Analytics at Motorola

Analytics at Motorola, a journey to enable

self-service using Google tools

Patrick Deglon, June 2015

After a PhD in Particle Physics and 10 years at the University of Geneva studying the creation of the Universe, Patrick spent the next decade driving business insights at eBay and Motorola Mobility.

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.

At Motorola Mobility, he raised the bar in Analytics and on-board open Google tools and technologies including Google Docs, Big Query, App Engine and Compute Engine.

In June 2015, he founded Deglon Consulting to help companies adopt the latest technologies in Cloud computing as well as integrate sound analytical methodologies to measure business impact and marketing incrementality.

He is married with two kids and recently moved to Sarasota, Florida

Patrick Deglon Bio

2

Agenda

● Industry overview (mobile)

● Motorola Example

o Daily Activations Report (BQ + Spreadsheet)

o Big Feed (Big Query ETL)

o Moto Insights (BQ + gChart + gDrive + GA)

o Moto Monitor (BQ usage & cost optimization)

o Self-Serve Email (Next gen of self-service)

● Q & A

3

Imagine a world...

… where information is ubiquitous (anytime & anywhere)

… where buildings can recognize your presence

… where even streetlights are connected to Internet

Welcome to a

digital world

Mobile was a revolution, but Mobile is an outdated concept.

The Digital World (Cloud, Internet, World’s Information, Digital Personal Assistant…) will be available everywhere: phones, watches, glasses, cars, appliance, microchip implant, ...

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Evolution of mankind

1973: First hand-held portable telephone1989: Web proposal2009: First microchip implant...

Homo Sapiens

Homo Technicus

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o Moto Insights (BQ + Charts + gDrive + GA)



● Q & A

Agenda

11

1928 1936 1943 1955 1969World’s first

portable FM two-way

radio

Motorola introduced

Police Cruiser Radio Receiver

World’s first high-power transistor

in commercial production

First words from moon relayed via Motorola

radio

Motorola: 80+ YEARS OF INNOVATION1947 1963

World’s first truly

rectangular color TV

tube

1983 19961991World’s first commercial handheld

cellular phoneDynaTAC 8000X

weighed 28-ounces (794 grams)

World’s first GSM cellular

system

World’s first dual-mode

cellular phone

The 3.1 ounce (88 grams) StarTac©

wearable cellular phone is the

world’s smallest and lightest

1990World’s first

HDTV technical standard

1973Demonstrated prototype of the DynaTAC

portable cellular system

World's first handset , iDEN i1000plus, to

combine a digital phone, two-way radio, Internet microbrowser,

e-mail, fax and two-way messaging

1999 2000World’s first

general packet radio service

(GPRS) wireless phone for always on

Internet access

2004 2006 2009Iconic RAZR V3 wireless

phone introduced

MING smart phone recognizes

10,000+ handwritten characters

from Chinese alphabet

20122002World’s first

wireless cable modem gateway

introduced

Motorola DROID #1 on

Time’s Top Ten of 2009

2013 2015Launch Moto X, Moto G

Fast upgrades

Galvin Manufacturing Corp

Moto EMoto 360

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Motorola Cloud Customers Ecosystem

WebProduct

Sales

Business Operation

CustomerSupport

Partners & Carriers

MotoMaker

Consumers: Phones, Wearables &

Companion ProductsInternal Business

Teams

Marketing

FinanceEngineering

Motorola Cloud

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Motorola Cloud Applications & Services

Infrastructure as a Service

On-Device Applications & Services

Web Applications & Software as a Service

Platform as a Service

Cloud Applications & Services

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Google Cloud Platform (GCP) 101

GAEGoogle App Engine

GCEGoogle Compute Engine

GCSGoogle Cloud Storage

BQGoogle Big Query

GAGoogle Analytics

Virtual Linux/Windows Server

Web Server with automatic scaling

“FTP”

Big Data warehouse, public version of Dremel that is powering Google Search

Website, Mobile and IoT tracking & analysis

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Confluence’s Data Wiki

OSQA’s FAQ (“Stackoverflow”)

Data & Analytics Summits

Solution Engineering

Analytics EcosystemDevice Instrumentation

Check it out:→ Android Settings → Motorola Privacy

⌧ Help Improve Motorola Products (On/Off) ⌧ Moto Care (On/Off)

Motorola Big Data Environment

Motorola Cloud (GAE/GCE) Big Data (BQ)

Moto Insights (GAE)

BigFeed (GAE)

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● Q & A

Agenda

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How to provide a global source of truth,

available on any form factor with an outreach

mentality?

