Conférence du Symposium 2018 - Le grand événement de la gestion de projet Certaines conférences sont disponibles pour visionnement en différé, voir ici : https://www.pmimontreal.org/webconferences Depuis quelques années, on note un intérêt croissant pour l'analytique, l'intelligence artificielle, ainsi que d'autres domaines d'application dans lesquels les données occupent une place majeure. La digitalisation de nos interactions présente de grandes opportunités: nous pouvons maintenant envoyer et partager des données presque instantanément, notamment pour prendre des décisions dites 'empiriques' (ou simplement pour partager des informations avec nos familles et amis). Par ailleurs, il existe un coût associé à ces opportunités, coût dont nous ne sommes parfois pas toujours conscients. Au fur et à mesure que les barrières techniques sont franchies, d'autres barrières apparaissent, notamment en relation avec des enjeux de légalité, d'éthique, et de moralité quant à la collecte, au transport, et à l'utilisation des données. On fournit ici un aperçu de ces opportunités, des défis, ainsi que des solutions innovantes qui ont été utilisées ou sont en développement pour s'attaquer au défi du transport de données. Plus particulièrement, nous nous tournons vers les avancées dans les domaines de la recherche et de la pratique dans différents domaines et industries pour montrer comment le transport de données et la collaboration peuvent chacun bénéficier de leur influence lorsqu'ils sont utilisés de façon judicieuse.

1. Big Data: Transport and
Collaboration
Gregory Vial, PhD
HEC Montréal
gregory.vial@hec.ca

2. Consider both operational and analytical perspectives
(Big) Data (and analytics) can help us
• Connect everything
• Improve our decisions
• Let machines make (some) decisions for us
Big data does (not) matter?
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3. I will not spare you the “Vs” definitions (apologies)
• Volume – size matters
• Velocity – process and decide
• Variety – type and source
• Veracity – trustworthiness
Ironically, value is often missing from these
Equally important is the idea that Big data is relative
Big Data?
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4. 4
What we hear… and would rather hear
“Tool with strategic potential”
“Unstructured is common”
“Analysis & interpretation help”
“Outliers”
“Big data strategy”
“Structured data is dead”
“Data speaks to us”
“Amazon, GE, Walmart etc.”

5. Revolutionary
An end
Technology
IT people’s territory
A panacea
Readily exploitable
Something cool
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Reality checks
An evolution
A means to an end
A bricolage of technologies
Cross-functional opportunity
An avenue to explore
Raw ingredients of a recipe
Something highly sensitive
Perceptions of Big data: The reality of Big data:

6. Putting the cart before the wheels
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“Very few companies know how to exploit the data
already embedded in their core operating systems”
(Ross, 2013:93)
Often, we still struggle to generate usable data
When we do, using big data can be challenging

7. “Evidence-based, data-driven decision making provides the answer,
but it requires a big cultural shift and changes in how operations are
managed” (Ross, 2013:93)
It may sound obvious, but it is not easy to achieve
Data-driven decision making implies a scientific approach
• Science is tough
• Science is (lots of) trial and error
• Science may not tell you what you want to hear
Change is hard
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8. What about exemplars?
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Consider Google or Walmart
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How many companies are effectively like them?

9. Failure rate of big data initiatives: ~60% (Gartner), claims up to 85%
“Big data is like teenage sex: everyone talks about it, nobody really
knows how to do it, everyone thinks everyone else is doing it, so
everyone claims they are doing it…” (Dan Ariely)
More seriously, what can we learn from ongoing developments?
• Ironically (or not), history tends to repeat itself…
What about the others?
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10. What is your experience managing/participating in
business intelligence/data warehousing projects?
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And the beat goes on and on…

11. We wish to deliver insight, not technology
Yet we often focus on technology delivery
This is but the very first step of a big data journey
It is also not the hardest one
Typical big data project
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you are here
(keep going!)
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12. The truth is, the word project is sometimes challenging
• Implies fixed boundaries
• Sets expectations with regards to (immediate) outcomes
• Focuses on exploitation
Not that we are not used to this reality
• Agile projects often encounter the same phenomenon
Typical big data project
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13. Mix of on-premises/cloud
Mix of technologies (proprietary, open source)
• With cool names (e.g., H2O, Samza, Pig, Hive, Kafka)!
Varying degrees of integration among technologies
Technologies that evolve quickly
• For some, Hadoop’s MapReduce is (slowly) becoming obsolete
Typical big data/analytics platform
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14. It can quickly get messy…
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Typical big data/analytics platform
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15. Good opportunity to explore things quickly
Facilitates transportation of data and collaboration
Challenges:
• Legal requirements
• Cost estimates
• Vendor lock-in
• Security (cue AWS S3 buckets left unprotected…)
To the cloud?
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16. Transportation is paradoxical:
• At the forefront of disruption (e.g., Uber, Lyft, Tesla)
• Heavily reliant on legacy equipment (and software)
This makes leveraging big data challenging!
Let’s get back on track…
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17. GE Digital
• Aircraft maintenance:
• Schedule
• Location
Challenges
• Great PR, but (very) costly
• Late player in cloud platforms
Example of a success
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18. Téo Taxi
• Dispatch
• Performance optimization
• Fleet redistribution
Challenges
• Real-time: this is why velocity matters
Example of a success
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19. Transport for London
• Mapping travellers’ journeys
• Traffic redistribution
• Urban planning
Challenges
• Lack of uniformity in processes (e.g., bus vs metro)
• Inferring phenomena when data is missing
Example of a success
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20. What do we learn from these
examples?
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So…

21. Magic does not exist! It takes hard work (and time)
Big data has implications at multiple levels
• Operational, Tactical, Strategic (e.g., AWS)
Changes through big data are ongoing
• Digital transformation as a “journey” (Kane, 2015)
Ambidexterity is desirable, but difficult to achieve
Well…
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22. Thanks to recent news, my job on this topic is
already done 
… but security and privacy are two different,
complementary things
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Addressing the privacy conundrum

23. Addressing the privacy conundrum
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"Chaque jour, nous disséminons des données ultra-personnelles ;
chaque jour, ces données sont stockées, utilisées, croisées au profit
de quelqu'un, quelque part. Et ce quelqu'un, ce n'est pas vous. " (P.
Lagacé, La Presse, 07/03/2018)
Consider all stakeholders
Think about cost/benefits and risk management
• Already used to manage projects

24. Parting thoughts
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Have a vision (not necessarily a 5 year plan)
Start small and iterate, iterate, iterate (think agile)
Measure outcomes (think S.M.A.R.T)… and costs
Communicate (within and across boundaries)
Collaborate effectively (think matrix organization)
Be here for the good times… and the bad learning times

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Merci / Thank you!
Gregory Vial
HEC Montréal
gregory.vial@hec.ca