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Data Driven Decision Making Kimberly Chan & Molly Mioduszewski
Fixed Income IT
13 December 2013
Agenda
Background Goals & Objectives Methodology Findings and
Conclusions & Recommendations
Summary
13/12/2013 2
Data Driven Decision Making
Background
Decision Making
Project Reporting
Data Reporting
13/12/2013 3
Data Driven Decision Making
Background - Areas of Focus
13/12/2013 4
Data Driven Decision Making
CA Clarity
Programme and Portfolio
management
Time Tracking
Project reporting
JIRA
Task management
Supports AGILE teams
Bug tracking, feature/request management
Estimate to Complete (ETC)
Budget
Forecasting
Agenda
Background Goals & Objectives Methodology
Findings and Conclusions &
Recommendations Summary
13/12/2013 5
Data Driven Decision Making
Goals & Objectives
13/12/2013 6
Data Driven Decision Making
Understand Clarity usage and best practice required to support effective decision making
Improve application of the Estimate to Complete (ETC) measure
Enhance JIRA’s visibility and connectivity to Clarity
Agenda
Background Goals & Objectives Methodology Findings and
Conclusions & Recommendations
Summary
13/12/2013 7
Data Driven Decision Making
Methodology
13/12/2013 8
Data Driven Decision Making
Research
Primary Interviews
Survey
Secondary Interviews
Conclusions
Agenda
Background Goals & Objectives Methodology
Findings and Conclusions &
Recommendations: Clarity
Summary
13/12/2013 9
Data Driven Decision Making
Clarity Findings
Primary Interviews
13/12/2013 10
Data Driven Decision Making
• Used mainly for budgeting time • Inconsistent data quality • Benefits are misunderstood
• Recently implemented
Clarity Survey Results: User Information
13/12/2013 11
Data Driven Decision Making
Number of Respondents 288
Clarity Survey Results: User Information contd.
13/12/2013 12
Data Driven Decision Making
Role Percentage of Respondents Number of Respondents Execution Manager 75.00% 6 Program Manager 62.50% 25 ProjectManager/Team Leader 45.00% 72
Total 103
Clarity Project Card
13/12/2013 13
Data Driven Decision Making
Clarity Project Card contd.
13/12/2013 14
Data Driven Decision Making
Clarity Project Card contd.
13/12/2013 15
Data Driven Decision Making
Clarity Project Card contd.
13/12/2013 16
Data Driven Decision Making
Survey Results – Clarity Project Card
13/12/2013 17
Data Driven Decision Making
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100% Project Card Component Ra0ngs
Essen1al
Helpful
Unnecessary
Components Between Q1 and Q2: • Back to Green Plan textbox
• Baseline textbox
• Key Milestones Top 10 List
• P&L in m€ • Project Costs • Running Costs
• Project Approvals
Components Below Q1: • Gantt Chart
• Financial Comments textbox
• Notes textbox
• Planning Comments textbox
• SteerCo Planning textbox
• SteerCo Members textbox
Evaluating Components with a Weighted Scale
13/12/2013 18
Data Driven Decision Making
Additional and Suggested Components
13/12/2013 19
Data Driven Decision Making
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
Reasoning and Objec1ves for the Project
Budgets Represented in Man Days (MD)
Budgets including Es1mate to Complete
(ETC)
% of R
espo
nses
Component
Addi0onal Components
Respondents’ Suggestions:
• Cost benefit of project
• ETC for budget and forecast
• Evolution of budget
• Risk & Issues
Components % of Respondents Number of Responses Reasoning and Objectives for the Project 29.27% 24 Budgets Represented in Man Days (MD) 46.34% 38 Budgets including Estimate to Complete (ETC) 24.39% 20
Clarity Findings
Secondary Interviews
13/12/2013 20
Data Driven Decision Making
• Gantt Chart is helpful but not necessary unless it can assist in budgeting
• Project financials would be better represented in man days
• May be helpful to have customized Project Cards per teams
Conclusions and Recommendations - Clarity
13/12/2013 21
Data Driven Decision Making
Proposed Project Card Template Page 1
Conclusions and Recommendations - Clarity
Proposed Project Card Template Page 1
13/12/2013 22
Data Driven Decision Making
Conclusions and Recommendations - Clarity
Proposed Project Card Template Page 2
