FOWA Boston

1. Pretty Pictures Zen, Data Visualization and the Art of Real-
Time Decision-Making
Brandon Satrom
@BrandonSatrom

2. SO. MUCH. DATA.
18% 25%
81%
!
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1
2
4
3

3. Social Apps
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

5. App Analytics
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

10. DevOps
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

14. WHAT ARE WE DOING
WITH ALL THIS DATA? $
-

16. 15
30
45
60
What effect, good or bad, did the
last release of your web or mobile
app have on customer conversion?
CONVERSION
15
30
45
60
Did that recent system outage
negatively impact website
engagement? How do you plan to
address it?
ENGAGEMENT
13
25
38
50
Why have only 40% of Android
users installed the latest version of
your app?
ADOPTION
18
35
53
70
What’s causing all of those app
crashes anyway?
BLOCKERS
QUESTIONS. QUESTIONS. QUESTIONS.

17. 18
35
53
70
Did that new pair of shoes affect
your speed or running style?
PERFORMANCE
15
30
45
60
How can your checkin history tell
help you choose a restaurant or a
beer or wine from this 3 page list?
PREFERENCES
13
25
38
50
What effect does meeting your
step count goal for the day have on
your energy, diet or overall well-
being?
GOALS
18
35
53
70
What impact does mood tracking
or journaling have on your career
choices?
ASPIRATIONS
QUESTIONS. QUESTIONS. QUESTIONS.

18. Building better experiences for our customers?
DOES ANY OF THAT DATA
MATTER IF WE’RE NOT…
1
2
3
Using insights to change and improve our
behavior?
Improving the apps and systems we use to run
our businesses?

19. HOW ARE WE MANAGING THE NOISE?
-
!
.
, /
0 1
2
3
4
5
6
7
8
9
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20. 300
billion
3 billion
1 billion
14 million
“BIGGER THAN THE INTERNET”
Internet Growth
from 1993 - 2015
The Number of “Connected
Things” by 2020
(Projected)

21. HOW DO WE SEPARATE THE
SIGNAL FROM THE NOISE?

22. Tools for Data Ingestion
Tools for Data Visualization
Tools for Recommendation
THE RIGHT TOOLS + THE RIGHT APPS = TRUE INSIGHT
Apps that can be human-directed
Apps that can learn
Apps that take action

23. THE CONTINUUM OF DATA INSIGHT
INSIGHT
DIRECTION
Human defines rules
or conditions in the
system in advance;
System takes action
when conditions are
met.
RECOMMENDATION
System makes
suggestions based
on data & a
human takes
action
VISUALIZATION
System provides
visuals to help you
reason about
data.
COLLECTION
System gathers
data and stores it
in some location
LEARNING
Systems that take
action based on
past behavior,
public information
or other factors
Real-Time
SPEED OF DECISION-MAKING
SPEED OF DATA INGESTION

24. Data Collection
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

25. DATA COLLECTION/INGESTION SYSTEMS
1. Data is created (and stored locally)
2.Data is sent to another location for
storage
3.The rest is up to you…
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26. EXAMPLES OF DATA COLLECTION/INGESTION SYSTEMS
Microsoft Azure
AT&T M2X
Wolfram Data Drop
Telerik Backend Services
Parse

27. Building Collection
Systems
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

28. • Data storage is foundational, so your
options are endless…
• HOWEVER, if you have a choice,
optimize for speed (of entry and
retrieval)
• Real-time apps begin with real-time
transport & storage
• Socket.io
• Meteor
• Firebase
• MongoDB
CONSIDERATIONS FOR DATA COLLECTION SYSTEMS

29. CONSIDERATIONS FOR DATA COLLECTION SYSTEMS
• Even better, consider backends with built-in analytics capabilities
• InfluxDB + Grafana
• Wolfram Alpha

30. Data Visualization
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

31. DATA VISUALIZATION SYSTEMS
1. System aggregates and presents
data in a consumable way.
2. Action taken in response, if any, is
manual
3. Many Experiences insert
“gamification” here to trigger action
or improve engagement
,
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36. • Favor tools that provide automated
visualizations of your data…
•OR tools that make it easy to configure
and analyze data.
• Automated Data Visualization Tools
• Jupyter.Org
• Grafana + InfluxDB
•Wolfram Alpha
•AT&T M2X
CONSIDERATIONS FOR DATA VISUALIZATION SYSTEMS

37. Data Recommendations
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

38. DATA RECOMMENDATION SYSTEMS
1. System processes data and
suggests one or more actions
2. Human intervenes and takes
action or redirects the decision
3. First popular in E-Commerce and
Marketing Systems, but applicable
elsewhere
18%
Likelihood to purchase a
related product based on
past history
25%
Category
81%

