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Why Won’t Managers Use My Data? Or: an invitation to become a decision engineer

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Dr. Lorien Pratt, Quantellia Chief Scientist, challenges you to advance your career by becoming a Decision Engineer. This emerging profession is a natural extension of business intelligence, and Dr. Pratt presents research to show that decision engineers are desperately needed. Learn how to design decisions, and some best practices as you help your organization and clients to gain that maximum value from data, "big data", databases, your expertise, and more.

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Why Won’t Managers Use My Data? Or: an invitation to become a decision engineer

  1. 1. #GMSQL
  2. 2. Why Won’t Managers Use My Data? Or: An Invitation to Become a Decision Engineer Dr. Lorien Pratt, Chief Scientist, Quantellia Mark Zangari, CEO, Quantellia
  3. 3. About Me• Based in Denver• Former college professor• Research focus: applied analytics/neural networks• Wrote Learning to Learn and a lot of articles• Ran market analyst team with Frost and Sullivan• Co-founded Quantellia in 2008• Chief Scientist • US Government spending • Community Justice Advisors analysis / Liberia
  4. 4. Agenda1. Decision Engineering: Research showing the importance of this need2. Research results for what’s needed to fill this need3. How to do it: key steps
  5. 5. Global research study:Q: What is the biggest problem that technology should be solving, that it is not?
  6. 6. Global research study:Q: What is the biggest problem that technology should be solving, that it is not? A: Decision making
  7. 7. Where all this greatdata could be used Where the data is actually used
  8. 8. Strong Demand for Better Use of Data "Better use of our data and analytics could produce substantially more value (cost savings and/or revenue growth) than it does today"Strongly Disagree Disagree Neutral Agree Strongly Agree 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
  9. 9. Ineffective Navigation Structure the Norm "We have an effective business navigation structure in place, where we make decisions, monitor their outcomes, then adjust decisions as needed to achieve our business goals"Strongly disagree Disagree Neutral Agree Strongly agree 0% 5% 10% 15% 20% 25% 30% 35%
  10. 10. Market Research Environment Pharmaceuticals Financial Services 2% 2% 2% Human Resources Nonprofit 2% 3% Manufacturing 3% Defense 6% Public Health 7% Media 10% Telecommunications Information 52% Technology 11%Source: Quantellia (2008) Number of samples = 61
  11. 11. Decision Making How carefully do organizations make decisions today? We have a All decisions are formal made in an ad methodology hoc manner and we generally 25% follow it 14% Approximately 86% of organizations do not consistently follow a formal methodology for ensuring sound decisions. We have a We follow an formal informal "rule of methodology thumb" but we do not methodology adhere to it very 32% closely or consistently 29%Source: Quantellia (2008). N = 28
  12. 12. So why don’t managers use my data?Because their most essential needs aren’t met
  13. 13. Wanted:Decision Engineers This can be you.
  14. 14. Decision making problems involve many business factors: especially communication, collaboration, and visualization What is Difficult in Your Organization About Making Decisions?Source: Quantellia (2008) N= 61
  15. 15. Decision makers have many needs that are not met by currentdecision support systems Qualitative plus quantitative data together Need to represent intangibles Organize information / Help with overload Iterative Methodology Social / Value Network Visibility Templates / pre-canned models and/or data Need for decision maker to tweak models themselves User Friendly Multiple bottom lines / objective functions High powered quantitative engine Handle uncertainty, e.g. by visualizing confidence levels Model Building Wizard Integrate with Excel KPI Identification / Dashboard Sensitivity Analysis Common Methodology for Visualization Include domain expertise Mine Unstructured Data Sources 0% 5% What features would be most valuable in 10% software that supports decision making? 15% Source: Quantellia (2008) N = 61
  16. 16. Systematic Decision Making Problems• “We focus on only one measure, when there are really multiple objectives.”• “We make decisions that assume a predictable unchanging future.”• “Our focus is on short-term goals, ignoring long-term ones.”• “We are unableReduce Time We Spend on long to reason about Reduced Knowledge of our cause-and-effect chains.” Customer Care Telephone Calls Customers• “We ignore intangibles like morale, reputation, trust, and brand. Brand Cost• “We plan for only a single future scenario Costs radically different Lower Customer Care when Unhappier Customers courses of action may be appropriate, depending on how the future unfolds.” Revenue “I can barely plan for next quarter, how can I think about the Community future, too?” Improved Contribution Margin Service “Five years from now, the market for our product will have grown by 30%” Worse Contribution Greater Customer Churn Margin
  17. 17. Decision Makers GAP
  18. 18. Decision Makers What will be the impact oftoday’s decision, tomorrow?
  19. 19. “What price should I charge for this product?”
  20. 20. “Is my money betterspent on moreservers or moreiPads?”
  21. 21. “Which buildings should Itransform to cloud/VOIP first, to maximize business benefit?
  22. 22. How can I design a new democracy tomeet the health and legal needs of rural populations, given limited funds?
  23. 23. What price should I charge for my new mobile service?
  24. 24. Decision Makers What will be the impact oftoday’s decision, tomorrow? Data
  25. 25. Q: So how can I get my data more widely used?
  26. 26. Q: So how can I get my data more widely used? A: Realize that a decision (likesoftware) can be engineered, andapply engineering principles to its creation and management
