 Increasing organizational efficiencies/effectiveness Improve Operations 26

Great point of initial inspiration... Formalizing stuff forces clarity Special shout out to Chapter 7 Measuring the value of information ISBN: 0470539399 http://www.amazon.com/how- Measure-Anything-Intangibles- Business 27 A National Cancer Institute This Virginia 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 28

A National Cancer Institute (cont d) Data Blueprint engineered a solution that provides a 360 degree view of an incident, i.e. patient s hospital visit New solution provides reports in 2 days: 360 degree view of patient s data including diagnosis, treatment, etc. Integrated hospital and physician data enhances financial and asset utilization Results include improved quality of care, optimized workflow processes as well as operational performance 29 Overview of Existing Data Management Process 1.Manual transfer of digital data 2.Manual file movement/duplication 3.Manual data manipulation 4.Disparate synonym reconciliation 5.Tribal knowledge requirements 6.Non-sustainable technology 30

100 Reversing The Measures Manipulation Analysis 75 50 25 0 Current Improved Currently: Analysts spend 80% of their time manipulating data and 20% of their time analyzing data Used to take 1 month to produce key reports After rearchitecting: Analysts spend 20% of their time manipulating data and 80% of their time analyzing data Two days to produce key reports 31 $300 billion is the potential annual value to health care $47 $108 $9 $5 $165 Savings come from a variety of agreed upon categories and values: Reduced hospital readmissions Patient Monitoring: Inpatient, out-patient, emergency visits and ICU Preventive care for ACO Epidemiology Patient care quality and program analysis Transparency in clinical data and clinical decision support Research & Development Advanced fraud detection-performance based drug pricing Public health surveillance/response systems Aggregation of patient records, online platforms, & communities 32

Book Recommendation Permits the reorientation of medicine From populations To individuals Big Data Capture Wireless sensors Genome sequencing Printing organs 33 Analytics in Health Care Descriptive Ask: What happened? What is happening? Find: Structured data Show: Profiles, Bar/pie charts, Narrative Predictive Ask: What will happen? Why will it happen? Find: Structured/unstructured data Show: Risk Profiles, Pros/Cons, Care Recs Prescriptive Ask: What should I do? Why should I do it? Find: Unstructured/structured data Show: Strategic Goals, Support Recs! Organization-wide! Volume and Noise! Utility! Meaningful scoring! Actionable recs! Realistic goals! Support! Manage & measure 3

35 Results: It is not always about money 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 36

Our hospital wants us to use the existing system, can we create an Oncology cube? Can you get all the information you need in a cube from an existing business intelligence data system? Would it include outpatient care? Would it capture the whole care continuum? Would it allow you to categorize by disease type? Would it allow you to categorize by modality of care? 37 Getting the C-suite s attention: How much of the hospital s business is Oncology? Disease-centered analyses are not limited to cost centers, divisions, clinics, units, or other silos for strategic planning purposes. 9

Profitability by disease and modality 13 39 Market Analysis Two measures of market share indicate that 26% - 32% of cancer patients in Central Virginia receive some or all of their treatment here. In other words, 68% - 74% receive none of their care here. VCU is capturing only 20% of inpatient oncologic surgeries originating in our primary service area (Bon Secours captures 40%, HCA 36%). VCU is 4 th in state for oncologic surgeries, behind UVA, Inova Fairfax, and St Mary s. This gives us plenty of opportunity to increase volumes. 40 21

State-wide, regional context 41 22 Patient-based analyses following patient from diagnosis through treatment Hospital Cancer Registry x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x From diagnosis: Primary site of cancer Date of diagnosis Stage/spread of disease + Hospital, Physician, Pharm Claims x x 1111111 x x x x x x x 2222222 x x x x x x x x x x x x x x x x 3333333 x x x x x x x x x x 4444444 x x x x x x x x x x x x 5555555 x x x Follow patient interactions over time: Capture all encounter dates and details 42

Consulting firm: Close down palliative care program VCU Health System opened one of first Palliative Care Units in the US, May 2000. Consultants recommended closing it in 2002. They looked at net margin for hospitalizations ending on the PC Unit and saw that the costs greatly exceeded reimbursement. They thought that getting rid of the unit would get rid of this problem. RWJ Foundation supported urgent response. Appropriate financial analyses convinced consultants that the unit actually produced valuable hospital outcomes. See KR White & JB Cassel (2009). The Business Case for a Hospital Palliative Care Unit: Justifying its Continued Existence. Practice of Evidence- Based Management, T Kovner, D Fine & R D Aquila (Eds.), Chicago: Health Administration Press, pp 171-180. 43 Cost-avoidance in drugs (-77%), labs (-95%), imaging (-95%), supplies (-60%). 44

Morrison, Penrod, Cassel et al. (2008). Cost savings associated with US hospital palliative care consultation programs. Archives of Internal Medicine 168 (16), 1783-1790. 8 Hospital study of cost reduction 45 Slide 34 8 Hospital Study of Cost Reduction 2000.0000 1750.0000 Usual Care Direct Cost ($) 1500.0000 1250.0000 1000.0000 750.0000 PC consult day 10-11 PC consult day 12-13 500.0000 250.0000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Day of Admission Morrison, Penrod, Cassel et al. (2008). Cost savings associated with US hospital palliative care consultation programs. Archives 46 of Internal Medicine 168 (16), 1783-1790. Slide 35

A closer look What we know from the cancer! registry What we gain from integrating billing claims 47 101 Workshop: Necessary Pre-requisites 1. Adopting a crawl, walk, run strategy 2. Understanding current and potential organizational maturity and corresponding capabilities 3. Achieving an appropriate technology/human capability balance 4. Implementing useful IT systems development practices 5. Installing necessary non-it leadership 48

Not Enough Data Management Involvement Data Warehousing XML Data Quality Customer Relationship Management Master Data Management Customer Data Integration Enterprise Resource Planning Enterprise Application Integration Initiative Leader Initiative Involvement Not Involved 49 % of DM organizations labeled "successful" 0.45 0.36 0.27 0.18 0.09 Successful In 25 years: Partial Success Don't know/too soon to tell "Successful" DM organizations fell from 43% to 15% "Unsuccessful" increased from 5% to 21%. Unsuccessful Does not exist 1981 2007 0 50

