www.iadss.org IADSS KDD 2019 Workshop Presentation by 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

8. How organizations
are approaching
the issue?

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

12. Design Sprints
Methodology

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)

16. Look for Actionable Insights

17. Start with a simple model (or not a model at all)

18. Lessons Learned
Example of
business outcome

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