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 only (through VPN)- No mobile form factor

Daily Activations Report

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Motorola Factory# Shipments

Distribution Channels# Sales

First Usage# Activations

Simplified Business Flow

Key Performance Indicators

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Motorola Factory# Shipments

Distribution Channels# Sales

First Usage# Activations

Google BigQuery

MotorolaCloud

Insights

...

Data Flow

Motorola IT

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Demo Daily Report: Final Email

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Features of 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

Demo: https://docs.google.com/spreadsheet/ccc?key=0AjgpL8JvOwsvdDJjV0s3NFphS3RnRzBXakNpZUR1ZGc#gid=21

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https://docs.google.com/a/motorola.com/spreadsheet/ccc?key=0AgldkCMfisBTdF83VVJqdFVEZFZiZjgyTWJNdzRNblE&usp=drive_web%23gid=21

https://docs.google.com/a/motorola.com/spreadsheet/ccc?key=0AgldkCMfisBTdF83VVJqdFVEZFZiZjgyTWJNdzRNblE&usp=drive_web%23gid=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)

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Step 1: Create a backbone table

SELECTCAL_DT,Country

FROM

ON A.Dummy=B.DummyWHERE

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 DummyFROM [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

BA

CK

UP

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Step 2: Calculate KPI value over time

SELECTCAL_DT,Country,

‘Phone 123’ AS Model,INTEGER(Scale*

EXP(-POW( -150,2)/2/POW(75,2))/(75*SQRT(2*PI()))

) AS Daily_ActivationsFROM

[motorola.com:sandbox:demo.backbone]

motorola.com:sandbox:demo.baseline:

DATEDIFF(TIMESTAMP(CAL_DT),TIMESTAMP(Launch_Date))

...

Normal Distribution:

BA

CK

UP

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Step 3: Add Random Noise

SELECTCAL_DT,Model,

Country,INTEGER(

Daily_Activations + SQRT(Daily_Activations) *SQRT(-2*LN(RAND()))*COS(2*PI()*RAND())

) AS Daily_ActivationsFROM

[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

BA

CK

UP

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Step 4: Final Pivot for reportSELECT

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]

WHERECAL_DT<CURRENT_DATE()

GROUP BY 1ORDER BY 1 DESC

BA

CK

UP

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Demo Daily Report: New Menu Item

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Demo Daily Report: Edit Code (App Script)

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Demo Daily Report: Example for adding a new menu item

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Demo Daily Report: Key Query (pivot)

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Demo Daily Report: Running the query

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Demo Daily Report: Parsing the results

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Demo Daily Report: Data Sheet

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Demo Daily Report: Summary sheet

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Demo Daily Report: Preparing email

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Demo Daily Report: Email template sheet

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Demo Daily Report: Preparing email

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Demo Daily Report: Sending email

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Demo Daily Report: Signal vs Noise and Main Drivers

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Demo Daily Report: Key Milestones

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Demo Daily Report: Final Email

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Demo Daily Report: Scheduling

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● Demos






● Wish List

● Q & A

AgendaShare of time

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Big Query to Big Query ETL

BigFeedCheck-in

Data(PB)

StaggingData(TB) Reporting

Data(GB)

BigFeed

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● Demos






● Q & A

Agenda

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Moto Insights: Democratizing Business Intelligence

Old Analytics Portal

1. Require VPN2. New Report takes weeks to develop3. New portal features takes months4. Tableau incompatibility with BigQuery5. Reports are produce by a centralized team6. Role management is becoming out of

control

1. Global access with App Engine2. Programmatic approach (SQL + metadata)3. Lightweight App Engine framework

(Go/AngularJS) using G API4. Google Charts and native BQ SQL5. Google Drive API6. Google Groups

Moto Insights (GAE+BQ+gChart)

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Product Architecture

Big Querydatasets

Moto

Insights

AppEngine

Google Analytics

data

Device Instrumentation

App Engine

Tableaureports

Big FeedApp

Engine

Users, ReportsDatastore

Goo

gle

Driv

e

Users

Machine Learned

Models

gCha

rt +

D3

+ Ta

blea

u AP

I

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Features of Moto Insights

● Responsive design● Report metadata, Chart widget metadata● Report sharing● Report viewing● Google Drive integration

