How can you adopt innovation at your company ? Why should you bother ? How can you do it ? What matters and why ? Here I share my learning from starting and running a startup and building data science products in thomson reuters and other organizations

1. Building an
Innovative Product
Shishir Chandra Choudhary

2. Few Examples of
Innovation
AirBnB
New Business Model for
Travel Accommodation
iPhone
Multitouch Phone & Platform
Amazon Web Services
IT Infrastructure Reimagined
Uber
New Business
Model for Taxi
SpaceX Rockets
Rocket Business
Reimagined
Alexa
Household
Platform for
Voice Assistance
YOUR
INNOVATION
HERE
Urban
Farming

3. Startups in your industry
may have you for breakfast,
unless you start innovating
Business model as well as platform innovation are
not just for traditional tech firms

4. What May Hold you Back ?
A successful business model of today that gives
comfortable money stream.
Nokia failed to explore multi touch
They were happy with status quo
Apple explored multi touch
for tablet and ended up applying
it to phone

5. Shrinking Time to Market
Innovation
Market
Adoption
10+ years
Innovation
Market
Adoption
2+ years
Google, AirBnb,
Uber, Dropbox,
Technology,
Business Model,
Solutions
Technology,
Business Model,
Solutions

6. How to turn your company into an
innovation + execution powerhouse ?
Execution Powerhouse Innovation Powerhouse
Do excellent in short run only Do excellent in long run only

7. What’s Innovation ?
1. Problem you care about
2. Idea Generation
3. Idea Validation
4. High Speed and
Low Cost of Iteration
and enough runway
5. Empowered People who love
what they do and care about the problem
1.1 Closeness
with User

8. 1. Problem
You care about
• If you don’t care a lot, you
won’t put up with interim
failures

9. What’s your company’s /
team’s / individual’s DNA ?
• What does your company or team do you individually do
very well ?
• Try to innovate in that problem space which you care a lot
about

10. 2. Idea Generation
For idea generation you want to do following
• Improve quality of ideas
• Improve quantity of ideas

11. Idea Quality & Quantity
•Be cloooose to
users
• Ideally - be your own target
demographic
• E.g. if an investment product, be user of
that investment product to invest
• Entire team should feel as
close to users as possible
• Otherwise, you solve the
wrong problem or fail to
learn

12. Why close to user ?
User Sales
Manage
ment
Product
Owner
Team
Leader
Dev
Pain Info.
= 100%
Pain Info.
= 50%
Pain Info.
= 25%
Pain Info.
= 12.5%
Pain Info.
= 6%
Pain Info.
= 3%
Day to day decisions taken are wrong
User Dev
Pain Info.
= 100%
Pain Info.
= 50%
Day to day decisions of team are better
Prod
Owner
Team
Lead
Scalability of software more than pays for user time

13. Why close to user
(or be the user) ?
Initial
Well Thought
Plan from user
50-60%
effective
Refined Idea
60 - 70%
effective
Experiment 1
Refined Idea
70 - 80%
effective
Experiment 2 Experiment 3
Adoption
= 5%
Adoption
= 15%
Adoption
= 30%
Adoption
= 80%
Involvement of user is that of collaboration and exploration rather than client and server
N…
…

14. Idea Quality and Quantity
•Explore
• Experiment a lot (side
projects / distributed
experiments)
• Have a long term process

15. Idea Quality and Quantity
• Be aware of and leverage
Open Source
• You can reduce redundant
effort and hence reduce
cost of experiments

16. 3. Idea Validation
• Never drive blind
Build analytics into your
product
• Talk to users who used your
new idea
• Take your product for test
drive yourself

17. 4. Rapid Cheap
Experimentation
• Each experiment should be
fast and cheap
• Invest in building infrastructure
to enable rapid experimentation

18. 5. Empowered People
Who love their work and care about the problem
• People are very important
component of innovation
• Hire people who just love what
they do
• Empower them, trust them
• Agile daily stands should be
just 5-10 min
• Teams should be small (5-10)
and self sufficient