DM Origins Which arrives first DM or DBMS? 1981 2007 26% 68% 75% DM 1st 9% DBMS 1st 6% Simultaneously A key indicator of organizational awareness 75% reacting instead of anticipating Best practices are obvious 6% 51 Why Data Projects Fail by Joseph R. Hudicka $0 $125,000 $250,000 $375,000 $500,000 Assessed 1200 migration projects! Surveyed only experienced migration specialists who have done at least four migration projects Median Project Expense Median Project Cost The median project costs over 10 times the amount planned! Biggest Challenges: Bad Data; Missing Data; Duplicate Data The survey did not consider projects that were cancelled largely due to data migration difficulties " problems are encountered rather than discovered" Joseph R. Hudicka "Why ETL and Data Migration Projects Fail" Oracle Developers Technical Users Group Journal June 2005 pp. 29-31 52

Largely Ineffective Data Management Investments Investments Approximately, 10% percent of organizations achieve parity and (potential positive returns) on their DM investments Only 30% of DM investments achieve tangible returns at all Seventy percent of organizations have very small or no tangible return on their DM investments Return 0 70% Investment <= Return 10% Investment > Return 20% 53 54

Cruiser Collector 55 Data management processes and infrastructure Data Program Coordination Combining multiple assets to produce extra value Five Integrated DM Practice Areas Goals Organizational Strategies Organizational Data Integration Organizational-entity subject area data integration Data Stewardship Integrated Models Standard Data Implementation Guidance Achieve sharing of data within a business area Data Development Feedback Direction Provide reliable data access Data Support Operations Application Models & Designs Business Data Data Asset Use Leverage data in organizational activities Business Value 56

Organizational DM Practices and Inter-relationships Data Program Coordination Manage data coherently. Organizational Data Integration Share data across boundaries. Data Stewardship Data Development Assign responsibilities for data. Engineer data delivery systems. Data Support Operations Maintain data availability. 57 Data Management Capability Maturity Model Levels We have a process for improving our DM capabilities We manage our DM processes so that the whole organization can follow our standard DM guidance We have experience that we have standardized so that all in the organization can follow it Initial (1) Repeatable (2) Defined (3) Managed (4) Optimizing (5) One concept for process improvement, others include: Norton Stage Theory TQM TQdM TDQM ISO 9000 and focus on understanding current processes and determining where to make improvements. We have DM experience and have the ability to implement disciplined processes Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts

Assessment Components Data Management Practice Areas Data program coordination Organizational data integration DM is practiced as a coherent and coordinated set of activities Delivery of data is support of organizational objectives the currency of DM Capability Maturity Model Levels 1 Initial 2 - Repeatable Examples of practice maturity Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts We have DM experience and have the ability to implement disciplined processes Data stewardship Designating specific individuals caretakers for certain data 3 - Documented We have standardized DM practices so that all in the organization can perform it with uniform quality Data development Efficient delivery of data via appropriate channels 4 - Managed We manage our DM processes so that the whole organization can follow our standard DM guidance Data support Ensuring reliable access to data 5 - Optimizing We have a process for improving our DM capabilities 59 CMU's Software Engineering Institute (SEI) Collaboration Results from hundreds organizations in various industries including: Public Companies State Government Agencies Federal Government International Organizations Defined industry standard Steps toward defining data management "state of the practice" Data Management Practices Measurement (DMPA) Initial (I) Repeatable (II) Documented (III) Managed (IV) Optimizing (V) Data Program Coordination Organizational Data Integration Focus: Guidance and Facilitation Data Stewardship Data Development Data Support Operations Focus: Implementation and Access 60

5 Comparison of DM Maturity 2007-2012 2007 Maturity Levels 2012 Maturity Levels 4 3 2 1 Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations 61 Service Orient or Be Doomed! Service Orient or Be Doomed! How Service Orientation Will Change Your Business (Hardcover) by Jason Bloomberg & Ronald Schmelzer I'm not quite sure what "doom" awaits by not service orienting, other than remaining mired in archaic, calcified and siloed processes which a lot of businesses do anyway, and still manage to stay afloat. But that's the topic for another posting. Reviewer 62

How SOA/Services are "Sold" Integration Possibilities User Interface Business Process Application Data AV Component Well defined components Self-contained No interdependencies Analogy derived from D. Barry "Web Services" Intelligent Enterprise 10/10/03 pp. 26-47 - wiring diagram from sunflowerbroadband.com 63 Contractor Implemented Wiring 64

Concise Notes on Software Engineering Published in 1979 93 pages including appendices & references Out of print $1.99 at half.com Principles of Information Hiding (p. 32-33) Conceal complex data structures whenever possible Allow only selected service modules to know about the concealed data structures Bind together modules that know about concealed data structures Package such modules along with the data itself 65 SOA is Dead; Long Live Services 8 April 2009 Anne Thomas Manes VP & Research Director amanes@burtongroup.com www.burtongroup.com We have a replay of the presentation, which I gave in February, on the Burton BrightTalk Channel: http://www.brighttalk.com/channels/750/view (You have to page down to get to the SOA is Dead presentation.) 66 All Contents 2008 Burton Group. All rights reserved.

