Addresses data infrastructure and how to scale efficiently. As a platform that is more than doubling in user base (and activity) YOY, MapMyFitness has build a sustainable platform that provides more than 22 million users worldwide access to the information they want in a simple, rewarding way. In the company’s early years, health care providers such as Humana and Discovery were the first major partners to recognize the importance of fitness data and built a community on top of our software that opened the door to their members as well. Now every major brand from Coca-Cola to Purina Dog Food is seeing the value of this data and how they can tap in to promote a healthy and active lifestyle through interactive digital tools. As a new addition to the Under Armour family, the company will look to move past just steps and miles, and more into your daily story of activity. Kevin Callahan will address what challenges lie ahead for presenting data in a compelling way, and the opportunities as a brand to be a leader in Connected Fitness.

1. Dealing
with
Health
Data
Op#mizing
Data
Toolkits

2. 2
THE
MAPMYFITNESS
BRAND
AND
PRODUCT
PLATFORM
Our
integrated
fitness
plaGorm
consists
of
iOS
and
Android
apps,
a
suite
of
websites,
hundreds
of
third-­‐party
devices,
and
API
and
SDK
solu#ons,
all
synchronized
with
the
cloud.
It’s
the
most
flexible
and
robust
pla9orm
in
the
world
of
Connected
Fitness.

3. 3
WHO
IS
MAPMYFITNESS?
We
are
Connected
Fitness
—
we
are
obsessed
with
using
technology
to
make
fitness
social,
fun,
simple,
effec#ve,
and
rewarding.
We
are
Innovators
–
aYer
8
years
we
have
many
“firsts”
we
are
proud
of:
• First
online
fitness
mapping
and
tracking
system
on
web
and
subsequently
mobile
• First
connected
fitness
media
plaGorm
reaching
over
24M
consumers
• First
fitness
tracking
plaGorm
used
for
rewards
programs
by
major
health
insurers
• First
to
support
wireless
Bluetooth
4.0
sensor
connec#ons
on
iOS
and
Android
We
are
a
Business
-­‐
we
have
been
genera#ng
revenue
since
day
1
and
were
recently
acquired
by
Under
Armour.

4. 4
LOTS
OF
DATA
1
New
users
per
month
24
MILLION
Registered
members
over
avg
MILLION

5. 5
LOTS
OF
DATA
1
New
users
per
month
24
MILLION
Registered
members
over
avg
MILLION
24M
<
NYC
9M

6. 6
LOTS
OF
DATA
15
MILLION
Workouts
in
the
last
30
days
over
350
Workouts
each
minute

7. 7
LOTS
OF
DATA
2M
lbs
fat
burned
Last
30
days…

8. 8
WHAT
IS
CONNECTED
FITNESS?

9. 9
CONNECTED
FITNESS
IS
EXPLODING
170
million
wearable
devices
projected
to
be
shipped
by
2016*
$3.9
billion
es#mated
market
for
fitness
and
wellness
devices
in
2016*
MapMyFitness
syncs
the
data
from
over
400
fitness
devices
and
brings
it
to
life
in
a
way
that’s
mo#va#ng,
inspira#onal,
and
useful.
The
world
is
on
the
cusp
of
a
Connected
Fitness
Revolu7on.
* IMS Research

10. 10
PEOPLE
ARE
ADOPTING
LIKE
CRAZY,
BUT
THERE’S
A
CATCH…
Users
have
as
many
interfaces
as
they
do
devices
&
services.
It’s
a
fractured
mess.
Too
much
to
keep
track
of!

11. 11
WE
ARE
THE
API,
THE
PLATFORM
AND
THE
DASHBOARD
MapMyFitness
synchronizes
with
over
400
third-­‐party
wearable
fitness
devices
&
services

12. 12
Device
integraWon
MAPMYFITNESS
PLATFORM:
THE
UNIFYING
HUB
• Comprehensive
online
solu#on
for
tracking
fitness
• Mobile
&
web
plaGorm
using
everyday
devices
(smartphones)
• Cloud-­‐based
data
management
for
tracking
fitness
• Users
can
upload
results,
share
progress,
and
mo#vate
one
another
• Reference
framework
for
connec#ng
3rd
party
devices
to
web,
social
sharing
&
data
Web
FuncWonality
Mobile
Apps
Core
Members
Extended
reach
Social
engagement

13. 13
API
AND
SDK
SOLUTIONS
Access
core
MapMyFitness
features
with
our
SaaS
plaGorm.
With
both
push
and
pull
func#onality,
you
can
integrate
our
tools
into
your
digital
experiences
or
join
our
ecosystem
to
access
fitness
data
at
scale.
Our
licensed
solu#ons
include:
•
Routes
Widgets
•
Device
Integra#on
Widgets
•
Nutri#on
Widgets
•
Users
API
•
Routes
API
•
Workouts
API
•
Ac#vity
Feed
API
•
Groups
API
•
Events
API
•
iOS
SDK
•
Android
SDK

