Ganes Kesari, Gramener's Head of Analytics & Co-Founder gives his insights on how to craft a data science roadmap that maximizes ROI. The biggest reason why 80% of analytics projects fail is that they don’t solve the right problem. Asking analytics or data-related question is the worst way to initiate a data analytics project. This webinar will walk you through how to get started in the most efficient way possible. You'll discover a straightforward step-by-step strategy to unlocking corporate value through industry examples. Things you will learn from this webinar: -The most common reasons for the failure of data science initiatives -Identifying projects and prioritizing them -Building a data science strategy in three easy steps -Real-life examples are used to explain the approach Watch this full webinar on: https://info.gramener.com/data-science-roadmap To know more from our industry experts book a free demo at: https://gramener.com/demorequest/

1. The Best Way to Choose your
Data Science Projects
Ganes Kesari
Gramener
Webinar
Nov 2019

2. “80% of analytics
projects will fail…
- Gartner
Reference: McKinsey; Gartner report
1000 Data Scientists
$250,000 Avg cost

3. 3

4. 4
INTRODUCTION
Ganes Kesari
Co-founder & Head of Analytics
“Simplify Data Science for all”
100+ Clients
Insights as Stories
@kesaritweets Help start, apply and adopt Data Science

5. 5
EXAMPLES
CHOICE
OUTCOME ROADMAP

6. 6
MATURITY LEVELS WITH DATA
Data Engineering
Activities
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformation
Reporting Insights Consumption Decisions
Logs, IOT
Int/External
Stage/Stream
SQL, Spark..
Un/Structured
Data lake..
Cleaning
ETL
Preparation
Aggregates
Metrics/KPI
Reports
ML
EDA
AI
Info Design
Narrative
Data Stories
Workflows
Change Mgmt
Actions

7. 7
MATURITY LEVELS WITH DATA
Data Engineering
Activities
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformation
Reporting Insights Consumption Decisions
Logs, IOT
Int/External
Stage/Stream
SQL, Spark..
Un/Structured
Data lake..
Cleaning
ETL
Preparation
Aggregates
Metrics/KPI
Reports
ML
EDA
AI
Info Design
Narrative
Data Stories
Workflows
Change Mgmt
Actions

8. 8
DATA SCIENCE MATURITY: REPORTING - EXAMPLE

9. 9
MATURITY LEVELS WITH DATA
Data Engineering
Activities
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformation
Reporting Insights Consumption Decisions
Logs, IOT
Int/External
Stage/Stream
SQL, Spark..
Un/Structured
Data lake..
Cleaning
ETL
Preparation
Aggregates
Metrics/KPI
Reports
ML
EDA
AI
Info Design
Narrative
Data Stories
Workflows
Change Mgmt
Actions

10. 10
DATA SCIENCE MATURITY: INSIGHT - EXAMPLES

11. 11
DATA SCIENCE MATURITY: INSIGHT - EXAMPLES
Identifying Salmon
using AI
https://partner.microsoft.com/en-us/case-studies/gramener

12. 12
MATURITY LEVELS WITH DATA
Data Engineering
Activities
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformation
Reporting Insights Consumption Decisions
Logs, IOT
Int/External
Stage/Stream
SQL, Spark..
Un/Structured
Data lake..
Cleaning
ETL
Preparation
Aggregates
Metrics/KPI
Reports
ML
EDA
AI
Info Design
Narrative
Data Stories
Workflows
Change Mgmt
Actions

13. 13
This is a dataset (1975 – 1990) that
has been around for several years,
and has been studied extensively.
Yet, a visualization can reveal
patterns that are neither obvious nor
well known.
For example,
• Are birthdays uniformly distributed?
• Do doctors or parents exercise the C-section option to move
dates?
• Is there any day of the month that has unusually high or low births?
• Are there any months with relatively high or low births?
Very high births in September.
But this is fairly well known.
Most conceptions happen during
the winter holiday season
Relatively few births during the
Christmas and Thanksgiving
holidays, as well as New Year
and Independence Day.
Most people prefer not
to have children on the
13th
of any month,
given that it’s an
unlucky day
Some special days like
April Fool’s day are
avoided, but Valentine’s
Day is quite popular
More births Fewer births … on average, for each day of the year (from 1975 to 1990)
DATA SCIENCE MATURITY: CONSUMPTION - EXAMPLES
https://gramener.com/posters/Birthdays.pdf

