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© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License
CMMI® - Explored
HM’s Fou...
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CMMI and CMM are regis...
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 This presentation cove...
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 This presentation cove...
5© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 The Fourteen essential...
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 The Fourteen essential...
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 We often like to think...
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 We often like to think...
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 We often like to think...
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 We often like to thin...
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 We often like to thin...
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 Another simplistic as...
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 Another simplistic as...
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 Another simplistic as...
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 Since change is const...
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 Since change is const...
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 Since change is const...
18© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Since change is const...
19© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Since change is const...
20© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Human beings (and ins...
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 Human beings (and ins...
22© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Human beings (and ins...
23© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Human beings (and ins...
24© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Let us do a quick exe...
25© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Let us do a quick exe...
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 Let us do a quick exe...
27© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Let us do a quick exe...
28© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 Let us do a quick exe...
29© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 We (including statist...
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 We (including statist...
31© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 We (including statist...
32© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 We (including statist...
33© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 We (including statist...
34© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
 If I prepare a chart ...
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 If I prepare a chart ...
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 If I prepare a chart ...
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 Competence makes a si...
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 Competence makes a si...
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 Competence makes a si...
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 Often, decision maker...
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 Often, decision maker...
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 Often, decision maker...
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 Processes do not perf...
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 Processes do not perf...
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 Processes do not perf...
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11. Correlation is not ...
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11. Correlation is not ...
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11. Correlation is not ...
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12. Process instability...
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12. Process instability...
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12. Process instability...
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12. Process instability...
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13. Control of critical...
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13. Control of critical...
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13. Control of critical...
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13. Control of critical...
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14. Simulation may be t...
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14. Simulation may be t...
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14. Simulation may be t...
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14. Simulation may be t...
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14. Simulation may be t...
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14. Simulation may be t...
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1. Nothing is definite
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CMMI Explored - HM’s Fourteen: Essential Beliefs for Effective High Maturity Implementation

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This presentation covers fourteen essential beliefs that need to be internalized to implement CMMI® high-maturity practices effectively

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CMMI Explored - HM’s Fourteen: Essential Beliefs for Effective High Maturity Implementation

  1. 1. AlignMentor © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License CMMI® - Explored HM’s Fourteen: Essential Beliefs for Effective High Maturity Implementation 1