SOA Obituary 67 SOA met its demise on January 1, 2009, when it was wiped out by the catastrophic impact of the economic recession. SOA is survived by its offspring: mashups, SaaS, Cloud Computing, BPM, and all other architectural approaches that depend on "services." ECONOMY SOAsaurus SOA Postmortem: Why did it die? Vague abstract architectural concept No universally accepted meaning Indefensible value proposition How do you measure flexibility/agility? Cost savings are lower than anticipated Success rate is very low Ill defined term of dubious business value All Contents 2008 Burton Group. All rights reserved. SOA DM Maturity Requirements 1.00 2.00 3.00 4.00 Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations Conclusion - more ground to cover than has been attained to date 68

The Insider's Guide to Business and IT Agility Leveraging Information and Intelligence David Linthicum Lack of Focus on Data Killing SOA Search Advanced Search Lack of Focus on Data is Killing SOA Go Login Sign Up Home Event Center Solution Center Blogs Subscriptions White Papers Industry expert Dave Linthicum's tells you what you need to know about building efficiency into the information management infrastructure By David Linthicum on July 24, 2009 1:42 PM 14 Vote 0 Votes What's missing within most typical SOA projects is the focus on the data For those of you that have been following me know that I'm very much an advocate of SOA. The architectural pattern of SOA is helpful in defining an enterprise architecture that much more agile, and thus pays for itself once the business has to shift and needs IT to follow. Leave a comment Vijay Narayanan July 26, 2009 10:42 PM Reply Completely agree David. I think data from a SOA standpoint is extremely critical for several reasons: - reducing errors/rework in automated business processes that leverage enterprise data services David Linthicum David Linthicum is an internationally known distributed computing and application integration expert. View more SOA, however, is complex and requires that the architect understand all aspects of the "as is" architecture before moving to the "to be." This means decomposing the existing architecture down to Subscribe a primitive state, and rebuilding it up again at sets of services, with a process configuration or composite applications layer to define and redefine business functions. I think most The get that. truth is Subscribe that in a the reader foundation What's missing within most typical SOA projects is the focus on the data, and that is killing SOA. of a healthy Subscribe and to SOA functional Visionaries Since the "S" in SOA, means service, most architects focus on the service definition, abstracting the existing data into collections of services, but don't pay much attention to the data within the SOA is the data architecture. Not good. Recently Commented On The truth is that the foundation of a healthy and functional SOA is the data, and you have to deal with the underlying data first, understand it, perhaps reorganize and abstract it, before defining the services that will sit on top of the data. While this is architecture 101, the fact is that those driving More on Why Big BI is Bad BI (2) SOAs these days have little understanding Most of the failed importance of SOA understanding projects and defining the data, can be Brian Gentile traced wrote: In the to near-term, the BI and thus the architecture ends up being a bunch of well defined services that sit on top of very vendor consoli... [more] dysfunctional data. The end result is performance issues, integrity issues, and even the lack of lack of a data level understanding Why IBM Buying SPSS is bad for BI (3) agility which is why you build SOAs in the first place. Chris Ballenger wrote: Well the world of BI The truth is that most failed SOA projects can be traced to the lack of a data level understanding, may be getting... [more] and while this is still an issue in this day and time is beyond me. There are many technology and Lack of Focus on Data Killing SOA (14) tools out there to assist you, and we've been doing data for a long long time. Nothing new here, just Kingsley Idehen wrote: David, I've come data. However, if you ignore it your SOA will be still born. late to this post... [more] 69 "Fighting cancer with Business Intelligence" More Articles: (1) guest wrote: As an IT professional and a «"Fighting cancer with Business Intelligence" Main Why IBM Buying SPSS is bad for BI» person... [more] BI is Evolving Quickly (3) Subscribe Email Slashdot Abel_T wrote: On the web-based EVERYTHING note, I... [more] reddit Digg De.li.cio.us Stumble It! newsvine http://3.bp.blogspot.com/-ptl-9maieuq/t-idbt1yfmi/aaaaaaaabgw/ib-nvkmmmeq/s1600/maslows_hierarchy_of_needs.png Hierarchy of Data Management Practices (after Maslow) 14 Comments 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 Recent Entries More on Why Big BI is Bad BI Why IBM Buying SPSS is bad for BI Lack of Focus on Data Killing SOA http://www.ebizq.net/blogs/linthicum/2009/07/lack_of_focus_on_da WfF9wsRons67fLqzsmxzEJ8%2Fx6%2BwqT%2Frn28M3109ad%2BrmPBy93Yo%3D Advanced Data Practices Cloud MDM Mining Big Data Analytics Warehousing SOA Basic Data Management Practices Data Program Management Organizational Data Integration Data Stewardship Data Development Data Support Operations Page 1 of 6 http://3.bp.blogspot.com/-ptl-9maieuq/t-idbt1yfmi/aaaaaaaabgw/ib-nvkmmmeq/s1600/maslows_hierarchy_of_needs.png

101 Workshop: Necessary Pre-requisites 1. Adopting a crawl, walk, run strategy 2. Understanding current and potential organizational maturity and corresponding capabilities 3. Achieving an appropriate technology/human capability balance 4. Implementing useful IT systems development practices 5. Installing necessary non-it leadership 71 J. C. R. Lickleider's Man-Computer Symbiosis Best approaches combines manual and automated reconciliation! Humans Generally Better Machines Generally Better Sense low level stimuli Detect stimuli in noisy background Recognize constant patterns in varying situations Sense unusual and unexpected events Remember principles and strategies Retrieve pertinent details without a priori connection Draw upon experience and adapt decision to situation Select alternatives if original approach fails Reason inductively; generalize from observations Act in unanticipated emergencies and novel situations Apply principles to solve varied problems Make subjective evaluations Develop new solutions Concentrate on important tasks when overload occurs Adapt physical response to changes in situation Sense stimuli outside human's range Count or measure physical quantities Store quantities of coded information accurately Monitor prespecified events, especially infrequent Make rapid and consisted responses to input signals Recall quantities of detailed information accurately Retrieve pertinent detailed without a priori connection Process quantitative data in prespecified ways Perform repetitive preprogrammed actions reliably Exert great, highly controlled physical force Perform several activities simultaneously Maintain operations under heavy operation load Maintain performance over extended periods of time 72