14. 14
This is a birth date dataset that’s
obtained from school admission data
for over 10 million children. When we
compare this with births in the US, we
see none of the same patterns.
For example,
• Is there an aversion to the 13th or is there a local cultural nuance?
• Are holidays avoided for births?
• Which months have a higher propensity for births, and why?
• Are there any patterns not found in the US data?
Very few children are born in the
month of August, and thereafter.
Most births are concentrated in
the first half of the year
We see a large number of
children born on the 5th
, 10th
,
15th
, 20th
and 25th
of each month
– that is, round numbered dates
Such round numbered patterns
a typical indication of fraud.
Here, birthdates are brought
forward to aid early school
admission
More births Fewer births … on average, for each day of the year (from 2007 to 2013)
DATA SCIENCE MATURITY: CONSUMPTION - EXAMPLES
https://gramener.com/posters/Birthdays.pdf

15. 15
MATURITY LEVELS WITH DATA
Data Engineering
Activities
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformation
Reporting Insights Consumption Decisions
Logs, IOT
Int/External
Stage/Stream
SQL, Spark..
Un/Structured
Data lake..
Cleaning
ETL
Preparation
Aggregates
Metrics/KPI
Reports
ML
EDA
AI
Info Design
Narrative
Data Stories
Workflows
Change Mgmt
Actions

16. 16
DATA SCIENCE MATURITY: INSIGHT + CONSUMPTION - EXAMPLES
https://tcdata360.worldbank.org/stories/tech-entrepreneurship/

17. 17
DATA SCIENCE MATURITY: INSIGHT + CONSUMPTION - EXAMPLES
https://gramener.com/servicerequests/
Navigation filters
Visual flow diagram indicating
bottlenecks & volume of requests
Automated analysis to identify
areas which need work and
which can create maximum
impact

18. 18
MATURITY LEVELS WITH DATA
Data Engineering
Activities
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data
Storage
Data
Transformation
Reporting Insights Consumption Decisions
Logs, IOT
Int/External
Stage/Stream
SQL, Spark..
Un/Structured
Data lake..
Cleaning
ETL
Preparation
Aggregates
Metrics/KPI
Reports
ML
EDA
AI
Info Design
Narrative
Data Stories
Workflows
Change Mgmt
Actions

19. 19
POLL #1
DATA SCIENCE MATURITY LEVELS

20. 20
1 WHY PROJECTS FAIL?
2 HOW TO IDENTIFY INITIATIVES?
3 HOW TO BUILD YOUR ROADMAP?

21. 21
WHY DATA
SCIENCE
PROJECTS FAIL
A
CHOOSING DATA SCIENCE PROJECTS

22. 22
Interesting
vs
Impactful
TEAMS OFTEN GET THEIR PRIORITIES WRONG
Urgent
vs
Strategic
Possible
vs
Feasible

23. 23
THE DATA SCIENCE JOURNEY

24. 24
“ Most insights don’t deliver
business benefits because they
solve the wrong problem

25. 25
IDENTIFYING
POTENTIAL DATA
INITIATIVES
B
CHOOSING DATA SCIENCE PROJECTS

26. 26
POLL #2
DATA SCIENCE ADOPTION CHALLENGES

27. 27
COMPANY CULTURE IS THE BIGGEST ROADBLOCK FOR ADOPTION
https://www.oreilly.com/data/free/ai-adoption-in-the-enterprise.csp

28. 28
ASK EXECUTIVES FOR THEIR TIME, NOT JUST THEIR BUDGET
Start data science
initiatives top-down
Use Exec power to
navigate change
• Brainstorming workshop
• Identify core team & champions
• Planned change management
• Roadshows, reviews, incentives