  2. 2. 2© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor ® CMMI and CMM are registered trademarks of Carnegie Mellon University © Rajesh Naik, 2013 This work is licensed under a Creative Commons Attribution 3.0 Unported License. This means you are free (1) to copy, distribute, display, and perform the work, (2) to make derivative works, and (3) to make commercial use of the work so long as you give proper attribution to the author and retain the license notice. If you create derivative works using this work, they should also be made available under a similar license. For further information go to http://creativecommons.org/licenses/by/3.0/ For uses outside the scope of the license, contact Rajesh Naik at naik.rajeshnaik@gmail.com
  3. 3. 3© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  This presentation covers fourteen essential beliefs that need to be internalized to implement CMMI® high-maturity practices effectively Contents of this Presentation ® CMMI and CMM are registered trademarks of Carnegie Mellon University
  4. 4. 4© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  This presentation covers fourteen essential beliefs that need to be internalized to implement CMMI® high-maturity practices effectively  The material in this presentation is derived from the following documents: – CMMI® for Services, Version 1.3 (CMU/SEI-2010-TR-034) – CMMI ® for Development, Version 1.3 (CMU/SEI-2010-TR-033)  The viewer should refer to the above documents, for the definitive requirements of High Maturity in CMMI® Contents of this Presentation ® CMMI and CMM are registered trademarks of Carnegie Mellon University
  5. 5. 5© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  The Fourteen essential beliefs are 1. Nothing is definite 2. Everything is interrelated 3. Nothing is permanent 4. Nobody likes variation 5. Statistics is non-intuitive 6. There are no bell-curves in real life 7. Outliers cannot be wished away 8. Skills impacts process performance 9. Statisticians should not be decision makers 10. Process performance cannot be decreed 11. Correlation is not the same as cause-effect 12. Process instability must be analyzed in real-time 13. Control of critical sub-processes impacts higher level performance 14. Simulation may be the only practical method to model reality today HM’s Fourteen
  6. 6. 6© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  The Fourteen essential beliefs are 1. Nothing is definite 2. Everything is interrelated 3. Nothing is permanent 4. Nobody likes variation 5. Statistics is non-intuitive 6. There are no bell-curves in real life 7. Outliers cannot be wished away 8. Skills impacts process performance 9. Statisticians should not be decision makers 10. Process performance cannot be decreed 11. Correlation is not the same as cause-effect 12. Process instability must be analyzed in real-time 13. Control of critical sub-processes impacts higher level performance 14. Simulation may be the only practical method to model reality today HM’s Fourteen We cover each of these beliefs in detail now…
  7. 7. 7© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We often like to think of outcomes as definite, single numbers 1. Nothing is definite 21
  8. 8. 8© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We often like to think of outcomes as definite, single numbers – But that is low maturity thinking, and does not reflect reality 1. Nothing is definite 21
  9. 9. 9© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We often like to think of outcomes as definite, single numbers – But that is low maturity thinking, and does not reflect reality  In reality, all activities have an inherent variation and what we can predict is only a probability 1. Nothing is definite 21
  10. 10. 10© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We often like to think of outcomes as definite, single numbers – But that is low maturity thinking, and does not reflect reality  In reality, all activities have an inherent variation and what we can predict is only a probability  In HM thinking, estimates or target dates are values that have a certain chance (probability) of being achieved 1. Nothing is definite 21 21 +/-
  11. 11. 11© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We often like to think of outcomes as definite, single numbers – But that is low maturity thinking, and does not reflect reality  In reality, all activities have an inherent variation and what we can predict is only a probability  In HM thinking, estimates or target dates are values that have a certain chance (probability) of being achieved – Only when we attach probabilities to targets/ goals, can we start looking at ways to increase those probabilities 1. Nothing is definite 21 21 +/-
  12. 12. 12© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Another simplistic assumption (low- maturity thinking) is that each goal/ objective of a project/ service is independent of other goals 2. Everything is interrelated Cost Timeliness Quality Other Goals
  13. 13. 13© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Another simplistic assumption (low- maturity thinking) is that each goal/ objective of a project/ service is independent of other goals  This gives rise to naïve decisions - E.g., we think that by adding more resources we can bring forward the deadline, expecting the cost and quality to remain the same 2. Everything is interrelated Cost Timeliness Quality Other Goals
  14. 14. 14© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Another simplistic assumption (low- maturity thinking) is that each goal/ objective of a project/ service is independent of other goals  This gives rise to naïve decisions - E.g., we think that by adding more resources we can bring forward the deadline, expecting the cost and quality to remain the same  In real situations, everything is interrelated, and HM is about understanding these relationships and taking more informed decisions 2. Everything is interrelated Cost Timeliness Quality Other Goals Cost Timeliness Quality Other Goals
