a business intelligence platform - its) canada business intellige… · 1 a business intelligence...
TRANSCRIPT
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1
A
Business Intelligence Platform to Support
Fact-Based Decision-Making for Public Transit
Alec Knowles, BSc, BA, LEED AP
MASc Candidate, University of Toronto ITS Canada ACGM 1-4 June 2014
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How many of you have…
heard of Business Intelligence (BI) tools?
used BI tools?
extensive experience with BI tools?
come to the wrong room but feel too embarrassed to leave?
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What does “fact-based decision-making” really mean?
Why fact-based decision-making? Why BI? Pilot Project:
Background
Data Preparation
Business Intelligence Tools (MicroStrategy)
Lessons
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1. fact, n.
Something that has really occurred or is actually the case... as opposed to what is merely inferred...
2. decision, n. The final and definite result of examining a question; a conclusion, judgement…
1 + 2 = A judgement based on truths known by actual observation, as opposed to what is merely inferred
Source: Oxford English Dictionary, 2014 4
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Visualisations
Aggregation is automated instantaneous data slicing Live updating of results empowers near real-time monitoring Consistency from one report to the next
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What does “fact-based decision-making” really mean?
Why fact-based decision-making? Why BI?
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Pilot project:
Background
Data Preparation
Business Intelligence Tools (MicroStrategy)
Lessons
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London [Ontario] Transit Commission, IBI Group, U of T (c/o NSERC)
Trapeze previously providing basic diagnostic tools as part of CAD/AVL & APC software
Pilot to produce Key Performance Indicators (KPIs) Percent Seated Capacity
Schedule Adherence
Overload Events
Percent Scheduled Revenue Service Hours
Running Time Adequacy
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SQL
Databases, tables, and columns, oh my! data dictionary
Data joining 50-80% of total time Data cleaning Overload events
Sample output Excel BI tools
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Microstrategy
Cloud-based (Analytics Express)
Demo (screenshots)
Disclaimer: this demo relies on an interim, incomplete sample data set intended for illustrative purposes only.
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Summary: System level view of all KPIs.
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Percent Seated Capacity: Ratio of passengers on board to
total seated capacity.
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Schedule Adherence: On-time performance as a
percentage of total trips.
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Percent Scheduled Revenue Service Hours: Ratio
of revenue hours served to scheduled revenue hours.
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Running Time Adequacy: Percentage of timepoints/trips
with insufficient, excess, or sufficient run time.
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Acquire data dictionaries / schematics ASAP Data analysts / wizards need not apply Allow more time than you think to clean data
Thoroughly research BI software options
Maintain a clear picture of the desired end
result – from both a BI & KPI perspective
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ITS Canada webinar (28 Feb 2013): starring John George (IBI Group), Gerry Akkerman (Translink), Bill Menzies (Dillon Consulting), Dr. Bruce Hellinga (University of Waterloo); hosted by Rajeev Roy (York Region Transit)
Wilson, N.H.M. (2013, January 25). Making Use of Automated Data Collection to Improve Transit Effectiveness. Bus Rapid Transit Centre of Excellence. Webinar [Video file]. Retrieved from http://www.youtube.com/watch?v=sWQkxkCuQPY&list=UUQgnrhQ9goiZ7WxL38bf19Q&index=1
Zhao, J. (2004). The Planning and Analysis Implications of Automated Data Collection Systems: Rail Transit OD Matrix Inference and Path Choice Modeling Examples. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, United States.
Image sources: http://daveberta.ca/wp-content/uploads/2011/06/Money.jpg
http://www.microstockdiaries.com/are-you-a-quality-microstocker-or-a-quantity-microstocker.html
http://www.silveroakcasino.com/blog/online-roulette/online-roulette-bonus.html
http://powerlisting.wikia.com/wiki/Black_Hole_Manipulation?file=Supermassive_black_hole.jpeg
http://img3.wikia.nocookie.net/__cb20120622020658/simpsons/images/f/fe/Wizard.jpg
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Thank you
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Acknowledgements: Dr. Amer Shalaby, U of T
Yuval Grinspun & John George, IBI Group Patrick Cormier, LTC
Paul Luong, Data Wizard