13/12/2013 23
Data Driven Decision Making
Conclusions and Recommendations - Clarity
Proposed Project Card Template Page 2
13/12/2013 24
Data Driven Decision Making
Agenda
Background Goals & Objectives Methodology
Findings and Conclusions &
Recommendations: ETC
Summary
13/12/2013 25
Data Driven Decision Making
ETC Findings
Primary Interviews
13/12/2013 26
Data Driven Decision Making
• Included in Clarity but not used properly
• No defined use for reporting
• Not commonly used in project teams
Survey Results – Estimate to Complete
13/12/2013 27
Data Driven Decision Making
6% Yes
40% Did not respond
17%
8% 22%
7%
54% No
Why employees are not using ETC
ETC Findings
Secondary Interviews
13/12/2013 28
Data Driven Decision Making
• Currently not used but would be beneficial for forecasting
• Needs more education on usage
• Would be better represented in REAL time
Conclusions and Recommendations - ETC
Further investigate
Implement company-
wide standard
Incorporate a real time measure
Track improvement
13/12/2013 29
Data Driven Decision Making
• ETC usage is not currently optimized
Agenda
Background Goals & Objectives Methodology
Findings and Conclusions &
Recommendations: JIRA
Summary
13/12/2013 30
Data Driven Decision Making
JIRA Findings
Primary Interviews
13/12/2013 31
Data Driven Decision Making
• Useful tool for task management
• Contains useful internal features
• Produces data that is updated in Clarity
Survey Results – JIRA external reporting
13/12/2013 32
Data Driven Decision Making
18%
41%
5% 4%
7%
25%
External Reporting tools
None
Excel
Businees Objects
FX intelligence
JIRA
OTHER
Tableau In-house
developed reports
MIS reports that link eComm
Power Pivot
KPI Spread sheet SVN Wiki release
pages Quality
Centre/Index
Survey Results – JIRAs connectivity with Clarity
13/12/2013 33
Data Driven Decision Making
66%
24%
4% 6%
How JIRA is connected to Clarity
Not Manually Web Other
Other Responses: • In unused Clarity fields
• Not at the moment, but would be ideal
• Connection was not well made at the present
• JIRA works with AGILE management where Clarity works with waterfall management
JIRA Findings
Secondary Interviews
13/12/2013 34
Data Driven Decision Making
• External databases are team specific
• There is no current linkage with Clarity
Conclusions and Recommendations - JIRA
13/12/2013 35
Data Driven Decision Making
Visibility • JIRA has many unused internal reporting tools
• There is no majority of external reporting tools
Increase user access of JIRA throughout FIIT
Share best practices Track improvement
Conclusions and Recommendations - JIRA
13/12/2013 36
Data Driven Decision Making
Connection to Clarity • JIRA has no formal connection to Clarity
• Employees presently are manually entering JIRA data into Clarity
• Received a positive reaction
• Optimization of Project Mapping
• Employees would gain more value added time
Further investigate shared data
Understand possible
connections Test
connections Track
improvement
Agenda
Background Goals & Objectives Methodology Findings and
Conclusions & Recommendations
Summary
13/12/2013 37
Data Driven Decision Making
Summary - Clarity
13/12/2013 38
Data Driven Decision Making
Suggested Clarity Project Card
Summary – Clarity Recommendations
Regularly review project card
Investigate potential of customizing project cards
per team Training sessions for
Clarity
13/12/2013 39
Data Driven Decision Making
Summary – ETC Recomendations
Further investigate
Implement company-
wide standard
Incorporate a real time measure
Track improvement
13/12/2013 40
Data Driven Decision Making
Summary – JIRA Recommendations
13/12/2013 41
Data Driven Decision Making
Increase user access of JIRA throughout FIIT Share best practices Track improvement
Further investigate shared data
Understand possible
connections Test
connections Track
improvement
Visibility
Connection with Clarity
Acknowledgements
• Philip Coleman • David Purdie • Fixed Income Information Technology teams
• Prof. Arthur Gerstenfeld • Prof. Kevin Sweeney • Prof. Jon Abraham • Prof. Micha Hofri • Interdisciplinary and Global Studies Division office
13/12/2013 42
Data Driven Decision Making