19. Distributed Experimentation
• Higher management can’t
be bottleneck for
innovation
• Allow in company anyone to start
a cheap experiment (infra)
• Easy approval for 20% time
• Simple approval for dedicated
time based on results of easy
approval
• Sufficient runway for failures and
retries

20. Distributed ExperimentsThousandEmployees
EasyApproval-20%Time-Early
DistributedExperiment
SimpleApproval-RunwayTime-Late
DistributedExperiment
SomeRunawaySuccesses

21. Big Bets
Solving Right Problem
1. Select right problem
you care for
2. Hire for “purpose”
If building investment product,
hire excellent data scientists/
technologists/designers who
actively invest out of interest
and have a fresh perspective.
Hire people who live in future
or at edge of technology.
3. Give sufficient runway
for interim failures

22. Detour - What
Innovation is NOT
Keep the innovation map we saw in mind. We will
come back to it shortly.

23. It’s Not
Eureka !
Best of First Ideas Do not Survive Impact of Reality

24. Neither is it careful
planning
You don’t know what result of your experiment would be.
You don’t know the learning you would have after next experiment.

25. Innovation is iterative,
but how to scale this economically ?
1. Problem you care about (and
potentially financially rewarding)
2. Idea Generation
3. Idea Validation
4. High Speed and
Low Cost of Iteration
and enough runway
5. Empowered People who love
what they do and care about the problem
1.1 Closeness
with User

26. Some parts of this are
familiar !
1. Problem you care about (and
potentially financially rewarding)
2. Idea Generation
3. Idea Validation
4. High Speed and
Low Cost of Iteration
and enough runway
5. Empowered People who love
what they do and care about the problem
1.1 Closeness
with User
This is a central Agile
Principal
This is Agile / Lean / Design Thinking
This is Agile Dev Ops
AGILE ≢ SCRUM
The spirit of Agile vs
Form of Agile

27. It’s not the
particular
technology
Start with the problem you care about first. Figure out the right tech to apply.
Never the other way around ! You can’t “experiment” with outcome in mind already.
Also, If you don’t care about the problem, you would not survive the initial failures.

28. Innovation needs
sufficient runway
Before innovation flies, it crawls, walks and runs. Too short a runway and no idea will fly.

29. You need small, self sufficient, fast
moving team
You need small, self sufficient, fast moving team. Large team is only needed to scale the
final solution.

30. $$$
$$$
You don’t check success rate of innovation ideas
One in ten may succeed financially, but rewards would be large, specially if digital
solution which can scale easily. Each idea should be tried in a lean way. Each “failure”
informs next better attempts.

31. For DataScience
Projects

32. All previous still
applies
Problem You Really Care About
Empowered People Who Love Work & Care about The
Problem
Being Close to User
Generating Ideas -> Validating Ideas -> Quick Iterations
Learning and Sufficient Runway

33. Adopting Latest Innovation Research In House
Using Open Source Models,
Libraries & Platform
Early on concentrate on
learning from each small
project, not success or
inevitable failures
As you figure things out,
scale and execute
You must do a bit of this to
develop appreciation of
nuances & solve problems
not yet solved
Identify core pain of users/
customers and focus on
each of those with research
too (longer runway)
Small self sufficient and multi
disciplinary (DS, DE, API, UI,
Devops etc) teams
Small self sufficient and multi
disciplinary (DS, DE, API, UI,
Devops etc) teams

34. 2. Idea Generation
3. Idea Validation
4. High Speed and
Low Cost of Iteration
and enough runway
Get This Right
Tools to speed up experimentations

35. Open Data
Platform / UI / Product Analytics
Data Science
Solution
DS is top layer of good cake, not the
cake itself

36. Start with simple solutions
Start with simple rules
Once you have lot of data, introduce ML
Replace any complex rules with ML

37. Open Data
Wikipedia, Wikidata,
Twitter etc
Apps and API
Your own UI, API etc
would provide data
DS Strengthens With Data
Data Augmentation
Use Sensible
Augmentation of
Available Data
DS Improves with data

38. Q & A