  27. 27. Analogies from HistoryWhat have we done in the pastwhen the complexity of aproblem eventually exceededour ability to manage it?Example: Construction.• Small structures require little planning, commit few resources, and have relatively few consequences if they fail.• As we try to build larger structures, we need more is needed.• There is a ceiling beyond which the complexity becomes too great.• An engineering discipline provides the organizational and communications tools that enable much larger structures to be reliably erected.
  28. 28. Decision making has reached its owncomplexity ceiling…
  29. 29. To overcome the complexity ceiling, weneed to create a structured paradigm fordecision making…We need Decision Engineering.
  30. 30. Previous times we’ve introduced visual engineering approaches Software Manufacturing Decision Making Increasing visualization / interactivity over time
  31. 31. “[It is essential] to visualize not just the data used tosupport decisions, but also the decisionsthemselves. [This is an] essential need in both thecommercial and nonprofit worlds.” -Lynn Langit, Developer Evangelist at Microsoft and author of the book Smart Business Intelligence Solutions with SQL Server 2008 Quantellia: Winner of the 2009 Microsoft Windows 7 Innovation Award
  32. 32. "In an age of global complexity, the time for makingdecisions is ever-shrinking, and the cost of bad choicestoo great to tolerate. Quantellia created a tool formaking the right decisions in this environment.” -Guy Pfeffermann, former Chief Economist of the International Finance Corporation (World Bank); Founder and CEO of the Global Business School Network (www.gbsnonline.org).
  33. 33. “Telecommunications companies, along with other businesses challengedby the rapid pace of a global environment, recognize the competitivevalue of applying Business Intelligence and analytic tools to the vaststores of data they generate. Visual, actionable decision engineeringsolutions are the next evolutionary step in BI, to help get at what decisionmakers need and how they think, rather than on what data managers canprovide.” - Susan McNeice, Vice President - Software Research, Yankee Group (www.yankeegroup.com)).
  34. 34. “Anyone facing complex decisions with many participants andstakeholders, mounds of data, and limited resources to addressthe decision-making process, should look closer at visualizationtools … Visualized decision support—decision engineering—isfast becoming a key part of effective business management.”-Karl Whitelock, Director Strategy – OSS/BSS, Stratecast, a Division of Frost and Sullivan (www.frost.com ).
  35. 35. What does all this mean in practice?  Some keys
  36. 36. To make the best use of data, youhave to start by setting all the data aside. Really.
  37. 37. Time for a blueprint for decisions
  38. 38.  Key Elements of a Decision Model External Factors: impact the outcome but over which we Decision Data have no control Examples: • Competitor price Levers External • Market demand f Factors Goals: targets against Decision Predictive analytics outcomes. Example: 5% margin growth in 2 years. Levers Decision levers: Factors over which we have control. f Analytics f Outcome Examples: Analytics #1 • Price of a product • Features of a product f • Investment in sales • Investment in marketing Intermediate Values Outcome • Investment in OSS f #2Dependencies: how one part ofthe model depends upon f Analytics fanother, through cause-and-effect or other flows. Intermediate Values: Facts OutcomeExamples: and values that areHow does MTTR respond to investment inCSR training? calculated along the way to #3 determining outcomesHow does brand respond to sales staffexpertise level? Examples: salesNote: these can be determined through volume, mean time to Outcomes: Measures of successtraditional analytics, staff expertise, or respond, sales expertise Examples: Margin, Brand, Shareindustry benchmarks level, fallout rate Price
  39. 39.  Understand time
  40. 40.  Understand how feedback loops end up dominating many systems
  41. 41. Demonstration #1: Carbon TaxProprietary and Confidential Not for Reproduction Without Permission of Quantellia Copyright © 2010, 2011 Quantellia Inc All rights reserved.
  42. 42. Understand that Situational Data + Decisions + Time = Outcomes
  43. 43. Use Human Intelligence(especially when data is imperfect)
  44. 44. Apply best practices of the engineering lifecycle Quality Assurance Objectives SecurityPlanning Phase Specification Design Alignment Implementation Execution & Phase Monitoring Change Management
  45. 45.  Beware the Whack-a-Mole “When I lower costs in one part of my business, it ends up creating bigger problems in another.”
  46. 46. My decision is only asgood as the data that supports it
  47. 47. My decision is only asgood as the data that supports it Not
  48. 48. Good Decisions from Imperfect DataHow: Since only 10% of the data impacts 90% of the decision, problems with the 90% matter much less. Know which is which Use sampling / statistical to extract excellent analytics from messy data Use human expertise when data is imperfect
  49. 49. Start with the decision maker, not the data
  50. 50.  Follow the decision value chain / connect the dots Customer Changes to Improvement Improvement demand curve: More revenue forexperience sell same product the same cost to a KPI to brandinvestment at a higher price  Keep asking why
  51. 51. Demonstration #2: Blue Jeans  Understand time
  52. 52. Decision vs. Operational Engineering Monitoring• Like automobile design • Like monitoring a working• Key competency: being able to vehicle understand how the system will • Key competency: detecting work problems accurately and quickly• Key competency: using • Key competency: diagnosis judgment where data is missing
  53. 53. Data Is a key element, becauseSituational Data + Decisions + Time = Outcomes
  54. 54. Decision Engineering is the NextGeneration of Business Intelligence Wanted: Decision Engineers. DecisionAn invitation: change the world.(or, just do the next cool thing) Engineeri Predictive Analytics ng Reporting/Business Intelligence Data Management
  55. 55. THANK YOU. Lorien.pratt@quantellia.com 303 589 7476 @LorienPrattPlease fill out the evaluation and turn it in to this session’s host. #GMSQL

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