29. 29
“ Success of the data science
journey is proportional to the
level of executive attendance in
the kick-off workshop

30. 30
Executive
Mandate
Change
Management
EXAMPLE: MEDIA BROADCASTER

31. 31
IDENTIFY THE USERS, THEIR OBJECTIVES & CHALLENGES
Who are your
users?
What are their
priorities?
What are their
pain areas?
• Define target users
• Roles, designations
• Qtrly/Yearly goals
• Interview, focus groups
• Metrics to quantify
• Ranked challenges

32. 32
A FRAMEWORK TO IDENTIFY THE POTENTIAL INITIATIVES
Stakeholder
groups
Objectives Initiatives Questions Data
have a set of that can be met by which answer specific using
for that meet that can address suggests
Data driven approach
Business driven approach
1
2
Your list of
relevant data
science initiatives

33. 33
• Ground level Salesforce
• Sales Managers
• Acquire & retain clients
• Cross-sell to clients
• Utilize ad inventories
• Loss of market share
• Poor client monetization
• Wasted ad inventory
Users
Priorities
Pain areas
EXAMPLE: MEDIA BROADCASTER

34. 34
PRIORITIZING
THEM INTO A
ROADMAP
C
CHOOSING DATA SCIENCE PROJECTS

35. 35
Impact
USE THE 3 LEVERS FOR PRIORITIZATION OF PROJECTS
Feasibility Urgency
• Revenue, Cost, Effort
• Quantified impact
• Data, Tech, Budget
• Low – medium – high
• Timeframe available
• Low – medium – high

36. 36
A FRAMEWORK TO CHOOSE THE RIGHT PROJECTS
Stakeholder
groups
Objectives Initiatives Questions Data
have a set of that can be met by which answer specific using
for that meet that can address suggests
Data driven approach
Impact
Prioritised Roadmap
Business driven approach
1
2
Feasibility
High
Med
Low
Low
Urgency
Med
High
Deferred
Quick wins
Strategic
Evaluate
Evaluate

37. 37
BUILD YOUR ROADMAP WITH PROJECTS ACROSS THE LEVELS
Data Engineering
Maturity
Phases
Data Science
Data as
‘Culture’
Data
Collection
Data Storage
Data
Transformation
Reporting Insights Consumption Decisions
Impact
Prioritised Roadmap
Feasibility
High
Med
Low
Low
Urgency
Med
High
Deferred
Quick wins
Strategic
Evaluate
Evaluate

38. 38
EXAMPLE: MEDIA BROADCASTER
Business
Challenge
Initiative to solve it Impact Urgency Feasibility
Loss of
Market
Share
• Customer acquisition
• Customer retention
$5 M
$4 M
High
Medium
Low
High
Poor client
monetization
• Cross selling
• Pricing improvements
$2 M
$0.5M
High
Medium
High
Low
Wasted ad
inventory
• Improve fulfilment
• Sell to new/existing
clients
$0.7M
$1 M
Medium
High
High
Medium

39. 39
POLL #3
CHALLENGES IN BUILDING YOUR DATA SCIENCE
ROADMAP

40. 40
RECAP: BEST WAY TO CHOOSE YOUR DATA SCIENCE PROJECTS
• Scaling the stages
• Insights + Consumption
Maturity levels in data
• 80% failure rate
• Misplaced priorities
Why projects fail?
Building your roadmap
• Impact, Feasibility, Urgency
• Framework to sequencing
Identifying initiatives
• Start initiatives top-down
• Users, priorities, pain areas

41. 41
“Amongst organizations that
reached the highest level of Data
Maturity, nearly half of them
significantly exceeded business
goals.
- Deloitte
Reference: Deloitte report

42. 42
@kesaritweets
Thank You!
gramener.com /ganes-kesari
gramener.com/solutions

43. 43
EXAMPLE: MEDIA BROADCASTER