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CRITERION #2: DEFINE:

INTENT: Formulate the stakeholder problem. Define the problem, needs and objectives.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. Are all requirements met?

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2. What are the Hardware accelerators for machine learning tasks and definitions?

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3. What is the scope?

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4. Is data collected and displayed to better understand customer(s) critical needs and requirements.

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5. Is it clearly defined in and to your organization what you do?

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6. How do you build the right business case?

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7. What is a worst-case scenario for losses?

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8. Do you all define Hardware accelerators for machine learning in the same way?

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9. Has the improvement team collected the ‘voice of the customer’ (obtained feedback – qualitative and quantitative)?

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10. Who approved the Hardware accelerators for machine learning scope?

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11. Have all of the relationships been defined properly?

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12. How are consistent Hardware accelerators for machine learning definitions important?

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13. What Hardware accelerators for machine learning services do you require?

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14. Do you have organizational privacy requirements?

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15. When are meeting minutes sent out? Who is on the distribution list?

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16. Are approval levels defined for contracts and supplements to contracts?

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17. How do you manage unclear Hardware accelerators for machine learning requirements?

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18. What are the Roles and Responsibilities for each team member and its leadership? Where is this documented?

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19. What baselines are required to be defined and managed?

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20. What is the definition of Hardware accelerators for machine learning excellence?

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21. Who are the Hardware accelerators for machine learning improvement team members, including Management Leads and Coaches?

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22. What is out-of-scope initially?

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23. Is Hardware accelerators for machine learning linked to key stakeholder goals and objectives?

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24. What happens if Hardware accelerators for machine learning’s scope changes?

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25. What system do you use for gathering Hardware accelerators for machine learning information?

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26. What is the worst case scenario?

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27. How do you catch Hardware accelerators for machine learning definition inconsistencies?

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28. Has your scope been defined?

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29. Have specific policy objectives been defined?

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30. What are the Hardware accelerators for machine learning use cases?

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31. Are audit criteria, scope, frequency and methods defined?

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32. Do you have a Hardware accelerators for machine learning success story or case study ready to tell and share?

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33. Is Hardware accelerators for machine learning required?

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34. Has a high-level ‘as is’ process map been completed, verified and validated?

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35. What is the scope of the Hardware accelerators for machine learning work?

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36. In what way can you redefine the criteria of choice clients have in your category in your favor?

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37. How will variation in the actual durations of each activity be dealt with to ensure that the expected Hardware accelerators for machine learning results are met?

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38. How can the value of Hardware accelerators for machine learning be defined?

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39. Has a team charter been developed and communicated?

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40. What are (control) requirements for Hardware accelerators for machine learning Information?

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41. Why are you doing Hardware accelerators for machine learning and what is the scope?

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42. How do you manage scope?

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43. What customer feedback methods were used to solicit their input?

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44. How do you think the partners involved in Hardware accelerators for machine learning would have defined success?

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45. Does the team have regular meetings?

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46. What is in scope?

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47. How does the Hardware accelerators for machine learning manager ensure against scope creep?

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48. Where can you gather more information?

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49. What sort of initial information to gather?

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50. What key stakeholder process output measure(s) does Hardware accelerators for machine learning leverage and how?

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51. Is the team adequately staffed with the desired cross-functionality? If not, what additional resources are available to the team?

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52. Are there any constraints known that bear on the ability to perform Hardware accelerators for machine learning work? How is the team addressing them?

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53. How was the ‘as is’ process map developed, reviewed, verified and validated?

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54. Who is gathering information?

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55. Is there regularly 100% attendance at the team meetings? If not, have appointed substitutes attended to preserve cross-functionality and full representation?

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56. When is the estimated completion date?

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57. What is the context?

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58. Is special Hardware accelerators for machine learning user knowledge required?

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59. How do you gather Hardware accelerators for machine learning requirements?

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60. How would you define Hardware accelerators for machine learning leadership?

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61. How do you gather requirements?

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62. Is the current ‘as is’ process being followed? If not, what are the discrepancies?

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63. How do you hand over Hardware accelerators for machine learning context?

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64. How will the Hardware accelerators for machine learning team and the group measure complete success of Hardware accelerators for machine learning?

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65. Is the Hardware accelerators for machine learning scope complete and appropriately sized?

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66. Are resources adequate for the scope?

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67. Has the Hardware accelerators for machine learning work been fairly and/or equitably divided and delegated among team members who are qualified and capable to perform the work? Has everyone contributed?