2012 London Summer Games 60 GB of data/second 200,000 hours of big data will be generated testing systems 2,000 hours media coverage/ daily 845 million facebook users averaging 15 TB/day 13,000 tweets/second 4 billion watching 8.5 billion devices connected 73 Corporate Governance "Corporate governance - which can be defined narrowly as the relationship of a company to its shareholders or, more broadly, as its relationship to society.", Financial Times, 1997. "Corporate governance is about promoting corporate fairness, transparency and accountability" James Wolfensohn, World Bank, President Financial Times, June 1999. Corporate governance deals with the ways in which suppliers of finance to corporations assure themselves of getting a return on their investment, The Journal of Finance, Shleifer and Vishny, 1997. 74

Definition of IT Governance IT Governance: "putting structure around how organizations align IT strategy with business strategy, ensuring that companies stay on track to achieve their strategies and goals, and implementing good ways to measure IT s performance. It makes sure that all stakeholders interests are taken into account and that processes provide measurable results. An IT governance framework should answer some key questions, such as how the IT department is functioning overall, what key metrics management needs and what return IT is giving back to the business from the investment it s making." CIO Magazine (May 2007) According to the IT Governance Institute, there are five areas of focus: Strategic Alignment Value Delivery Resource Management Risk Management Performance Measures 75 Data Governance Definitions The other half of MDM The Bloor Group The formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset. - The MDM Institute A convergence of data quality, data management, business process management, and risk management surrounding the handling of data in an organization Wikipedia A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods Data Governance Institute The execution and enforcement of authority over the management of data assets and the performance of data functions KiK Consulting A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information IBM Data Governance Council Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions Sunil Soares The exercise of authority and control over the management of data assets DM BoK 76

Suicide Mitigation 77 Suicide Mitigation Data Mapping Deploy ments Work History Abuse Soldier Mental illness Legal Issues Suicide Analysis DMSS G1 DMDC FAP CID MDR Data objects complete? All sources identified? Best source for each object? How reconcile differences between sources? 12 78

Senior Army Official A very heavy dose of management support Any questions as to future data ownership, "they should make an appointment to speak directly with me!" Empower the team The conversation turned from "can this be done?" to "how are we going to accomplish this?" Mistakes along the way would be tolerated Implement a workable solution in prototype form 79 Communication Patterns 80 Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide Copyright and 2013 by Saving Data Blueprint Lives - The Final Report of the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010

Technique/Technical Interdependencies Master Data Management Data Governance Data Quality 81 Benefits of a Database Data can be shared Redundancy can be reduced All redundancy cannot be or necessarily should be reduced Inconsistency can be avoided Data obtained by Physics department will be the same as the Chemistry department Transaction support can be provided Transaction is not complete until money is deleted from the savings account after adding it to the checking account Integrity can be maintained A student can be recorded as having obtained 1000 marks, as compared to 100 this can be corrected by enforcing integrity. Security can be enforced Information on demand Finance need to see the records related to Human resources Conflicting requirements can be balanced Volume of data as compared to speed Business Standards can be enforced Data Dependency Technique used to physically stored and accessed are dictated by the application, and the knowledge of physical representation and access technique is built into the application code. Not desirable in a Database System Different users require different views of the same data Freedom to change the physical representation or access technique in view of the changing requirements Changing record types Physical storage location 82

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. 83 Typically Managed Architectures Enterprise Architecture Business Architecture Systems Architecture 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 Hierarchical Arrangement Network Arrangement 84

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 85 Data Architecture Better Definition 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] 86

Vocabulary is Important-Tank, Tanks, Tankers, Tanked 87 How one inventory item proliferates data throughout the chain 555"Subassemblies"&"subcomponents System 1: 18,214 Total items 75 Attributes/ item 1,366,050 Total attributes 17,659"Repair"parts"or"Consumables System"2 47"Total"items 15+"A@ributes/item 720"Total"a@ributes System"3 16,594"Total"items 73"A@ributes/item 1,211,362"Total"a@ributes System"4 8,535"Total"items 16""A@ributes/item 136,560"Total"a@ributes System"5 15,959""Total"items 22""A@ributes/item 351,098"Total"a@ributes Total"for"the"five"systems"show"above: 59,350"Items 179"Unique"a@ributes 3,065,790"values 88

Business Implications National Stock Number (NSN) Discrepancies If NSNs in LUAF, GABF, and RTLS are not present in the MHIF, these records cannot be updated in SASSY Additional overhead is created to correct data before performing the real maintenance of records Serial Number Duplication If multiple items are assigned the same serial number in RTLS, the traceability of those items is severely impacted Approximately $531 million of SAC 3 items have duplicated serial numbers On-Hand Quantity Discrepancies If the LUAF O/H QTY and number of items serialized in RTLS conflict, there can be no clear answer as to how many items a unit actually has on-hand Approximately $5 billion of equipment does not tie out between the LUAF and RTLS Information Architecture Representation Information architectures are the symbolic representation of the structure, use and reuse of information resources Common components are represented using standardized notation and 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. 90

Architectural Answers Where do they go? When are they needed? What standards should be adopted? What vendors should be chosen? What rules should govern the decisions? Computers Policies, directives, and rules Management responsibilities Human resources Communication facilities What policies should guide Software 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? Data (Adapted from [Allen & Boynton 1991]) 91 Data structures organized into an Architecture 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/two separate strategies Achieving efficiency and effectiveness goals Providing organizational dexterity for rapid implementation 92

