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Next Generation Analytics: Overcoming the 8 Key Challenges to

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How can military leaders determine the status of a complex organization in a concise, accurate and meaningful way? Next generation analytical tools offer new approaches and opportunities to uncover data relationships that were never before possible. However, unless they can fundamentally address key challenges in data normalization they will fail to create actionable business intelligence for the enterprise and could harm DOD leaders ability to make the best strategic and operational decisions. This session will explore best practices in data normalization and how to utilize next generation analytic tools to create meaningful intelligence for the enterprise.

Jiro Akiyama, Director, Strategy and Organizational Development, Paragon Technology Group

Veröffentlicht in: Technologie, Business
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Next Generation Analytics: Overcoming the 8 Key Challenges to

  1. 1. Next Generation Analytics: Overcoming the8 Key Challenges to Data NormalizationJiro AkiyamaDirector, Paragon Technology GroupTechNet Mid-America 2012
  2. 2. Agenda • Defining the Problem • 8 Key Challenges to Data Normalization • Data Normalization Methodology • Recap & Resources
  3. 3. Defining the ProblemKey Performance Measuresare essential to supportleadership decision makingData Risks• Inaccurate data• Imprecise data• Misunderstood data• Misleading dataResult in poor decisions ordetrimental actions that leadto adverse consequences.
  4. 4. Why is Data Normalization Difficult?• Growing data complexity• Increased need for nearreal-time decisions• Disparate data sources• Complexity multiplies when datais aggregated
  5. 5. Data Normalization Challenges
  6. 6. Data Normalization: Issue #1 Metrics with Differing Units of Measure Measures Score Cost Savings $12.3 mil Employee Satisfaction 98% Number of Errors 20 Time to Respond 59 Seconds
  7. 7. Data Normalization: Issue #1 Metrics with Differing Units of Measure Normalized Measures Score Score Cost Savings $12.3 mil 7.5 Employee Satisfaction 98% 9.4 Number of Errors 20 6.0 Time to Respond 59 Seconds 2.9
  8. 8. Data Normalization: Issue #2 Non-linear Metrics Converted to a Linear scale Measures Score Normalized Customer Satisfaction 95% 9.5 Transactional Accuracy 95% 9.5
  9. 9. Data Normalization: Issue #3 Differing Control Boundaries Control Example Measure Score Boundary Upper Turnaround Time 2 Days Lower Systems Accessibility 99.9% Uptime Channel Variance to Budget ± 5%
  10. 10. Data Normalization: Issue #4Differing Logical Minimum and Maximum Amounts Measures Logical Logical Minimum Maximum Customer Satisfaction 0% 100% Number of Errors 0 ?
  11. 11. Data Normalization: Issue #5 Unbounded Maximums or MinimumsExample: Number of Complaints 6-100-5 Complaints =Range for Green Complaint s = Range 11 - ?? Complaints = Range for Red for Yellow
  12. 12. Data Normalization: Issue #6 Composite Measures Cost Variance: Green ≤ ±5%, ±5% ≤ Yellow ≤ ±10% Red ≥ ±10%Straight Average of Actual ScoreScore for Project #1 = 1%Score for Project #2 = -10.1% Average Normalized ScoresComposite Avg. Score = -4.55% Score for Project #1 = 9.33 Score for Project #2 = 3.31Average of Green, Yellow, Red(Green = 3 points, Yellow = 2 points, Red = 1 Composite Avg. Score = 6.32point)Score for Project #1 = 3Score for Project #2 = 1Composite Avg. Score = 2
  13. 13. Data Normalization: Issue #7 Comparability Over Time
  14. 14. Data Normalization: Issue #8Fidelity into the Thin Ranges
  15. 15. Summary Issue #1: Metrics with Differing Units of Measure Issue #2: Non-linear Metrics Converted to a Linear Scale Issue #3: Differing Control Boundaries Issue #4: Differing Logical Minimum and Maximum Amounts. Issue #5: Unbounded Maximums or Minimums Issue #6: Composite Measures Issue #7: Comparability Over Time Issue #8: Fidelity into Thin Ranges
  16. 16. Additional Resources Paragon Insights Blog (www.paragontech.net) Contact Info: Jiro Akiyama P 301-792-0483 jakiyama@paragontech.net