Reporting at Motorola - Predictive analytics & business insights 2014

motorola confidential
Reporting at Motorola
Predictive Analytics &
Business Insights 2014
http://www.gatewayanalyticsnetwork.com/b122-home
Dr Patrick Deglon
Director of Engineering,
Analytics Area Tech Lead
Motorola Mobility
pdeglon@motorola.com
pdeglon

...
Overview of Motorola Business
About Us
Motorola exists to invent, build and deliver
the best mobile devices on the planet,
improving the lives of millions of people.

Motorola
1928
“Galvin
Manufacturing
Corporation”
created, producing
battery eliminators
1940
First walkie-
talkie which
was vital to
Allied
communication
“one small step for
man, one giant leap
for mankind” through a
Motorola transceiver
1969 1973 2011 20141986
Invented six
sigma quality
improvement
process
first private handheld
mobile phone call;
world's first
commercial cellular
device
1983
world's first
handset to
combine a Linux
operating system
and Java
technology
2003
Source: Wikipedia
Motorola splits into
Motorola Mobility &
Motorola Solutions

•  Business overview
•  Demos
–  Daily Activations Report
–  Moto Insights
–  Drive Insights
Agenda

Daily Activations Report
How to provide a global
source of truth and start
to provide insights from
data points?
Existing Situation
- Numerous (conflicting) sources of truth
- Too many variations of same data cube
- “Table in your face” approach
- No global business definition
- No curation of manually entered data
points
- Report accessible on an internal portal
(through VPN)
- No mobile form factor

Key Performance Indicators
Motorola Factory
# Shipments
Distribution Channels
# Sales
First Usage
# Activations
Simplified Business Flow

Data Flow
Motorola Factory
# Shipments
Distribution Channels
# Sales
First Usage
# Activations
Google BigQuery
Motorola
Cloud
Insights
...

Demo Daily Report: Final Email

Google: A galaxy of open APIs and Tools…
App EngineBig Query
Compute Engine
Users Cloud Messaging
Analytics
Maps
Visualization
Data Store
Cloud Storage
Cloud SQL
Drive
Docs
Translate
Predict
Mail
Data Store
Task Queue
Memcache
URL Fetch
Cloud End Point
Channel
Java
Python
Go
Mail and many more ...
BACKUP

Demo Daily Report
●  Get data (pivot) from BigQuery
●  Spreadsheet magic
●  Insights: WoW trends with statistics test, Key driver for growth, Key
milestone, internal QA tests
●  Email
●  Embedded Chart
●  Scheduler
https://docs.google.com/a/motorola.com/spreadsheet/ccc?key=0AgldkCMfisBTdF83VVJqdFVEZFZiZjgyTWJNdzRNblE&usp=drive_web#gid=21

•  Assume sales follow a diffusion S-shape, i.e.
Description of the illustrative simulation
Marketing Word of mouth
ΔN
Nmax
N
•  Add random noise to theoretical daily activations
(Poisson)
•  Simulated daily activations (sales) for United States,
Canada, Brazil, India, Russia, China, Germany and
United Kingdom with various launch date per region
ΔN = a (Nmax - N) + b N (Nmax - N)

Step 1: Create a backbone table
SELECT
CAL_DT,
Country
FROM
ON A.Dummy=B.Dummy
WHERE
B.CAL_DT>=A.Launch_Date
motorola.com:sandbox:demo.backbone:
INNER JOIN
(
SELECT
Country,
CASE
WHEN Country IN ('United States','Canada') THEN '2013-08-01'
WHEN Country IN ('Brazil','Russia','India','China') THEN '2013-10-01'
ELSE '2013-12-01'
END AS Launch_Date,
GDP_USD/1e7 AS Scale,
1 AS Dummy
FROM
[motorola.com:sandbox:pdeglon.countries]
WHERE
Country IN ('United States','Canada','Brazil','Russia','India',
'China','Germany','United Kingdom')
) AS B
(
SELECT
CAL_DT,
1 AS Dummy
FROM
[motorola.com:sandbox:pdeglon.calendar]
) AS A
X
BACKUP

Step 2: Calculate KPI value over time
SELECT
CAL_DT,
Country,
‘Phone 123’ AS Model,
INTEGER(Scale*
EXP(-POW( -150,2)/2/POW(75,2))
/(75*SQRT(2*PI()))
) AS Daily_Activations
FROM
[motorola.com:sandbox:demo.backbone]
motorola.com:sandbox:demo.baseline:
DATEDIFF(TIMESTAMP(CAL_DT),TIMESTAMP(Launch_Date))
...
Normal Distribution:
BACKUP

Step 3: Add Random Noise
SELECT
CAL_DT,
Model,
Country,
INTEGER(
Daily_Activations + SQRT(Daily_Activations) *
SQRT(-2*LN(RAND()))*COS(2*PI()*RAND())
) AS Daily_Activations
FROM
[motorola.com:sandbox:demo.baseline]
motorola.com:sandbox:demo.simulation:
Normal (Gaussian) Random Number
(mu=0, sigma=1)
(pseudo) Poisson distribution for
N=Daily_activation
BACKUP