Products Product Types Warehouses Parts Bins Customers Territories Orders Employees Vehicles Accounts Forecast Sales Plan Production Sell Products Take Orders Train Employees Assign Territories Develop Markets Maintain Facilities Repair Record Products Transctns Material Supply Ntwk Product Dist. Ntwk Voice Comm. Ntwk Data Comm. Ntwk Manu. Process Ntwk Office Wrk Flow Ntwk Parts Dist. Ntwk Personnel Dist. Ntwk etc., etc. General Mgmt Product Mgmt Engineering Design Manu. Engineering Accounting Finance Transportation Distribution Marketing Sales A li A li i Product Cycle Market Cycle Planning Cycle Order Cycle Employee Cycle Maint. Cycle Production Cycle Sales Cycle Economic Accounting Cycle Cycle C o m p o s i t e I n t e g r a t i o n s Inventory Identification Process Identification C o m p o s i t e I n t e g r a t i o n s New Markets Revenue Growth Expns Reduction Cust Convenience Customer Satis. Regulatory Comp. New Capital Social Contribution Increased Yield Increased Quality A l i g n m e n t Distribution Identification Responsibility Identification C o m p o s i t e I n t e g r a t i o n s Timing Identification Motivation Identification e.g. Accounts e.g. Record Transctns e.g. e.g. Sales e.g. Accounting Cycle e.g. (Business Context (Scope Identification Planners) Lists) List: Inventory Types List: Process Types List: Distribution Types List: Responsibility Types List: Timing Types List: Motivation Types Inventory Definition Process Definition Distribution Definition Responsibility Definition Timing Definition Motivation Definition e.g.: primitive e.g.: composite model: model: e.g. e.g. e.g. e.g. e.g. e.g. (Business Concept i i (Business Definition Owners) Business Entity Business Transform Business Location Business Role Business Interval Business End Models) Business Relationship Business Input/Output Business Connection Business Work Product Business Moment Business Means Inventory Representation Process Representation Distribution Representation Responsibility Representation Timing Representation Motivation Representation (Business Logic Designers) (Business Physics Builders) (Business Component Implementers) (Users) e.g. e.g. e.g. e.g. e.g. e.g. System Entity System Relationship Inventory Specification e.g. e.g. e.g. e.g. e.g. e.g. Technology Entity Technology Relationship e.g. e.g. e.g. e.g. e.g. e.g. Tool Entity Tool Relationship System Transform System Location System Role System Interval System Input /Output System Connection System Work Product System Moment Process Specification Distribution Specification Responsibility Specification Timing Specification Technology Transform Technology Input /Output Tool Transform Tool Input /Output Technology Location Technology Connection Tool Location Tool Connection A l i g n m e n t Technology Role Technology Work Product Tool Role Tool Work Product Technology Interval Technology Moment Tool Interval Tool Moment System End System Means Motivation Specification Technology End Technology Means Motivation Configuration A li Tool End Tool Means C o m p o s i t e I n t e g r a t i o n s A li i (System Representation Models) (Technology Specification Models) (Tool Configuration Models) (Implementations) *Horizontal integration lines are shown for example purposes only and are not a complete set. Composite, integrative relationships connecting every cell horizontally potentially exist. Classification Names Classification Names What How Where Who When Why Zachman Framework 3.0 - the Enterprise Ontology 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 Audience Perspectives Executive Perspective (Business Context Planners) A li g n m e n t T ra Inventory Identification A l i g n m e n t Distribution Identification Responsibility Identification e.g. e.g. e.g. e.g. e.g. e.g. List: Inventory Types Process Identification List: Process Types List: Distribution Types List: Responsibility Types Timing Identification List: Timing Types Motivation Identification A li List: Motivation Types g n m e n t T ra Model Names Scope Contexts (Scope Identification Lists) Business Mgmt Perspective (Business Concept Owners) n s fo r m at i o n s Inventory Definition Process Definition Distribution Definition Responsibility Definition Timing Definition Motivation Definition e.g.: primitive e.g.: composite model: model: e.g. e.g. e.g. e.g. e.g. e.g. Business Entity Business Relationship Business Transform Business Input/Output Business Location Business Connection Business Role Business Work Product Business Interval Business Moment Business End Business Means n s fo r m at i o n s Business Concepts (Business Definition Models) Architect Perspective (Business Logic Designers) Inventory Representation e.g. e.g. e.g. e.g. e.g. e.g. System Entity System Relationship Process Representation Distribution Representation Responsibility Representation Timing Representation System Transform System Input /Output System Location System Connection System Role System Work Product System Interval System Moment Motivation Representation System End System Means System Logic (System Representation Models) Engineer Perspective (Business Physics Builders) Inventory Specification e.g. e.g. e.g. e.g. e.g. e.g. Technology Entity Technology Relationship Process Specification Distribution Specification Responsibility Specification Timing Specification Technology Transform Technology Input /Output Technology Location Technology Connection Technology Role Technology Work Product Technology Interval Technology Moment Motivation Specification Technology End Technology Means Technology Physics (Technology Specification Models) Technician Perspective (Business Component Implementers) A li g n m e n t T ra Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration e.g. e.g. e.g. e.g. e.g. e.g. Tool Entity Tool Relationship Tool Transform Tool Input /Output Tool Location Tool Connection Tool Role Tool Work Product Tool Interval Tool Moment Motivation Configuration A li Tool End Tool Means g n m e n t T ra Tool Components (Tool Configuration Models) Enterprise Perspective (Users) The Enterprise Audience Perspectives Enterprise Names n s fo r m at Inventory Instantiations i o n Operations Entities s Operations Relationships C o m p o s i t e I n t e g r a t i o n s Inventory Sets Process Instantiations Operations Transforms Operations In/Outputs Process Flows Distribution Instantiations Operations Locations Operations Connections Distribution Networks A l i g n m e n t Responsibility Instantiations Operations Roles Operations Work Products Responsibility Assignments Timing Instantiations Operations Intervals Operations Moments Timing Cycles Motivation Instantiations Operations Ends Operations Means C o m p o s i t e I n t e g r a t i o n s Motivation Intentions n s fo r m at i o n s Operations Instances (Implementations) The Enterprise *Horizontal integration lines are shown for example purposes only and are not a complete set. Composite, integrative relationships connecting every cell horizontally potentially exist. 2013 by Data Blueprint Copyright 2008-2011 John A. Zachman 93 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 Classification Names Audience Perspectives Executive Perspective Business Mgmt Perspective Architect Perspective Engineer Perspective Technician Perspective Enterprise Perspective The Enterprise g n m e n t T ra n s fo r m at o n s g n m e n t T ra n s fo r m at What Products Product Types Warehouses Parts Bins Customers Territories Orders Employees Vehicles Inventory Instantiations o n Operations Entities s Operations Relationships How Where Who When Forecast Sales Plan Production Sell Products Take Orders Train Employees Assign Territories Develop Markets Maintain Facilities Repair Products Process Instantiations Operations Transforms Operations In/Outputs Material Supply Ntwk Product Dist. Ntwk Voice Comm. Ntwk Data Comm. Ntwk Manu. Process Ntwk Office Wrk Flow Ntwk Parts Dist. Ntwk Personnel Dist. Ntwk etc., etc. Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration Distribution Instantiations Operations Locations Operations Connections General Mgmt Product Mgmt Engineering Design Manu. Engineering Accounting Finance Transportation Distribution Marketing Responsibility Instantiations Operations Roles Operations Work Products Product Cycle Market Cycle Planning Cycle Order Cycle Employee Cycle Maint. Cycle Production Cycle Sales Cycle Economic Cycle Timing Instantiations Operations Intervals Operations Moments Why New Markets Revenue Growth Expns Reduction Cust Convenience Customer Satis. Regulatory Comp. New Capital Social Contribution Increased Yield Increased Quality Motivation Instantiations Operations Ends Operations Means g n m e n t T ra n s fo r m at o n s g n m e n t T ra n s fo r m at o n s Classification Names Model Names Scope Contexts Business Concepts System Logic Technology Physics Tool Components Operations Instances The Enterprise Audience Perspectives Enterprise Names Inventory Process Sets Flows Distribution Responsibility Networks Assignments Timing Motivation Cycles Intentions The really important question is: how can organizations more effectively use their information architectures to support strategy implementation? 94