39. TYPES OF “PRODUCT RECOMMENDATION” SYSTEMS
MANUAL
CROSS-SELL
CROWD-
SOURCED
PERSONA-
BASED
RULE- &
ALGORITHM-
BASED

40. RECOMMENDATIONS - NOT JUST FOR E-COMMERCE
• Recommendations are applicable to nearly any
problem we’re solving with software
• Many examples of recommender tools are
popping up on Mobile
• Uses
• Prioritizing bugs based on crash report
frequency/geo/other factors
• Suggesting web-pages for optimization based
on automated funnel analysis
• Suggesting modification to watering
frequency based on soil-moisture readings
78%
Enterprise
Software
Developers
19%
Solo
Developers &
Entrepreneurs
{
{

41. • Note: not many tools exist outside of e-
commerce/marketing for general use
• HOWEVER, a few open-source options
and example applications do exist
• Consider looking at:
• LensKit
• PredictionIO
• Wikipedia SuggestBot
• Cyclopath/Cycloplan
CONSIDERATIONS FOR RECOMMENDER SYSTEMS

42. Human-Directed
Systems
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

43. HUMAN-DIRECTED SYSTEMS
1. System provides capability for
defining rules or conditions in the
system
2. Includes
• Rules & Workflow
• Triggers
• Data Tagging/Categorization
P
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44. EXAMPLES OF HUMAN-DIRECTED SYSTEMS
Tagging (RunScribe)
Triggers (AT&T M2X)
Rules & Workflow (NodeRed)

45. Learning Systems
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

46. LEARNING SYSTEMS
1. Can apply insights based on
publicly-available information or
history
2. Takes action without intervention
3. Informs a human after the fact,
who can refine and adjust the
parameters for the next decision
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47. • This is the wild-west of insight
• There are few tools available today to
support this, out of the box
• Start with your recommendation and
visualization tools and build from there
• Learning Systems Require:
• Enough Data Volume to be
meaningful
• A Facility for automated decision-
making and refinement (incl. manual)
•Rules++
CONSIDERATIONS FOR LEARNING SYSTEMS
1) 2 3] ^

48. Building Decision-
Making Systems
$ % & ' ( $ ) * + , !
% & ' ) $ % ! ' ( '
% & ' ) $ % ! ' ( '

49. “REAL-TIME”
• Real-time means…
• Speed of entry
• Speed of insight
• Speed of action
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50. BUILDING “REAL-TIME” APPS - TIPS
1. Use tools and transports that make real-
time simple
• Meteor, Modulus.io & MongoDB
2. Push insights outward
• Push Notifications
• Triggers to other systems
3. Build with rules and workflow in mind
• Rules defined in advance
• Workflow between systems
4. Use public data to make decisions
simple (or automatic)
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51. Create an Arduino-based weather station that:
• Monitors environmental data from on-board sensors
• Temp, pressure, wind speed & direction, rainfall, soil moisture, etc.
• Powered by a small solar panel with integrated battery monitoring
• Posts all data to a cloud-hosted source
• Can respond to environmental conditions and external inputs
SCENARIO. GARDEN WEATHER STATION
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52. Ingestion
• Store environmental data as a ContentType in Telerik Backend
Services
• Store Battery Data (Charge, Voltage) as a separate
ContentType
Visualization
• Create a monitoring dashboard that shows summary views for
environmental and battery data
• Built with
• Node.js, Express and Modulus (hosting)
• Meteor for real-time communication
• Kendo UI for web dashboard widgets/UI
• NativeScript and UI for NativeScript for
Mobile
GARDEN WEATHER STATION - INGESTION AND VISUALIZATION
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53. Recommendations & Triggers
• Send a notification when:
• Soil moisture falls below a certain level
• Rainfall is greater than a certain amount
• The temperature is nearing freezing
• Device battery drops below a threshold or isn’t charging
enough over time
• Recommendations can be combined with triggers to suggest
action
Human-Directed (Rules)
• Instruct the device to enter “low-power” mode when battery
level is low
• Set thresholds for soil moisture, rainfall and wind speed
• Trigger sprinkler system when moisture-level is low
• Delay sprinkler system when rainfall surpasses a threshold
GARDEN WEATHER STATION - RECOMMENDATIONS & RULES
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54. GARDEN WEATHER STATION - LEARNING SYSTEMS
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Learning Systems
• Set a watering schedule for the week/day based on public
forecast data
• Combine history and rules to adjust moisture and rain delay
thresholds
• Re-position the solar panel (w/ servo) to obtain optimal charge
• Self-monitor sensors and hardware and send notifications
when future malfunctions are likely
∠

55. THE CONTINUUM OF DATA INSIGHT
INSIGHT
DIRECTION
Human defines rules
or conditions in the
system in advance;
System takes action
when conditions are
met.
RECOMMENDATION
System makes
suggestions based
on data & a
human takes
action
VISUALIZATION
System provides
visuals to help you
reason about
data.
COLLECTION
System gathers
data and stores it
in some location
LEARNING
Systems that take
action based on
past behavior,
public information
or other factors
Real-Time
SPEED OF DECISION-MAKING
SPEED OF DATA INGESTION

56. THANK YOU!
@BrandonSatrom
? Brandon.Satrom@Telerik.com
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