1 / 25

Data Reliability in FRMS – Concepts and Methodology

Data Reliability in FRMS – Concepts and Methodology. Mat Jackmond, President, The Rules Guys Inc. Discussion of DATA (NFIRS and EMS DATA): Data is more important in these lean financial times. -------------------------------------------------------------------------

benson
Download Presentation

Data Reliability in FRMS – Concepts and Methodology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Reliability in FRMS – Concepts and Methodology Mat Jackmond, President, The Rules Guys Inc.

  2. Discussion of DATA (NFIRS and EMS DATA): Data is more important in these lean financial times. ------------------------------------------------------------------------- What is good DATA and how DATA is (and can be) used.  What programs are available that can use the data we all Capture on a daily basis?  Why (and HOW) to Capture good DATA. Q&A Session Introduction

  3. Introduction Mat Jackmond FF / Network Administrator experience (18+ years): • Fire & EMS data management & analysis • RescueNetFireRMS application & database • Network Administration • Microsoft SQL Server (ver. 7 / 2000 / 2005 / 2008) Areas of Direct Fire Department Experience (27 years): • EMT & Advanced Life Support Paramedic • Firefighter / Driver-Operator / Acting LT • Apparatus Maintenance & Mechanics Additional Experience: • Senior Software Implementation Specialist • Sunpro SMS-CAD Liaison • Independent Consultant, Advanced Sunpro SMS • Licenses & Degrees • Bachelors of Science in Information Technology • Associate of Arts in Fire Science • MICU Paramedic License (’79 – ’83)

  4. Introduction Data modification Outliers Accuracy Accessibility Timeliness normal distribution Curve-Fitting relevance Imputation logically consistent

  5. DATA (NFIRS and EMS DATA) • NFIRS, AdvancedEMS & NEMSIS Data • With all this DATA, how much is enoughvs.too much • (# control Fields is an estimate using an average of 5 Fields per Table as “keys” or “db-controls”)

  6. DATA (NFIRS and EMS DATA) “how much is enoughvs.too much” - To answer that, you have to ask: What do I need the data for? (see URL Link at bottom of page) • Annual Report and Basic Statistical Info • National • NFPA 1710/1720 • NFIRS Reporting (Grants) • State • Fire Marshall Reports (fireworks, etc.) • Workman’s Comp • County • County EMS Data • Local • Budgeting, Billing, “justifying our existence” • Litigation • Your records are “discoverable public records” ( see Cobb County video ) http://firedatamanagement.com/Sample%20docs/Uses%20of%20NFIRS.ppt

  7. DATA (NFIRS and EMS DATA) • The “Basic Rules” for getting DATA Out ARE: • You can ONLY Get OUT what you PUT IN… • (pretty obvious) • If you only put in what is RED, you’ll only get the minimum • Garbage IN = Garbage OUT • The DATA needs to correlate with the “Narrative” • The Report needs to make sense

  8. DATA (NFIRS and EMS DATA) • Statistical Data versus Usable Data: • Statistical Data … • An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs.(From Wikipedia, the free encyclopedia) • “Second, we can remove the detected outliers, and then produce estimates of average, standard deviation, and confidence intervals using the data without outliers.” (Tukey’s Outlier Filter - See David Hoaglin, Frederick Mosteller, and John Tukey (editors), Understanding Robust and Exploratory Data Analysis, New York, John Wiley & Sons, 1983, pp. 39, 54, 62, 223.) • Unless it can be ascertained that the deviation is not significant, it is ill-advised to ignore the presence of outliers.Outliers that cannot be readily explained demand special attention – see kurtosis risk and black swan theory. • Usable Data … • To get usable data, Quality Assurance must be done on the Data • Data with ERRORS makes any Reports on that Data “suspect”

  9. DATA (NFIRS and EMS DATA) • Statistical Data versus Usable Data: • Why do we discuss this? • Do you use 3rd Party Reporting Tools? • MyFireRules Metrics http://www.myfirerules.com/metrics_eval • CAD Analyst http://www.deccanintl.com/cad_overview.asp • NFIRS 5 Alive http://www.nfirs5.com/ • How do they deal with “outliers”? • Do you get to decide which “outliers” are Removed and which are left in the Report Data? • OR Do they automatically remove “outliers” based on a “formula”?

  10. DATA (NFIRS and EMS DATA) How can a Third Party Tool help you see errors in your Data, so you can fix it?

  11. DATA (NFIRS and EMS DATA)

  12. DATA (NFIRS and EMS DATA)

  13. DATA (NFIRS and EMS DATA)

  14. DATA (NFIRS and EMS DATA)

  15. DATA (NFIRS and EMS DATA)

  16. DATA (NFIRS and EMS DATA)

  17. DATA (NFIRS and EMS DATA)

  18. DATA (NFIRS and EMS DATA)

  19. DATA (NFIRS and EMS DATA)

  20. DATA (NFIRS and EMS DATA) Is there a Third Party Tool that will help your users to identify the errors in your Data and require them to fix it?

  21. DATA (NFIRS and EMS DATA)

  22. DATA (NFIRS and EMS DATA)

  23. DATA (NFIRS and EMS DATA)

  24. DATA (NFIRS and EMS DATA) We hope we have brought you some valuable insights into FireRMS Data Reliability Concepts and Methodology. ----------------------------------------------- Please download the 45-Day FREE Demo of our “Pivot Tables-In-A-Box” at http://www.myfirerules.com/metrics_eval

  25. DATA (NFIRS and EMS DATA) To see how Data can go bad please download the “Cobb County Mock Trial Video” at ftp://mfrftp.myfirerules.com/videos

More Related