How are data structures expressed as architectures? Details are organized into larger components Larger components are organized into models Models are organized into architectures 95 Information Architectures produce and are made up of information models that are developed in response to organizational needs Organizational Needs become instantiated and integrated into an!!!! Informa(on)System) Requirements Information Architecture authorizes and articulates satisfy specific organizational needs #dataed 96

Reengineering O-1/3 reconstitute original metadata O-4/5 improve the current metadata O-6/9 improve system data capabilities based on the improved metadata Reverse Engineering Existing New As Is Information Requirements Assets O3 Recreate Requirements To Be Requirements Assets As Is Data Design Assets As Is Data Implementation O5 Reconstitute Assets Requirements O7 Redevelop Requirements O2 Recreate Data Design To Be Design Assets O6 Redesign Data Metadata O4 Reconstitute Data Design To Be Data Implementation Assets O1 Recreate Data Implementation O8 Redesign Data O9 Reimplement Data Forward engineering 97 Reengineering Options O-1 data implementation (e.g., by recreating descriptions of implemented file layouts); O-2 data designs (e.g., by recreating the logical system design layouts); or O-3 information requirements (e.g., by recreating existing system specifications and business rules). O-4 data design assets by examining the existing data implementation (when appropriate O-1 can facilitate O-4); and O-5 system information requirements by reverse engineering the data design O-4. (Note: if the data design doesn't exist O-4 must precede O-5.) O-6 transforming as is data design assets, yielding improved to be data designs that are based on reconstituted data design assets produced by O-2 or O-4 and (possibly O-1); O-7 transforming as is system requirements into to be system requirements that are based on reconstituted system requirements produced by O-3 or O-5 and (possibly O-2); O-8 redesigning to be data design assets using the to be system requirements based on reconstituted system requirements produced by O-7; and O-9 re-implementing system data based on data redesigns produced by O-6 or O-8. 98

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 following a formal component transformation cycle 99 Profiling Data Discovery Technologies Analysis Data analysis software technologies deliver up to 10X productivity over manual approaches Based on a powerful computing technology that allows data engineers to quickly form candidate hypotheses with respect to the existing data structures Hypotheses are then presented to the SMEs (both business and technical) who confirm, refine, or deny them Allows existing data structures to be inferred at rate that is an order of magnitude more effective than previous manual approaches Pioneers include Evoke->CSI, Metagenix->Ascential->IBM, Sypherlink 100

Select an Attribute to"get a list of values Double-click a value to see rows with that value 101 Information Architecture Simplification Existing System 1 System 2 System 4 System 3 System 5 System 6 102

Information Architecture Simplification Existing System 1 New System-to-System Program Transformation Knowledge System 2 Transformations System 4 Transformations Data Store Transformations System 3 System 6 System 5 Transformations Generated Programs 103 Information Architecture Simplification Existing System 1 New System-to-System Program Transformation Knowledge System 2 System 3 Transformations Transformations Transformations Transformations Data Store Generated Programs System 6 104

101 Workshop: Necessary Pre-requisites 1. Adopting a crawl, walk, run strategy 2. Understanding current and potential organizational maturity and corresponding capabilities 3. Achieving an appropriate technology/human capability balance 4. Implementing useful IT systems development practices 5. Installing necessary non-it leadership 105 A likely state of your data Very Silo ed or conflicting data sources Multiple Data Sources Multiple changes to source system Inconsistent Data Quality Difficult to report and mine against Inconsistent data definitions of common terms Redundancy Lots of Data.Minimum Information IT are data owners 106

A Model Specifying Relationships Among Important Terms Data Intelligence Wisdom & knowledge are often used synonymously Data Information Use Data Data Data Request Fact Data Meaning Data 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 USES. 107 [Built on definition by Dan Appleton 1983] Application-Centric Development In support of strategy, the organization develops 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 that data is formed around the application and not the information requirements Process are narrowly formed around applications Very little data reuse is possible Strategy t Goals/Objectives t Systems/Applications t Network/Infrastructure Data/Information Original articulation from Doug Bagley @ Walmart 108

"The significant problems we face cannot be solved at the same level of thinking we were at when we created them." - Albert Einstein Einstein Quote 109 History Lesson "In the first decades of computing, the programs in a corporation became an unruly mess, far removed from the orderliness one would normally associate with an engineering discipline." James Martin 1981 110