  15. 15. 15© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Since change is constant, past performance cannot be blindly used for predicting the future 3. Nothing is permanent
  16. 16. 16© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Since change is constant, past performance cannot be blindly used for predicting the future – Past performance can only be taken as one of the inputs for future performance 3. Nothing is permanent
  17. 17. 17© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Since change is constant, past performance cannot be blindly used for predicting the future – Past performance can only be taken as one of the inputs for future performance  This needs to be combined with expected change in performance due to evolving technology, familiarity, skills, and risks 3. Nothing is permanent
  18. 18. 18© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Since change is constant, past performance cannot be blindly used for predicting the future – Past performance can only be taken as one of the inputs for future performance  This needs to be combined with expected change in performance due to evolving technology, familiarity, skills, and risks  While forecasting the future, we also need to predict the impacts of our potential actions, to take informed decisions 3. Nothing is permanent
  19. 19. 19© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Since change is constant, past performance cannot be blindly used for predicting the future – Past performance can only be taken as one of the inputs for future performance  This needs to be combined with expected change in performance due to evolving technology, familiarity, skills, and risks  While forecasting the future, we also need to predict the impacts of our potential actions, to take informed decisions  (All this in a probabilistic, interrelated way ) 3. Nothing is permanent
  20. 20. 20© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Human beings (and institutions run by human beings) like consistency, and dislike surprises/ variations 4. Nobody likes variation
  21. 21. 21© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Human beings (and institutions run by human beings) like consistency, and dislike surprises/ variations – Think of what happens when the summer is hotter or cooler than what has been in the past – Why do people visit fast food chains? – not for the great taste or nutritional value; but just for the consistency of experience 4. Nobody likes variation
  22. 22. 22© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Human beings (and institutions run by human beings) like consistency, and dislike surprises/ variations – Think of what happens when the summer is hotter or cooler than what has been in the past – Why do people visit fast food chains? – not for the great taste or nutritional value; but just for the consistency of experience  Our customers, employees, and vendors also expect minimal variations from us 4. Nobody likes variation
  23. 23. 23© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Human beings (and institutions run by human beings) like consistency, and dislike surprises/ variations – Think of what happens when the summer is hotter or cooler than what has been in the past – Why do people visit fast food chains? – not for the great taste or nutritional value; but just for the consistency of experience  Our customers, employees, and vendors also expect minimal variations from us  So, a key HM principle is to identify and reduce variation, wherever it is unacceptable 4. Nobody likes variation
  24. 24. 24© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Let us do a quick exercise 5. Statistics is non-intuitive
  25. 25. 25© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Let us do a quick exercise  If you throw a dice, what are the possible outcomes? – An integer between 1 and 6 (both included), with a probability of 1/6 for each result 5. Statistics is non-intuitive
  26. 26. 26© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Let us do a quick exercise  If you throw a dice, what are the possible outcomes? – An integer between 1 and 6 (both included), with a probability of 1/6 for each result  If you throw the dice twice, and add the two results, what are the possible outcomes? 5. Statistics is non-intuitive +
  27. 27. 27© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Let us do a quick exercise  If you throw a dice, what are the possible outcomes? – An integer between 1 and 6 (both included), with a probability of 1/6 for each result  If you throw the dice twice, and add the two results, what are the possible outcomes?  Without adequate time, most people come up with the wrong answer  The right answer is – An integer between 2 and 12 (both included), with different probabilities for each result – see chart on the left 5. Statistics is non-intuitive + 0 0.05 0.1 0.15 0.2 1 2 3 4 5 6 7 8 9 101112
  28. 28. 28© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Let us do a quick exercise  If you throw a dice, what are the possible outcomes? – An integer between 1 and 6 (both included), with a probability of 1/6 for each result  If you throw the dice twice, and add the two results, what are the possible outcomes?  Without adequate time, most people come up with the wrong answer  The right answer is – An integer between 2 and 12 (both included), with different probabilities for each result – see chart on the left  When we combine distributions, our gut-feel falters  So, we need to start using stats to understand and predict performance (and keep our guts aside ) 5. Statistics is non-intuitive + 0 0.05 0.1 0.15 0.2 1 2 3 4 5 6 7 8 9 101112
