11 Metrics And Productivity Issues


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11 Metrics And Productivity Issues

  1. 1. Metrics and Productivity Issues a presentation by Derek Kipps and Guy Langston
  2. 2. Seminar Goals <ul><li>Convey the importance of metrics </li></ul><ul><li>Provide insight </li></ul><ul><li>Make you think </li></ul><ul><li>We are not going to repeat the facts </li></ul><ul><li>We won’t provide any set answers </li></ul>
  3. 3. Agenda <ul><li>The IS Problem? </li></ul><ul><li>What we can do about it </li></ul><ul><li>Metrics </li></ul><ul><ul><li>core concepts </li></ul></ul><ul><ul><li>application & issues </li></ul></ul><ul><li>Productivity </li></ul><ul><li>Some Conclusions </li></ul>
  4. 4. Metrics are everywhere <ul><li>Metrics are the basis of the Scientific Method </li></ul><ul><li>Relevance to Information Systems: </li></ul><ul><ul><li>Systems development </li></ul></ul><ul><ul><li>Empirical research </li></ul></ul><ul><ul><li>Management </li></ul></ul>
  5. 5. Why all the IS fuss? <ul><li>Projects have a reputation for being late, over budget and of bad quality. </li></ul><ul><li>WHY? </li></ul><ul><li>We aren’t estimating properly </li></ul>TIME QUALITY COST
  6. 6. What can we do about it? <ul><li>Take metrics seriously </li></ul><ul><li>Use the right metrics for the right reasons and interpret them the right way </li></ul>Imagine a mechanic who has no way of determining the price of replacement parts, no idea of the labour estimate and no access to a service manual describing the logical order of steps required to complete the task. Would you take your car to this person?
  7. 7. What really happens... <ul><li>WAG </li></ul><ul><ul><li>used by inexperienced estimators </li></ul></ul><ul><li>SWAG </li></ul><ul><li>= (WAG x 2.85) R + 2cf </li></ul><ul><li>CYA </li></ul><ul><ul><li>used by estimators who are regulated </li></ul></ul>
  8. 8. Metric Toolbox Size Cost Quality Risk Schedule McFarlan Halstead Cyclomatic Complexity COCOMO SLIM Estimacs MSCM Estor Function Points Lines of Code Defects Reliability Maintainability WBM PERT Gantt
  9. 9. Metric Guidelines <ul><li>Metrics are dysfunctional </li></ul><ul><li>what makes a metric functional </li></ul><ul><ul><li>comparable vs documentative </li></ul></ul><ul><ul><li>objective vs subjective </li></ul></ul><ul><ul><li>empirical vs intuitive </li></ul></ul><ul><ul><li>practical vs theoretical </li></ul></ul><ul><li>Metrics need not be complex - it’s about the knowledge gained </li></ul>
  10. 10. Continuous Measurement Feasibility Analysis Development Implementation Maintenance Review Size Plan Estimate Schedule Constant Progress Reports Recording actual - estimate comparisons
  11. 11. Quantitative Management <ul><li>If it doesn’t fit into your strategic plan, don’t use it </li></ul><ul><li>Metrics are part of management and should deal with all aspects of business </li></ul>If you can’t measure it, you can’t manage it” (Lord Kelvin)
  12. 12. Bad Estimating...
  13. 14. Benchmarking <ul><li>A comparison of similar measurements in similar organisations: </li></ul><ul><ul><li>evaluating KSFs </li></ul></ul><ul><ul><li>comparing against market leaders </li></ul></ul><ul><ul><li>for determining best business practices </li></ul></ul>
  14. 15. Technical Metrics <ul><li>Database volumes </li></ul><ul><li>Transaction volumes </li></ul><ul><li>Server hits </li></ul><ul><li>Bandwidth </li></ul><ul><li>Logins per day </li></ul>
  15. 16. The ultimate metric: Defects <ul><li>Users want zero defects </li></ul><ul><li>Low defects represent high quality products </li></ul><ul><li>60-80% of SDLC spent on maintenance </li></ul><ul><li>HIGH MAINTENANCE = LOW PRODUCTIVITY </li></ul><ul><li>LOW MAINTENANCE = HIGH PRODUCTIVITY </li></ul>
  16. 17. Productivity <ul><li>Reuse </li></ul><ul><li>Human Factors </li></ul><ul><ul><li>mythical manmonth </li></ul></ul><ul><ul><li>team size </li></ul></ul><ul><ul><li>overtime & turnover rates </li></ul></ul><ul><li>Productivity Tools </li></ul><ul><ul><li>Wideband Delphi / JAD </li></ul></ul><ul><li>Productivity can be a skill / organisational ability </li></ul>
  17. 18. The maturity of metrics INITIAL REPEATABLE DEFINED MANAGED OPTIMISING No metrics used Facilitate continuous improvement Knowledge based estimates Metrics have mgmnt. impact Start applying metrics R I C H N E S S
  18. 19. Conclusions <ul><li>Metrics have been used in the wrong ways for the wrong reasons </li></ul><ul><li>Well defined metrics are needed to achieve IS potential and productivity </li></ul><ul><li>In IS, productivity, metrics and quality are closely linked. </li></ul>
  19. 20. The Big ( holistic) Picture <ul><li>Metrics provide powerful methods for managing projects efficiently and productively. </li></ul>