Typical System Evolution Payroll Data (database) Payroll Application (3rd GL) Finance Data (indexed) Finance Application (3rd GL, batch system, no source) Marketing Data (external database) Marketing Application (4rd GL, query facilities, no reporting, very large) R & D Data (raw) Personnel App. (20 years old, un-normalized data) R& D Applications (researcher supported, no documentation) Personnel Data (database) Mfg. Data (home grown database) Mfg. Applications (contractor supported) 111 Typical System Evolution Finance Application (3rd GL, batch system, no source) Payroll Data (database) Payroll Application (3rd GL) Finance Data (indexed) Marketing Data (external database) Marketing Application (4rd GL, query facilities, no reporting, very large) Personnel Data (database) R & D Data (raw) Personnel App. (20 years old, un-normalized data) R& D Applications (researcher supported, no documentation) Mfg. Data (home grown database) Mfg. Applications (contractor supported) 112

How many interfaces are required to solve this integration problem? Application 1 Application 2 Application 3 15 Interfaces (N*(N-1))/2 Application 4 Application 5 Application 6 RBC: 200 applications - 4900 batch interfaces 113 The rapidly increasing cost of complexity 20000 N 6 / 15 60 / 1,770 600 / 179,700 200 / 19,900 200 / 5,000 (actual) Each interface is another system to be maintained 10000 0 Number of Silos Worst case number of interconnections 114 1 101 201

An organization's data architecture... Software Package 1 Software Package 2 Software Package 3 Data Architecture... maps between and across software packages Software Package 4 Software Package 5 Software Package 6 115 What do we teach knowledge workers about data? What percentage of the deal with it daily? 116

What do we teach IT professionals about data? 1 course How to build a new database 80% if UT expenses are used to improve existing IT assets What impressions do IT professionals get from this education? Data is a technical skill that is used to develop new databases This is not the best way to educate IT and business professionals - every organization's Sole, non-depletable, non-degrating, durable, strategic asset 117 Software/Database Architecture Foci Who makes decisions about the range and scope of common data usage? Application domain 3 Program G Program I Program H Application domain 2 Program D Program F Program E 118

Data Architecture Focus has Greater Potential Business Value Broader focus than either software architecture or database architecture Focus of a software architecture engineering effort Data Program A Data Analysis scope is on the system wide use of data Focus of a database architecture engineering effort Data Application domain 1 Program C Program B Data Data Problems caused by data exchange or interface problems Program G Data Program D Architectural goals more strategic than operational Application domain 3 Program I Program H Data Application domain 2 Program F Program E Data Data Data 119 Information Architecture Context Organization wide focus Requirement is to "understand" Understanding is of both current and future needs Making data and its use effective and efficient Data is used to support business activities Developing and maintaining standard data products and models Developing and maintaining an organizational data bank for storing and integrating organizational data assets; Encouraging the use of common procedures and tools; and Providing education, training, and consultation services 120

Designing for Evolution is Different than Creating New Systems Common Organizational Data (and corresponding data needs requirements) Future State Evolve (Version +1) Data evolution is separate from and external to the system development life cycle! Systems Development Activities Create New Organizational Capabilities 121 Data-Centric Development Flow 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 to support organizationwide use of data 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 Strategy t Goals/Objectives t Data/Information t Network/Infrastructure Systems/Applications Original articulation from Doug Bagley @ Walmart 122

Individual SDLC efforts make increasing use of IA Increasing scope and depth of information architecture utility Results Organized system metadata Organized system metadata Organized system metadata Requests Individual SDLC Effort Requests Requirements Design Results Requests Results Implement Individual SDLC Effort Requirements Over time the: Number of requests increase Utility of the results increase Design Amount of metadata contributed by new systems development increases Implement Requirements Individual SDLC Effort Design Implement 123 Data is not a Project Durable asset An asset that has a usable life more than one year Reasonable project deliverables 90 day increments Data evolution is measured in years Data Evolves - it is not created Significantly more stable Readymade data architectural components Prerequisite to agile development Only alternative is to create additional data siloes! 124

Niccolo Machiavelli (1469-1527) He who doesn t lay his foundations before hand, may by great abilities do so afterward...... although with great trouble to the architect and danger to the building. Machiavelli, Niccolo. The Prince. 19 Mar. 2004 http://pd.sparknotes.com/philosophy/prince 125 You cannot architect after implementation! 126

Architecture and Engineering are Symbiotic Architecture enables complex "things" to be built Engineering ensures a disciplined approach to development Engineering Architecture 127 Engineering 1.The application of scientific and mathematical principles to practical ends such as the design, construction, and operation of efficient and economical structures, equipment, and systems. 2.The profession of or the work performed by an engineer. 3.Deals almost entirely with measurable; using analytic tools derived from mathematics and the hard sciences 4.Engineering is a deductive process a conclusion follows necessarily from the stated premises; inference by reasoning from the general to the specific 5.Emphasis on quantifiable costs 6.Goal is technical optimization 7.Engineering is more of a science American Heritage Dictionary, Second College Edition, Boston: Houghton Mifflin Company, 1982 128

Applying Engineering Concepts It is tall It has a clutch It was built 1943-1945! It is still in regular use! USS Midway & Pancakes 129 Concrete Blocks & Engineering Continuity 130

The Role of Engineering/Architecture in Rapid Development 360 hours or 15 days of continuous building http://www.youtube.com/watch?v=hdpf-mqm9vy&feature=player_embedded#! 131 Development Standards 132

An underground garage was being dug on the south Side of the building, to a depth of 4.6 metres (15 ft). 133 The excavated dirt was being piled up on the north Side of the building, to a height of 10 metres (32 ft). 134

They dug right up to the base of the building. Then the rains came. 135 The building experienced uneven lateral pressure From north to south. 136

This resulted in a lateral pressure of 3,000 tonnes, which was greater than what the un-reinforced pilings could tolerate. Thus, the building toppled completely over in a southerly direction. 137 138

139 140

Notice that there's NO rebar in the pilings! Just some wire mesh. 141 142

143 In Conclusion They built 13 stories on grade, with no basement, And tied it all down to hollow pilings with no rebar 144

Why data structure problems have been difficult? 145 Student System Data Model 146