  29. 29. 29© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We (including statisticians) would like to fit the world into neat, symmetrical bell-curves 6. There are no bell-curves in real life
  30. 30. 30© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We (including statisticians) would like to fit the world into neat, symmetrical bell-curves  But human activity does not typically result in neat bell curves 6. There are no bell-curves in real life
  31. 31. 31© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We (including statisticians) would like to fit the world into neat, symmetrical bell-curves  But human activity does not typically result in neat bell curves  E.g., the air travel from Bangalore to Delhi takes typically 2:40 hrs. On a real good day it can take 2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30 hrs (or till they run out of fuel!). So, there is no symmetry around the typical time 6. There are no bell-curves in real life
  32. 32. 32© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We (including statisticians) would like to fit the world into neat, symmetrical bell-curves  But human activity does not typically result in neat bell curves  E.g., the air travel from Bangalore to Delhi takes typically 2:40 hrs. On a real good day it can take 2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30 hrs (or till they run out of fuel!). So, there is no symmetry around the typical time  In other words – “There is a limit to how well you can do; but no limit to how badly you can screw up ” 6. There are no bell-curves in real life
  33. 33. 33© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  We (including statisticians) would like to fit the world into neat, symmetrical bell-curves  But human activity does not typically result in neat bell curves  E.g., the air travel from Bangalore to Delhi takes typically 2:40 hrs. On a real good day it can take 2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30 hrs (or till they run out of fuel!). So, there is no symmetry around the typical time  In other words – “There is a limit to how well you can do; but no limit to how badly you can screw up ”  HM organizations will not blindly assume smooth, neat distributions for their plans and estimates 6. There are no bell-curves in real life
  34. 34. 34© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  If I prepare a chart of the flight time taken for all my trips from Bangalore to Delhi over the past 10 years, the chart is odd; there are times the plane diverted to Jaipur till the fog/ dust/ smog cleared 7. Outliers cannot be wished away Fog diversions
  35. 35. 35© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  If I prepare a chart of the flight time taken for all my trips from Bangalore to Delhi over the past 10 years, the chart is odd; there are times the plane diverted to Jaipur till the fog/ dust/ smog cleared  I cannot ignore these events just because they make it difficult to handle the math/ stats – Unless, climate-change eliminates fog in Delhi altogether  7. Outliers cannot be wished away Fog diversions
  36. 36. 36© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  If I prepare a chart of the flight time taken for all my trips from Bangalore to Delhi over the past 10 years, the chart is odd; there are times the plane diverted to Jaipur till the fog/ dust/ smog cleared  I cannot ignore these events just because they make it difficult to handle the math/ stats – Unless, climate-change eliminates fog in Delhi altogether   So, in HM thinking, outliers are a part of the process (with low probability), unless the root-cause is eliminated 7. Outliers cannot be wished away Fog diversions
  37. 37. 37© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Competence makes a significant difference to performance (speed, quality, throughput) 8. Skills impacts process performance
  38. 38. 38© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Competence makes a significant difference to performance (speed, quality, throughput)  That is why managers fight to get the right people in the their teams – “Processes make us people independent” (who said that?) - is misunderstood and misused 8. Skills impacts process performance
  39. 39. 39© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Competence makes a significant difference to performance (speed, quality, throughput)  That is why managers fight to get the right people in the their teams – “Processes make us people independent” (who said that?) - is misunderstood and misused  In HM organizations, estimates, plans, and forecasts consider the skills of the people doing the work – Process performance baselines also factor the skill levels 8. Skills impacts process performance
  40. 40. 40© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Often, decision makers get over-whelmed (and terrified) by analytics and abdicate decision making to statisticians 9. Statisticians should not be decision makers
  41. 41. 41© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Often, decision makers get over-whelmed (and terrified) by analytics and abdicate decision making to statisticians  Statisticians can analyze data and prove or disprove some hypothesis, but the decision making still rests with the managers and executives 9. Statisticians should not be decision makers
  42. 42. 42© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Often, decision makers get over-whelmed (and terrified) by analytics and abdicate decision making to statisticians  Statisticians can analyze data and prove or disprove some hypothesis, but the decision making still rests with the managers and executives  In HM organizations, managers/ executives make effort to understand enough stats/ analytics to remain on top of decision making 9. Statisticians should not be decision makers
