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Predictive Analytics - How to get stuff out of your Crystal Ball

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Everyone wants to leverage data.  The optimal implementation of analytics is an organization-wide set of capabilities.  These are called advantageous organizational analytic capabilities in that a clear ROI is demonstrable from these efforts.  Turns out that there are a number of prerequisites to advantageous organizational analytics.  These include:
Adopting a crawl, walk, run strategy
Understanding current and potential organizational maturity and corresponding capabilities
Achieving an appropriate technology/human capability balance
Implementing useful IT systems development practices
Installing necessary non-IT leadership
This webinar will explore these and other topics using examples drawn from DOD, healthcare researchers, and donation center operations.

Publicado en: Tecnología
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Predictive Analytics - How to get stuff out of your Crystal Ball

  1. 1. Presented by Steffani Burd, PhD & Peter Aiken, Ph.D. Predictive Analytics Getting Stuff from Your Crystal Ball Protect Your Data | Build Your Business Copyright 2013 by Data Blueprint Your Presenters Steffani Burd • PhD Columbia/
 Statistics • B.A. University of Chicago/ Specialization: Neurobiology and Behavioral Science • InfraGard, Secret Service Electronic Crimes Task Force, NYPD Auxiliary Police Officer • Founder, Ansec Group • Ernst & Young Consulting • Experienced Internationally/Fluent Chinese/Spanish • Cageless shark diving Peter Aiken • 30+ years data mgt. • Multiple Int. awards/recognition • Founding Director, 
 Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • Past, President, DAMA International (dama.org) • 9 books and dozens of articles • 500+ empirical practice descriptions • Multi-year immersions w/ organizations as diverse as US DoD, Nokia, Deutsche Bank, Wells Fargo, Walmart, and the Commonwealth of Virginia 6
  2. 2. Copyright 2013 by Data Blueprint Ordering Pizza in the Future 7 8Copyright 2016 by Data Blueprint Slide # Data Science The Sexiest Job of the 21st Century
  3. 3. What is a Data Scientist? 9Copyright 2016 by Data Blueprint Slide # Copyright 2013 by Data Blueprint 10
  4. 4. Data Scientist? 11Copyright 2016 by Data Blueprint Slide # Data Scientist? 12Copyright 2016 by Data Blueprint Slide #
  5. 5. Data Scientist? 13Copyright 2016 by Data Blueprint Slide # Data Scientist? 14Copyright 2016 by Data Blueprint Slide #
  6. 6. Data Scientist? 15Copyright 2016 by Data Blueprint Slide # Data Scientist? 16Copyright 2016 by Data Blueprint Slide #
  7. 7. Data Scientist? 17Copyright 2016 by Data Blueprint Slide # Data Scientist? 18Copyright 2016 by Data Blueprint Slide #
  8. 8. Customer 19Copyright 2016 by Data Blueprint Slide # Current Customer Ex-Custom er? Potential Customer VIP-Custom er? Data Scientist? 20Copyright 2016 by Data Blueprint Slide # Data science is a redundant term, since all science involves data; it's like saying, "book librarian."
 