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Demo Moto Insights: Main menu & Responsive Design

responsive design

Front-end based on Bootstrap CSS and AngularJS

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Demo Moto Insights: Report Details

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Demo Moto Insights: 1st Chart Details

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Demo Moto Insights: Chart Types

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Demo Moto Insights: Dummy Example

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● Demos






● Q & A

Agenda

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Enlightenment Questions for an AnalystWhen was this BigQuery table last refreshed?How often is it refreshed?How was it created? Which underlying data sources/tables is it using?Who created this table?Who knows how to use this table?Where can I find this great query I ran?Who knows how to use this tag?

How much bandwidth am I using in BigQuery?How much space are my tables using?How much does my usage of BigQuery cost?

www.holymolecartoon.com

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How to track Big Query usage?Google does not provide a data feed on Motorola’s usage of BigQuery. However three API can help us:

bigquery.jobs.list

List all the Jobs in a specified project.

Note: use projection = full to get email of user

bigquery.jobs.get

Retrieve the specified job by ID.

We created an App Engine (Moto Monitor) to crawl Google API so we can recursively collect all queries ran (since mid 2013; for a specific list of projects). The queries are parsed to extract underlying tables used, and the data is stored in the App Engine datastore as well as in Big Query through the streaming API (every 15 minutes).

bigquery.projects.list

List all (visible) projects

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Product Architecture

Moto Monitor App Engine (Web Service)

default module

web pages, CSS, JS, etc.

bqusage module

user requests

worker module

CRON jobs Big Querydatastorequeries/tables

information

GoogleAPIs

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Moto Monitor Browsing

Browse a table

Browse a job

Browse a flow

https://moto-monitor.appspot.com/bq/info/{long table name}

e.g. https://moto-monitor.appspot.com/bq/info/motorola.com:analytics-data:activations.gcp_activations_shipments

https://moto-monitor.appspot.com/bq/jobinfo/{long job name}

e.g. https://moto-monitor.appspot.com/bq/jobinfo/bold-site-589:job_pz_J3anj2HjIz5AEX0_STXPtWb4

https://moto-monitor.appspot.com/bq/flow/{long table name}

e.g. https://moto-monitor.appspot.com/bq/flow/motorola.com:analytics-data:devices.asn_r12

Browse your usage

https://moto-monitor.appspot.com/bq/about/me

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https://moto-monitor.appspot.com/info/motorola.com:analytics-data:activations.gcp_activations_shipments

https://moto-monitor.appspot.com/jobinfo/bold-site-589:job_pz_J3anj2HjIz5AEX0_STXPtWb4

https://moto-monitor.appspot.com/bq/info/motorola.com:analytics-data:activations.gcp_activations_shipments

https://moto-monitor.appspot.com/bq/jobinfo/bold-site-589:job_pz_J3anj2HjIz5AEX0_STXPtWb4

https://moto-monitor.appspot.com/bq/flow/motorola.com:analytics-data:devices.asn_r12

https://moto-monitor.appspot.com/about/me


How often is activations.gcp_foundation3 refreshed?

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How is activations.gcp_foundation3 being populated?

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Example: 1000 files limit bug for BQ Load (Apr 17th)

SELECT STRING(MSEC_TO_TIMESTAMP(creationtime)) AS creationtime, id, User_email, DestinationProjectId, DestinationDatasetId, InputFiles FROM [moto-monitor:usage.bq_raw] WHERE creationtime>=TIMESTAMP_TO_MSEC(TIMESTAMP('2015-04-16 00:00:00')) AND JobType='Load' AND InputFiles>=1000ORDER BY creationtime

Project: motorola.com:ds-prodJob:job_qrH3tNUgKT_R84PKWJXM_AATRPMState: DONE

StatisticsCreationTime: Thu Apr 16 12:30:16 2015StartTime: Thu Apr 16 12:33:41 2015EndTime: Thu Apr 16 12:36:08 2015Gate Time: 205259Run Time: 146249TotalBytesProcessed: 0InputFileBytes: 134834356368InputFiles: 2048OutputBytes: 39957719968OutputRows: 89549136Dry Run flag: false

