Self Service A Complete Guide - 2020 Edition. Gerardus Blokdyk
Do your organizations IT BI teams and development experts provide guidance to users of self-service BI and analytics technologies?
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65. How should the self-service technology features be prioritized?
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66. Was a data collection plan established?
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67. Is data collected on key measures that were identified?
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68. Where can you go to verify the info?
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69. What is an unallowable cost?
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70. What are the agreed upon definitions of the high impact areas, defect(s), unit(s), and opportunities that will figure into the process capability metrics?
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71. Have you deployed self-service/automated tools to optimize your IT operational cost?
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72. Does management have the right priorities among projects?
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73. Is key measure data collection planned and executed, process variation displayed and communicated and performance baselined?
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74. How will costs be allocated?
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75. Which self-service technology are you focusing on?
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76. How advanced and coherent is the data architecture in support of a self-service analytics initiative?
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77. Do you aggressively reward and promote the people who have the biggest impact on creating excellent Self-service services/products?
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78. Among the Self-service product and service cost to be estimated, which is considered hardest to estimate?
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79. What key measures identified indicate the performance of the stakeholder process?
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80. What impact has using self-service for customer service currently had on your organizations ability to address priorities?
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81. Is it cost effective to use a self-service system because of the consequent reduction in staff costs?
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82. Are key measures identified and agreed upon?
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83. Who participated in the data collection for measurements?
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84. Have you found any ‘ground fruit’ or ‘low-hanging fruit’ for immediate remedies to the gap in performance?
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85. Which Self-service impacts are significant?
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86. Are high impact defects defined and identified in the stakeholder process?
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87. What does self-service really cost?
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88. What are the estimated costs of proposed changes?
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89. Should you focus on self-service or sales-supported SaaS?
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90. How frequently do you verify your Self-service strategy?
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91. How do you leverage Data Discovery for self-service analytics?
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92. What are predictive Self-service analytics?
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93. Has automation and self-service allowed for cost savings?
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94. What does losing customers cost your organization?
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95. What disadvantage does this cause for the user?
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96. What is self-service analytics and why is it important?
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97. What users will be impacted?
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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 Self-service Index at the beginning of the Self-Assessment.
CRITERION #4: ANALYZE:
INTENT: Analyze causes, assumptions and hypotheses.
In my belief, the answer to this question is clearly defined:
5 Strongly Agree
4 Agree
3 Neutral
2 Disagree
1 Strongly Disagree
1. What are the sources of data from self-service interactions?
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2. What are the best opportunities for value improvement?
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3. Do you drive employee experience and engagement through self-service?
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4. Do you, as a leader, bounce back quickly from setbacks?
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5. Are Self-service changes recognized early enough to be approved through the regular process?
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6. How will you manage IT and self-service data integration?
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7. Were Pareto charts (or similar) used to portray the ‘heavy hitters’ (or key sources of variation)?
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8. Do you have difficulty supporting self-service access to complex data sources?
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9. What are the Self-service design outputs?
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10. Is the process self-service or managed?
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11. What quality tools were used to get through the analyze phase?
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12. What are your key Self-service indicators that you will measure, analyze and track?
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13. Will your solution be self-serviced locally or provisioned in the data center?
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14. What is the cost of poor quality as supported by the team’s analysis?
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15. What are evaluation criteria for the output?
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16. What conclusions were drawn from the team’s data collection and analysis? How did the team reach these