14. 14
MAPMYFITNESS
PLATFORM:
THE
UNIFYING
HUB
MapMyAPI.com

15. 15
SCALING
AND
WORKING
WITH
DATA

16. 16
EXPONENTIAL
GROWTH,
TIMELY
SCALING
0
40M
80M
120M
160M
2006
2007
2008
2009
2010
2011
2012
2013
Users
Routes
Workouts
2005
We’ve
evolved
our
scaling
approaches
as
usage
has
grown
Premature
scaling
would
have
been
wasteful.
Scaling
solu#ons
enable
business
growth!
Scaling
problems
got
interes#ng.
2014?

17. 17
EXPONENTIAL
GROWTH,
TIMELY
SCALING
0
40M
80M
120M
160M
2006
2007
2008
2009
2010
2011
2012
2013
Users
Routes
Workouts
2005
2013-­‐2014
March
Workout
Comparison
2013:
9M
vs.
2014:
16M

18. 18
EVOLUTION
OF
THE
INFRASTRUCTURE
2005
SINGLE
VPS
2007
PHYSICAL
SERVERS
Managed
hos#ng
2010
HYBRID
Public
cloud
for
new
applica#ons
2011
HYBRID
Hybrid
applica#ons
with
cloud
and
physical
capacity

19. 19
PREDICTABLE
VARIATION
IN
TRAFFIC
Over
years
Weeks
Days

20. 20
EVOLUTION
OF
THE
INFRASTRUCTURE
2012
CONSOLIDATION
2013
OPTIMIZATION
Private
/
Public
Cloud
Specialized
Data
Clusters
(MySQL,
PostgreSQL,
Mongo)
Separa#on
of
Services
(SOA)

21. 21
DATA
TOOLKITS
• Old
Way
• Simple
SQL
• Excel
Pivot
Tables
• New
Way
• Amazon
RedshiY
• Aggregate
SQL
tables,
Data
Par##oning
• iPython
Notebook
and
SciPy
• Future...
• we’ll
get
to
that
in
a
minute
-­‐-­‐

22. 22
AMAZON
REDSHIFT
Amazon
Redshid
is
a
fast,
fully
managed,
petabyte-­‐scale
data
warehouse
service

23. 23
IPYTHON
NOTEBOOK
The
IPython
Notebook
is
a
web-­‐based
interac#ve
computa#onal
environment
where
you
can
combine
code
execu#on,
text,
mathema#cs,
plots
and
rich
media
into
a
single
document.

24. 24
EXAMPLE
1:
SIMPLE
WORKOUTS
FROM
API

25. 25
EXAMPLE
1:
SIMPLE
WORKOUTS
(CONT)

26. 26
PANDAS:
PYTHON
DATA
ANALYSIS
LIBRARY
hvp://pandas.pydata.org/
en#re
data
analysis
workflow
in
Python
without
having
to
switch
to
a
more
domain
specific
language
like
R.
Google
“10
Minute
Pandas
Tour”
By
Wes
McKinney

27. 27
PANDAS
AND
REDSHIFT
Connect
directly
to
RedshiY
Data
Source!

28. 28
PANDAS
AND
REDSHIFT
(ZOOM)
Connect
directly
to
RedshiY
Data
Source!
df
=
sql.read_sql(sql_query,
conn)

29. 29
EXAMPLE
2:
PLOTTING
MAP
DATA
Trick
for
UI

30. 30
EXAMPLE
2:
PLOTTING
MAP
DATA
(CONT.)

31. 31
MATPLOTLIB
BASEMAP
TOOLKIT
library
for
plozng
2D
data
on
maps
in
Python.

32. 32
EXAMPLE
3:
ADVANCED
BASEMAP
VISUALIZATIONS

33. 33
EXAMPLE
5:
DATA
ANALYSIS
FOR
SALES

34. 34
EXAMPLE
6:
USER
SEGMENTATION

35. 35
FUTURE
PLANS
• Develop
more
robust
tools
to
access
our
GIS,
ac#graphy
and
physiological
data
• Explore
Interac#ve
• D3,
MapBox/TileMill,
etc.
• API
endpoints
tailored
to
data
analysis
• Incorpora#ng
UA
retail
and
consumer
data
to
support
UA
corporate
goals
• recommenda#on
systems
• prescrip#ve
training
• route
genera#on

36. 36
Thank
You!
WE
ARE
HIRING
IN
AUSTIN,
TX