 Eric Siegel, Ph.D., author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
  9. 9. PA in the Analytics World 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 C Four Analytic Problems C Source: Elder Research (www.datamininglabs.com). “The Ten Levels of Analytics
  10. 10. Four Categories of Modeling Technology C Source: Elder Research (www.datamininglabs.com). “The Ten Levels of Analytics Getting Stuff from Your Crystal Ball S Based on Tom Davenport’s “A predictive analytics primer” in Predictive Analytics in Practice from Harvard Business Review Insight Center, 2014
  11. 11. Copyright 2013 by Data Blueprint Maslow's Hierarchy of Needs 25 Data Management Practices Hierarchy 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) Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices 26Copyright 2016 by Data Blueprint Slide # Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities
  12. 12. 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. Copyright 2013 by Data Blueprint DMM Capability Maturity Model Levels Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts Performed (1) Managed (2) Our DM practices are defined and documented processes performed at the business unit level Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices Defined (3) Measured (4) We manage our data as a asset using advantageous data governance practices/structures 
 Optimized (5)
 DM is strategic organizational capability, most importantly we have a process for improving our DM capabilities 27 Development guidance Data Adminstration Support systems Asset recovery capability Development training 0 1 2 3 4 5 Client Industry Competition All Respondents Data Management Practices Assessment Challenge Challenge Challenge Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operations 28 Copyright 2016 by Data Blueprint
  13. 13. Copyright 2013 by Data Blueprint Industry Focused Results • 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" 29 Data Management Strategy Data Governance Platform & Architecture Data Quality Data Operations Focus: Implementation and Access Focus: Guidance and Facilitation Optimized(V)
 Measured(IV)
 Defined(III)
 Managed(II)
 Initial(I) 1 2 3 4 5 DataProgramCoordination OrganizationalDataIntegration DataStewardship DataDevelopment DataSupportOperations 2007 Maturity Levels 2012 Maturity Levels Comparison of DM Maturity 2007-2012 30 Copyright 2016 by Data Blueprint
  14. 14. “Good” Data Analytic
 Projects Data Program Coordination Organizational Data Integration Data Stewardship Data Development Data Support Operation Initial (I) Repeatable (II) Documented (III) Managed (IV) Optimizing (V) Foundational Strategies Data ROT DM Practices Processes CMM/CMMI Data-centric 
 Development Flow S “Appropriate” Statistical Analyses Regression Techniques Hypothesis-driven, IVs and DVs, correlations, error Linear regression, Discrete choice models, Logistic regression, Multinomial logistic regression, Probit regression, Time series models, Survival or duration analysis, CART, Multivariate adaptive regression splines Machine Learning Techniques Exploratory, emerging variables, scope and purpose Neural networks, MLP, Radial basis functions, Support vector machines, k-means cluster, Naïve Bayes, Geospatial predictive modeling S
  15. 15. “Valid” Assumptions Consider Future and past Timeframes Key variables Missing data Consequences Model Application Additional and less Documented S Don’t let this be you! The Future of Predictive Analytics Applications Industries Problems Solutions Technologies Automation Processing Disruptive F Parallel Evolution?
  16. 16. Achieving Your Goals - Checklist Data Source (what, when, where, how, why) Cleaning, Missing data, Outliers, Variables Generalizability to population Statistics Rationale and Implementation Assumptions List and description Implications if not valid (individual, combination) Conditions would make assumptions not valid Variables could include/remove F Derived from Tom Davenport’s “A predictive analytics primer” in Predictive Analytics in Practice from HBR Insight Center, 2014 Achieving Your Goals (cont’d) Data Analytic Factors Implementation Strategies Repeatable & Scalable Solutions Organizational Factors Governance Models Aligning Data and IT Chief Data Officer Success Factors SLOTS Last is First F Success?Success!
  17. 17. Steffani Burd, Ph.D. sburd@ansecgroup.com 917.783.8496 Resources 5 DAMA KDnuggets Society for Design and Process Sciences Presidion HIMMS – Analytics Peter Aiken, Ph.D. paiken@datablueprint.com 804.382.5957 F To Err Is Human (Institute of Medicine, Nov 1999) The Price of Excess (PwC, 2011) USA, Inc. (Mary Meeker – KPCB, Feb 2011) Best Care at Lower Cost (Inst of Medicine, Sept 2012) Bitter Pill (Steven Brill, Feb 2013) Additional Resources
  18. 18. Data Strategy October 11, 2016 @ 2:00 PM ET/11:00 AM PT
 with Micheline Casey Sign up here: www.datablueprint.com/webinar-schedule or www.dataversity.net Copyright 2013 by Data Blueprint 39 Upcoming Events Copyright 2013 by Data Blueprint Questions? + = 40
  19. 19. Backup Slides Health Care IT Failures
  20. 20. “Within three years, there won’t be a Fortune 500 company without a CDO…” Futurist David Houle The Chief Data Officer Source: LinkedIn July 2013, Analysis of ten pages * “Healthcare” companies: Pharmaceuticals, Online Media, IT&S specializing in HC, Health Insurance Plan * Capability Maturity Model
  21. 21. Results: It Is Not Always About Money Solution Integrate multiple databases into one to create holistic view of data Automation of manual process Results Safe matches increased from 3 out of 10 to 6 out of 10 Turnaround time for matching patients with potential donor significantly reduced Data is passed safely and effectively Inconsistencies, redundancies, corruption reduced Ability to cross-analyze enhanced Diabetes Management Facilitators ▪ Secure Access with Consent ▪ Direct Secure Messaging (DSM) ▪ State and Federal, DOH ▪ Insurance Data Inputs ▪ PHR ▪ Home Monitoring ▪ Telehealth ▪ Office Visits ▪ Hospital Visits ▪ Diagnostics ▪ Lab Work ▪ Images/X-Ray Reports Treatment ▪ Home Healthcare / Long term Care ▪ Medications ▪ Behavioral Changes 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 Diabetic’s Circle of Care
  22. 22. Hemophilia Management 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 BioMarin Licenses Factor VIII Gene Therapy Program for Hemophilia Novel Gene Therapy Approach to Hemophilia B Sangamo BioSciences Receives $6.4 Million 
 Strategic Partnership Award From 
 California Institute for Regenerative Medicine to Develop ZFP Therapeutic® Treating Hemophilia in the 2010s Data Warehousing Courtesy of: http://www.infosys.com/industries/healthcare/industryofferings/Pages/healthcare 
 -data-warehousing.aspx
  23. 23. Big Data 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

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