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68. Have all basic functions of Hardware accelerators for machine learning been defined?

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69. Is the work to date meeting requirements?

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70. What Hardware accelerators for machine learning requirements should be gathered?

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71. Has everyone on the team, including the team leaders, been properly trained?

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72. What gets examined?

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73. What defines best in class?

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74. If substitutes have been appointed, have they been briefed on the Hardware accelerators for machine learning goals and received regular communications as to the progress to date?

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75. How and when will the baselines be defined?

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76. What sources do you use to gather information for a Hardware accelerators for machine learning study?

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77. Do the problem and goal statements meet the SMART criteria (specific, measurable, attainable, relevant, and time-bound)?

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78. Has a project plan, Gantt chart, or similar been developed/completed?

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79. How would you define the culture at your organization, how susceptible is it to Hardware accelerators for machine learning changes?

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80. What critical content must be communicated – who, what, when, where, and how?

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81. Who is gathering Hardware accelerators for machine learning information?

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82. How is the team tracking and documenting its work?

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83. How do you keep key subject matter experts in the loop?

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84. Are roles and responsibilities formally defined?

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85. Have the customer needs been translated into specific, measurable requirements? How?

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86. When is/was the Hardware accelerators for machine learning start date?

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87. What scope do you want your strategy to cover?

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88. Is the Hardware accelerators for machine learning scope manageable?

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89. What are the record-keeping requirements of Hardware accelerators for machine learning activities?

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90. Is the improvement team aware of the different versions of a process: what they think it is vs. what it actually is vs. what it should be vs. what it could be?

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91. What knowledge or experience is required?

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92. What would be the goal or target for a Hardware accelerators for machine learning’s improvement team?

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93. What is the scope of the Hardware accelerators for machine learning effort?

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94. Has the direction changed at all during the course of Hardware accelerators for machine learning? If so, when did it change and why?

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95. What are the rough order estimates on cost savings/opportunities that Hardware accelerators for machine learning brings?

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96. Will a Hardware accelerators for machine learning production readiness review be required?

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97. What was the context?

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98. Is there a completed, verified, and validated high-level ‘as is’ (not ‘should be’ or ‘could be’) stakeholder process map?

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99. Is there a critical path to deliver Hardware accelerators for machine learning results?

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100. Are there different segments of customers?

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101. How often are the team meetings?

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102. Is Hardware accelerators for machine learning currently on schedule according to the plan?

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103. Has a Hardware accelerators for machine learning requirement not been met?

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104. What is out of scope?

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105. Has anyone else (internal or external to the group) attempted to solve this problem or a similar one before? If so, what knowledge can be leveraged from these previous efforts?

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106. What are the tasks and definitions?

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107. Scope of sensitive information?

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108. Are task requirements clearly defined?

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109. Is the team equipped with available and reliable resources?

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110. What intelligence can you gather?

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111. What are the core elements of the Hardware accelerators for machine learning business case?

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112. What information do you gather?

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113. Are customer(s) identified and segmented according to their different needs and requirements?

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114. Is there a Hardware accelerators for machine learning management charter, including stakeholder case, problem and goal statements, scope, milestones, roles and responsibilities, communication plan?

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115. Has/have the customer(s) been identified?

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116. What are the compelling stakeholder reasons for embarking on Hardware accelerators for machine learning?

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117. What are the requirements for audit information?

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118. Are different versions of process maps needed to account for the different types of inputs?

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119. How do you gather the stories?

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120. Is there any additional Hardware accelerators for machine learning definition of success?

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121. The political context: who holds power?

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122. What are the boundaries of the scope? What is in bounds and what is not? What is the start point? What is the stop point?

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123. What is the definition of success?

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124. What information should you gather?

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125. How did the Hardware accelerators for machine learning manager receive input to the development of a Hardware accelerators for machine learning improvement plan and the estimated completion dates/times of each activity?

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126. Who defines (or who defined) the rules and roles?

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127. What scope to assess?

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128. How do you manage changes in Hardware accelerators for machine learning requirements?

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129. What are the dynamics of the communication plan?

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130. What constraints exist that might impact the team?

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131. Is scope creep really all bad news?

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132. What specifically is the problem? Where does it occur? When does it occur? What is its extent?

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133. What is in the scope and what is not in scope?

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134. Is there a completed SIPOC representation, describing the Suppliers, Inputs, Process, Outputs, and Customers?

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Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Hardware accelerators for machine learning Index at the beginning of the Self-Assessment.