Step 4: Final Pivot for report
SELECT
CAL_DT,
SUM(Daily_Activations) AS Total,
SUM(CASE WHEN Country IN ('United States','Canada') THEN Daily_Activations ELSE 0 END) AS NA,
SUM(CASE WHEN Country IN ('Brazil','Russia','India','China') THEN Daily_Activations ELSE 0 END) AS BRIC,
SUM(CASE WHEN Country IN ('Germany','United Kingdom') THEN Daily_Activations ELSE 0 END) AS EU,
SUM(CASE WHEN Country='United States' THEN Daily_Activations ELSE 0 END) AS UnitedStates,
SUM(CASE WHEN Country='Canada' THEN Daily_Activations ELSE 0 END) AS Canada,
SUM(CASE WHEN Country='Brazil' THEN Daily_Activations ELSE 0 END) AS Brazil,
SUM(CASE WHEN Country='Russia' THEN Daily_Activations ELSE 0 END) AS Russia,
SUM(CASE WHEN Country='India' THEN Daily_Activations ELSE 0 END) AS India,
SUM(CASE WHEN Country='China' THEN Daily_Activations ELSE 0 END) AS China,
SUM(CASE WHEN Country='Germany' THEN Daily_Activations ELSE 0 END) AS Germany,
SUM(CASE WHEN Country='United Kingdom' THEN Daily_Activations ELSE 0 END) AS UnitedKingdom
FROM
[motorola.com:sandbox:demo.simulation]
WHERE
CAL_DT<CURRENT_DATE()
GROUP BY 1
ORDER BY 1 DESC
BACKUP

Demo Daily Report: New Menu Item

Demo Daily Report: Edit Code (App Script)

Demo Daily Report:
Example for adding a new menu item

Demo Daily Report: Key Query (pivot)

Demo Daily Report: Running the query

Demo Daily Report: Parsing the results

Demo Daily Report: Data Sheet

Demo Daily Report: Summary sheet

Demo Daily Report: Preparing email

Demo Daily Report: Email template sheet

Demo Daily Report: Sending email

Demo Daily Report: Signal vs Noise and Main Drivers

Demo Daily Report: Key Milestones

Demo Daily Report: Scheduling

Moto Insights
How to provide insights to
executive-on-the-go with a
robust system where new report
take seconds to create?
Existing Situation
- Require VPN
- New Report take weeks
- New features take
months
- Issue Tickets come
weekly

Demo Moto Insights
Moto Insights
App Engine
Data Source:
Big Query
Report
Meta Data:
Datastore
Tracking:
Datastore
Users Access:
Google Users
(email) +
Datastore (role)
Moto Insights
Web portal
Moto Insights
Android App

Demo Moto Insights:
Main menu & Responsive Design
responsive
design

Demo Moto Insights: Report Details

Demo Moto Insights: 1st Chart Details

Demo Moto Insights: Chart Types

Demo Moto Insights: Dummy Example

Drive Insights
How to democratize Analytics
within the company while
maintaining quality (data &
insights) as well as maintain Big
Data usage under control?
Existing Situation
- Reports are produce by a
centralized team
- Role management is becoming out
of control
- Product teams are complaining to
have to run SQL repeatedly on
BigQuery console

Demo Drive Insights (v2)
Drive Insights
App Engine
Data Source:
Big Query
Data Source:
Google Analytics
iFrame Source:
Tableau Server
iFrame Source:
Google Documents
Data Source:
Spreadsheet & CSV
Report
Meta Data:
Google Drive
(Text file with
JSON)
Report
Meta Data:
Datastore
(Report copy &
usage tracking)
Users Access
Control:
Google Users +
Drive Sharing
Google Drive
Moto Insights
Android App
Drive Insights
Web portal

Demo Drive Insights: New document
type

Demo Drive Insights: Report creation

Demo Drive Insights: 1st Chart Details

Demo Drive Insights: 2nd Chart Details

Demo Drive Insights: Chart Types

Demo Drive Insights: SQL Editor (CodeMirror)

Demo Drive Insights:
Report document in your Google Drive

Demo Drive Insights: Dummy Example

Demo Drive Insights: Drive Sharing

Q&A
?
Dr Patrick Deglon
Motorola Mobility
pdeglon@motorola.com
pdeglon

In this presentation, Patrick Deglon will share his learnings and provide best
practices when using open Google tools & API. He will present his daily email
report that hundreds of key Motorola stakeholders are receiving to drive the
business, as well as a mobile solution based on the latest web technologies,
including Google Visualization, Bootstrap CSS and many of the Google APIs
(Gmail, BigQuery, Analytics, Drive, App Engine, Users authentication, etc.).
Abstract

Dr Patrick Deglon
Motorola Mobility
With a PhD in Particle Physics, Patrick Deglon spent the last decade driving business
insights at eBay and at Motorola Mobility, a Google company.
At eBay, he led significant improvements in marketing effectiveness by developing
methods to measure incremental sales, and by running large scale experiments on
Internet marketing channels.
He joined Motorola Mobility in 2013 to help raise the bar in Analytics and on-board open
Google tools and technologies. He is now the Area Tech Lead for Analytics within the
Cloud Services organization.
He is married with two kids and enjoys his town of Campbell, CA.
Bio

Reporting at Motorola - Predictive analytics & business insights 2014

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