147 Proposed Model 101 Workshop: Necessary Pre-requisites 1. Adopting a crawl, walk, run strategy 2. Understanding current and potential organizational maturity and corresponding capabilities 3. Achieving an appropriate technology/human capability balance 4. Implementing useful IT systems development practices 5. Installing necessary non-it leadership 148

149 CIOs are generally unsuccessful at remaining data-focused Question: What is the hardest part of doing analysis? Answer: Not doing design! Question: What is the hardest part of a CIO's job? Answer: Remaining data focused! Anything the CIO does that is not...... applying organizational data assets to the implementation of organizational strategy...... is a similar distraction 150

Chief 151 C-level 152

C XESPOQR O 153 58 Commonly Used Chief Officer Titles Chief Accounting Officer, Chief Administrative Officer, Chief Analytics Officer, Chief Audit Officer, Chief Brand Officer, Chief Business Officer, Chief Channel Officer, Chief Commercial Officer, Chief Communications Officer, Chief Compliance Officer, Chief Creative Officer, Chief Data Officer, Chief Executive Officer, Chief Financial Officer, Chief Human Resources Officer, Chief Information Officer, Chief Information Security Officer, Chief Innovation Officer, Chief Investment Officer, Chief Immigration Officer, Chief Geospatial Information Officer, Chief Knowledge Officer, Chief Leadership Officer, Chief Learning Officer, Chief Legal Officer, Chief Marketing Officer, Chief Marketing Information Officer, Chief Medical Officer, Chief Merchandising Officer, Chief Networking Officer, Chief Operating Officer, Chief Process Officer, Chief Procurement Officer, Chief Product Officer, Chief Research Information Officer, Chief Risk Officer, Chief Science Officer, Chief Stores Officer, Chief Strategy Officer, Chief Technology Officer, Chief Visionary Officer, Chief Web Officer 154

The "Chief Officer" Title Chief The head or leader of an organized body of people; the person highest in authority: the chief of police Chief Financial Officer (CFO) Individual possessing the knowledge, skills, and abilities to be both the final authority and decision-maker in organizational financial matters Chief Risk Officer (CRO) Individual possessing the knowledge, skills, and abilities makes decisions and implements risk management Chief Medical Officer (CMO) Responsible for organizational medical matters. The organization, and the public, has similar expectations for any of chief officer especially after the Sarbanes-Oxley bill. [dictionary.com] 155 CIO Infrastructure Focus User Needs Connectivity Desktop Mobile Help Desk Back End Sys. Networking Telephony Data Centers Cloud Support Virtualization 156

Top Five CIO Concerns 2005-2011 0.800 0.600 0.400 0.200 0.000 IT/Information Security/Privacy Virtualization Data center/it efficiencies/cloud Social Media Improving people/leadership BI/analytics Standardization/consolidation IT workforce development IT governance Risk management Mobile applications/technologies Information Sharing Implementing plans/initatives/achieving results Acquisition/project mgt Process/system integration Strategic planning 2011 2010 2009 2008 2007 2006 2005 157 The Top Job Finance Operations Sale/Marketing HR Risk Information Technology/CIO Align IT initiatives with business goals Improving IT operations performance Cultivating the IT/business partnership Cost control/expense management Implementing new systems Leading change efforts Driving business innovation Redesigning business processes Developing and refining business strategy Negotiating with IT vendors Managing IT crises Developing market strategies & technologies Security management Studying trends to identify opportunities Where does data go?... data 158

CDO Reporting Top Job Top Information Technology Job Top Operations Job Chief Data Officer Top Finance Job Top Marketing Job Data Governance Organization There is enough work to justify the function There is not much talent The CDO provides significant input to the Top Information Technology Job 159 New division of labor Reporting to IT Data Development Database Operations Management Shared with the business Metadata Management Data Security Management Reporting to Business Data Architecture Management Reference & Master Data Management Data Warehousing & BI Management Document & Content Management Data Quality Management Data Governance 160

Chief Electrification Officer Not all roles are needed always! Chief Electrification Officer responsible for electrical generating and distribution systems. The title was used mainly in developed countries from the 1880s to 1940s during the electrification of industry, but is still used in some developing countries. 161 CDO Survey: What key traits are necessary to be a successful CDO? 100.0%$ 100.0%$ 50.0%$ 50.0%$ 93.8% 93.8% 85.8% 85.8% 82.3% 82.3% 82.3% 82.3% 74.3% 72.6% 74.3% 72.6% 66.4% 66.4% 54.9% 54.9% 45.1% 45.1% 38.9% 38.9% 29.2% 29.2% 0.0%$ 0.0%$ Possess a Outstanding balance Possess of a Outstanding relationship technical balance skills, of building relationship and technical business skills, communication building and knowledge business and communication skills knowledge people skills and skills people skills Politically Politically savvy savvy Leader and Leader visionary and visionary Team builder Team builder Understands Understand Can work with SME in Not too Entrepreneurial Understands privacy, data Understand core Can pure work IT and with requisite SME in technical Not too but Entrepreneurial privacy, security, data and information core pure information IT and business requisite side not technical a technical but security, risk and information domains of management information business as well as side not a novice technical management risk risk, domains industry of management specialists to methodologies as well as novice components management of risk, knowledge, industry specialists bridge the to methodologies and practices components data of product/service knowledge, information bridge the gap and needed practices to data product/service and customer information gap and customer needed effectively to effectively connect business connect requirements business requirements to IT to IT 162

73% of CDO Functions Are Less than 1 Year Old Does your CDO have a staff? Does your CDO have a budget? 163 CDO Success 1. Dedicated solely to data asset leveraging 2. Unconstrained by an IT project mindset 3. Reporting to the business 164

101 Workshop: Necessary Pre-requisites 1. Adopting a crawl, walk, run strategy 2. Understanding current and potential organizational maturity and corresponding capabilities 3. Achieving an appropriate technology/human capability balance 4. Implementing useful IT systems development practices 5. Installing necessary non-it leadership 165 Finish 166

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