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How Data
Scientists can
bridge the gap
between Data
and Business
Ary Bressane, Head of Data Innovation Lab
About me
Ary Bressane
• Head of Data Innovation Lab at MNUBO
• Lecturer at Concordia University on the Big Data Analytics program
• Co-organizer of MTL DATA Meetup
Technical Challenges
Approaches to tackle technical challenges
Methodology, Best Practices, Infrastructure, Technology, Tools
Big Data & Data Science Projects
Failure Rate
GARTNER ESTIMATED
85%
of big data projects fail
(2017). The initial
estimation was 60%
(GARTNER 2016)
THROUGH 2020
80%
of AI projects will remain
alchemy, run by wizards
whose talents will not
scale in the organization.
(GARTNER 2018)
THROUGH 2022
20%
of analytic insights will
deliver business
outcomes. (GARTNER
2018)
EXECUTIVE SURVEY
77%
respondents say that
“business adoption” of
big data and AI initiatives
continues to represent a
challenge for their
organizations
(NEWVANTAGE
PARTNERS 2019)
Big Data & Data Science Projects
Why they Fail
DATACONOMY (2016)
1. Solving the wrong problem
2. Mismatch of problem,
technology and personnel
3. Data integrity
HBR (2018)
1. You need to make your
purpose clear
2. Choose the tasks you
automate wisely
3. Choose your data wisely
4. Shift humans to higher-
value social tasks
KDNUGGET (2018)
1. Asking the wrong question
2. Trying to use it to solve the
wrong problem
3. Not having enough data
4. Not having the right data
5. Having too much data
6. Hiring the wrong people
…
https://www.kdnuggets.com/2018/07/why-
machine-learning-project-fail.html
https://hbr.org/2018/07/how-to-make-an-
ai-project-more-likely-to-succeed
https://dataconomy.com/2016/06/3-
reasons-why-data-science-can-fail/
Big Data & Data Science Projects
Skill Gap
Big Data & Data Science Projects
Skill Gap
How organizations
are approaching
the issue?
How organizations are approaching the issue?
Using Design Tools
• Focus on human values
• Radical collaboration
• Embrace experimentation
• Bias towards action
• Visual
• Incentive variety and diversity
• Defer judgement
How organizations are approaching the issue?
Design Thinking
Norman Tran
How organizations are approaching the issue?
Design Thinking
Design Sprints
Methodology
Design Sprints
Methodology
SET THE STAGE
• Choose a big challenge
• Recruit the team
• Lock five full days
• Get sprint supplies
SPRINT
• Runt the Five days
• Evaluate the learning
Design Sprints for Data Science Projects
Lessons Learned
Think first how the data will be consumed
(last mile problem)
Look for Actionable Insights
Start with a simple model (or not a model at all)
Lessons Learned
Example of
business outcome
Example of Business Outcome
Predictive Maintenance
FAILURE
P(Failure)
P(Failure)P(Failure)
P(Failure)P(Failure)
Failure Detect on HVAC systems
Results
Example of Business Outcome
Predictive Maintenance
Schedule Maintenance Assistant
WORKFLOW MOCKUP + PROTOTYPES
Map
1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23
26 27 28 29 30
24 25
Sun Mon Tue Wed Thu Fri Sat
Jan 2018
Schedule
CUSTOMER
MONITOR
ASSETS
REACTIVEPROACTIV
E
SALES
TECHNICIAN SERVICE
ENGINEERING
CUSTOMER
SERVICE
INVESTIGATE
ASSET
NEW ISSUE
!