  43. 43. 43© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Processes do not perform differently just because executive management “decrees” a certain performance 10. Process performance cannot be decreed
  44. 44. 44© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Processes do not perform differently just because executive management “decrees” a certain performance  In low maturity organizations, the distinction between performance baseline (actual performance) and performance target (desired performance) is not clear – and desired performance is used for estimation and planning 10. Process performance cannot be decreed
  45. 45. 45© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Processes do not perform differently just because executive management “decrees” a certain performance  In low maturity organizations, the distinction between performance baseline (actual performance) and performance target (desired performance) is not clear – and desired performance is used for estimation and planning  In true HM organizations, baselines are not dictated by management, but derived from past performance. And management sets process performance targets (for driving process changes) 10. Process performance cannot be decreed
  46. 46. 46© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 11. Correlation is not the same as cause- effect • Here is an example: – One may be able to find a good correlation between the number of people carrying umbrellas/ raincoats to work in the morning, and whether it rained during the day – But that does not mean that carrying umbrellas and raincoats causes rainfall
  47. 47. 47© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 11. Correlation is not the same as cause- effect • Here is an example: – One may be able to find a good correlation between the number of people carrying umbrellas/ raincoats to work in the morning, and whether it rained during the day – But that does not mean that carrying umbrellas and raincoats causes rainfall • Correlations can be established statistically, but cause-effect is based on logical thinking and requires domain knowledge (which is why statisticians cannot be the decision makers )
  48. 48. 48© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 11. Correlation is not the same as cause- effect • Here is an example: – One may be able to find a good correlation between the number of people carrying umbrellas/ raincoats to work in the morning, and whether it rained during the day – But that does not mean that carrying umbrellas and raincoats causes rainfall • Correlations can be established statistically, but cause-effect is based on logical thinking and requires domain knowledge (which is why statisticians cannot be the decision makers ) • HM thinking requires us to separate correlation from cause-effect
  49. 49. 49© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 12. Process instability must be analyzed in real-time • Identifying process instability and doing root- cause analysis must be as close to the event as possible
  50. 50. 50© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 12. Process instability must be analyzed in real-time • Identifying process instability and doing root- cause analysis must be as close to the event as possible • If the root-cause analysis is done too far from the event (more like a post-mortem), there is lesser likelihood of identifying the true root cause
  51. 51. 51© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 12. Process instability must be analyzed in real-time • Identifying process instability and doing root- cause analysis must be as close to the event as possible • If the root-cause analysis is done too far from the event (more like a post-mortem), there is lesser likelihood of identifying the true root cause – E.g., If you find out today that the time to reach office (from home) was out-of- control 6 weeks ago, you may not be able to recall the conditions that caused this
  52. 52. 52© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 12. Process instability must be analyzed in real-time • Identifying process instability and doing root- cause analysis must be as close to the event as possible • If the root-cause analysis is done too far from the event (more like a post-mortem), there is lesser likelihood of identifying the true root cause – E.g., If you find out today that the time to reach office (from home) was out-of- control 6 weeks ago, you may not be able to recall the conditions that caused this • HM organizations create the infrastructure, tools, and processes to collect, analyze and report data on a real-time basis to effectively debug their process instability
  53. 53. 53© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 13. Control of critical sub-processes impacts higher level performance • If you want your weight in control, measuring and monitoring your weight frequently (and putting it on a control chart) is not going to bring your weight under control 
  54. 54. 54© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 13. Control of critical sub-processes impacts higher level performance • If you want your weight in control, measuring and monitoring your weight frequently (and putting it on a control chart) is not going to bring your weight under control  • To control weight, you may need to monitor and measure: – The amount and type of exercise that you do – The amount and type of calories that you eat You may even have to measure the calories consumed and expended at various times during the day