KNOWN ISSUE
X OPEN TICKET
1
2
3
4
5
The Customer reports a issue to the Sales Rep, the
Technician or directly to the Service Team
The Service Team investigates the problem
with the specific asset
In the case of a new issue, a ticket is opened and the
Engineering Team informed
In the case of a known issue, a ticket is opened or the Customer is
contacted to solve the problem
The Service Team monitors all the assets on the install base to identify
the ones with low health
Thank you
Ary Bressane, Head of Data Innovation Lab
ary@mnubo.com

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KDD 2019 IADSS Workshop - How Data Scientists can bridge the gap between Data and Business - Ary Bressane

  • 1. How Data Scientists can bridge the gap between Data and Business Ary Bressane, Head of Data Innovation Lab
  • 2. About me Ary Bressane • Head of Data Innovation Lab at MNUBO • Lecturer at Concordia University on the Big Data Analytics program • Co-organizer of MTL DATA Meetup
  • 3. Technical Challenges Approaches to tackle technical challenges Methodology, Best Practices, Infrastructure, Technology, Tools
  • 4. Big Data & Data Science Projects Failure Rate GARTNER ESTIMATED 85% of big data projects fail (2017). The initial estimation was 60% (GARTNER 2016) THROUGH 2020 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization. (GARTNER 2018) THROUGH 2022 20% of analytic insights will deliver business outcomes. (GARTNER 2018) EXECUTIVE SURVEY 77% respondents say that “business adoption” of big data and AI initiatives continues to represent a challenge for their organizations (NEWVANTAGE PARTNERS 2019)
  • 5. Big Data & Data Science Projects Why they Fail DATACONOMY (2016) 1. Solving the wrong problem 2. Mismatch of problem, technology and personnel 3. Data integrity HBR (2018) 1. You need to make your purpose clear 2. Choose the tasks you automate wisely 3. Choose your data wisely 4. Shift humans to higher- value social tasks KDNUGGET (2018) 1. Asking the wrong question 2. Trying to use it to solve the wrong problem 3. Not having enough data 4. Not having the right data 5. Having too much data 6. Hiring the wrong people … https://www.kdnuggets.com/2018/07/why- machine-learning-project-fail.html https://hbr.org/2018/07/how-to-make-an- ai-project-more-likely-to-succeed https://dataconomy.com/2016/06/3- reasons-why-data-science-can-fail/
  • 6. Big Data & Data Science Projects Skill Gap
  • 7. Big Data & Data Science Projects Skill Gap
  • 9. How organizations are approaching the issue? Using Design Tools • Focus on human values • Radical collaboration • Embrace experimentation • Bias towards action • Visual • Incentive variety and diversity • Defer judgement
  • 10. How organizations are approaching the issue? Design Thinking Norman Tran
  • 11. How organizations are approaching the issue? Design Thinking
  • 13. Design Sprints Methodology SET THE STAGE • Choose a big challenge • Recruit the team • Lock five full days • Get sprint supplies SPRINT • Runt the Five days • Evaluate the learning
  • 14. Design Sprints for Data Science Projects Lessons Learned
  • 15. Think first how the data will be consumed (last mile problem)
  • 17. Start with a simple model (or not a model at all)
  • 19. Example of Business Outcome Predictive Maintenance FAILURE P(Failure) P(Failure)P(Failure) P(Failure)P(Failure) Failure Detect on HVAC systems Results
  • 20. Example of Business Outcome Predictive Maintenance Schedule Maintenance Assistant WORKFLOW MOCKUP + PROTOTYPES Map 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 26 27 28 29 30 24 25 Sun Mon Tue Wed Thu Fri Sat Jan 2018 Schedule CUSTOMER MONITOR ASSETS REACTIVEPROACTIV E SALES TECHNICIAN SERVICE ENGINEERING CUSTOMER SERVICE INVESTIGATE ASSET NEW ISSUE ! KNOWN ISSUE X OPEN TICKET 1 2 3 4 5 The Customer reports a issue to the Sales Rep, the Technician or directly to the Service Team The Service Team investigates the problem with the specific asset In the case of a new issue, a ticket is opened and the Engineering Team informed In the case of a known issue, a ticket is opened or the Customer is contacted to solve the problem The Service Team monitors all the assets on the install base to identify the ones with low health
  • 21. Thank you Ary Bressane, Head of Data Innovation Lab ary@mnubo.com