  55. 55. 55© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 13. Control of critical sub-processes impacts higher level performance • If you want your weight in control, measuring and monitoring your weight frequently (and putting it on a control chart) is not going to bring your weight under control  • To control weight, you may need to monitor and measure: – The amount and type of exercise that you do – The amount and type of calories that you eat You may even have to measure the calories consumed and expended at various times during the day • Similarly, (in a project) measuring schedule variance alone is not likely to bring the schedule under control
  56. 56. 56© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 13. Control of critical sub-processes impacts higher level performance • If you want your weight in control, measuring and monitoring your weight frequently (and putting it on a control chart) is not going to bring your weight under control  • To control weight, you may need to monitor and measure: – The amount and type of exercise that you do – The amount and type of calories that you eat You may even have to measure the calories consumed and expended at various times during the day • Similarly, (in a project) measuring schedule variance alone is not likely to bring the schedule under control • HM organizations identify and control sub-processes that are critical to overall performance
  57. 57. 57© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 14. Simulation may be the only practical method to model reality today • Given that: – There are multiple, interrelated goals to achieve
  58. 58. 58© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 14. Simulation may be the only practical method to model reality today • Given that: – There are multiple, interrelated goals to achieve – Inputs and processes have their own variations (probabilities)
  59. 59. 59© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 14. Simulation may be the only practical method to model reality today • Given that: – There are multiple, interrelated goals to achieve – Inputs and processes have their own variations (probabilities) – Most of these variations do not fit neat symmetrical bell curves
  60. 60. 60© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 14. Simulation may be the only practical method to model reality today • Given that: – There are multiple, interrelated goals to achieve – Inputs and processes have their own variations (probabilities) – Most of these variations do not fit neat symmetrical bell curves – We need to predict the potential impact of our choices on the outcomes (multiple objectives)
  61. 61. 61© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 14. Simulation may be the only practical method to model reality today • Given that: – There are multiple, interrelated goals to achieve – Inputs and processes have their own variations (probabilities) – Most of these variations do not fit neat symmetrical bell curves – We need to predict the potential impact of our choices on the outcomes (multiple objectives) • Simple, deterministic mathematical equations alone do not suffice to reflect reality
  62. 62. 62© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 14. Simulation may be the only practical method to model reality today • Given that: – There are multiple, interrelated goals to achieve – Inputs and processes have their own variations (probabilities) – Most of these variations do not fit neat symmetrical bell curves – We need to predict the potential impact of our choices on the outcomes (multiple objectives) • Simple, deterministic mathematical equations alone do not suffice to reflect reality • In HM organizations, random number based computer simulations (e.g., Monte Carlo) provide the best platform to model reality
  63. 63. 63© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor 1. Nothing is definite 2. Everything is interrelated 3. Nothing is permanent 4. Nobody likes variation 5. Statistics is non-intuitive 6. There are no bell-curves in real life 7. Outliers cannot be wished away 8. Skills impacts process performance 9. Statisticians should not be decision makers 10. Process performance cannot be decreed 11. Correlation is not the same as cause-effect 12. Process instability must be analyzed in real-time 13. Control of critical sub-processes impacts higher level performance 14. Simulation may be the only practical method to model reality today (Recalling…) HM’s Fourteen
  64. 64. 64© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor  Related Presentations – CMMI® Explored – Concept of Maturity – CMMI® - SVC Explored – Process Area Overview
  65. 65. 65© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor ® CMMI and CMM are registered trademarks of Carnegie Mellon University © Rajesh Naik, 2013 This work is licensed under a Creative Commons Attribution 3.0 Unported License. This means you are free (1) to copy, distribute, display, and perform the work, (2) to make derivative works, and (3) to make commercial use of the work so long as you give proper attribution to the author and retain the license notice. If you create derivative works using this work, they should also be made available under a similar license. For further information go to http://creativecommons.org/licenses/by/3.0/ For uses outside the scope of the license, contact Rajesh Naik at naik.rajeshnaik@gmail.com
  66. 66. 66© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor Thank You! Rajesh Naik AlignMentor Email naik.rajeshnaik@gmail.com Mobile +91 9845488767 Blog http://alignmentor.com Website www.rajeshnaik.com Also, have a look at the latest “business novel”: Aligning Ferret: How an Organization Meets Extraordinary Challenges By Swapna Kishore & Rajesh Naik Available at Amazon http://www.amazon.com/dp/B00CZA94XC

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