LoadAllowJaggedRows: falseAllowQuotedNewlines: falseIgnoreUnknownValues: falseLoad.MaxBadRecords: 0SkipLeadingRows: 0SourceFormat:NEWLINE_DELIMITED_JSONSourceUris: [gs://dspipeline-event-export/transfer/20150416_12/job_gdi_1429186260434*]

Project: motorola.com:ds-prodJob: job_XWekiaqjKnHg2kpZQZp_BJbclS0State: DONE

StatisticsCreationTime: Fri Apr 17 12:27:02 2015StartTime: Fri Apr 17 12:28:48 2015EndTime: Fri Apr 17 12:30:02 2015Gate Time: 105788Run Time: 74061TotalBytesProcessed: 0InputFileBytes: 67622530704InputFiles: 1000OutputBytes: 20037554506OutputRows: 44919153Dry Run flag: false

LoadAllowJaggedRows: falseAllowQuotedNewlines: falseIgnoreUnknownValues: falseLoad.MaxBadRecords: 0SkipLeadingRows: 0SourceFormat: NEWLINE_DELIMITED_JSONSourceUris: [gs://dspipeline-event-export/transfer/20150417_12/job_gdi_1429272660897*]

Moto Monitor portal Moto Monitor BQ data

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https://moto-monitor.appspot.com/bq/jobinfo/motorola.com:ds-prod:job_qrH3tNUgKT_R84PKWJXM_AATRPM

https://moto-monitor.appspot.com/bq/jobinfo/motorola.com:ds-prod:job_XWekiaqjKnHg2kpZQZp_BJbclS0

Which table are impacted by motorola.com:analytics-data:devices.asn_r12?

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Where can I find this great query I ran the other day?https://moto-monitor.appspot.com/bq/about/me

https://moto-monitor.appspot.com/bq/about/me?before=2014-12-09

https://moto-monitor.appspot.com/bq/about/me?before=2014-12-09&limit=500

https://moto-monitor.appspot.com/bq/about/me?before=1418223600&limit=500

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https://moto-monitor.appspot.com/bq/about/me?before=2014-12-09

https://moto-monitor.appspot.com/about/me?before=2014-12-09&limit=500


https://moto-monitor.appspot.com/bq/about/me?before=2014-12-09&limit=500

https://moto-monitor.appspot.com/about/me?before=1418223600&limit=500


https://moto-monitor.appspot.com/bq/about/me?before=1418223600&limit=500

Moto Monitor is available in BigQuery tooId STRING Unique Identifier for the job (ProjectId:JobId)

ProjectId STRING Project Id under which the job was run

JobId STRING Job Id

CreationTime INTEGER Unix Time when the job was submitted

StartTime INTEGER Unix Time when the job started

EndTime INTEGER Unix Time when the job finished

GateTime INTEGER Gating time in ms between CreationTime and StartTime

RunTime INTEGER Running time in ms between StartTime and EndTime

TotalBytesProcessed INTEGER Total Bytes scanned for the job

CacheHit BOOLEAN Boolean flag to indicate if cache was used

User STRING Email of user running the job

MD5 STRING MD5 of the full query

Query STRING Query truncated to 18,000 characters

Status STRING Status of the job (=DONE here)

AllowLargeResults BOOLEAN If true, allows the query to produce arbitrarily large result tables at a slight cost in performance. Requires destinationTable to be set.

Priority STRING Specifies a priority for the query. Possible values include INTERACTIVE and BATCH.

UseQueryCache BOOLEAN Boolean flag to indicate if cache was requested in the job

DestinationProjectId STRING Define the project where the results of the query will be written.

DestinationDatasetId STRING Define the dataset where the results of the query will be written.

DestinationTableId STRING Define the table where the results of the query will be written.

ErrorLocation STRING Specifies where the error occurred, if present.

ErrorMessage STRING A human-readable description of the error.

ErrorReason STRING A short error code that summarizes the error.

moto-monitor:usage.bq_raw

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https://bigquery.cloud.google.com/table/moto-monitor:usage.bq_raw

Who was using the tag MOT_DEVICE_STATS_L1 in the last 7 days?

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How much bandwidth am I using in BigQuery?Use the view moto-monitor:usage.bq_view

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https://bigquery.cloud.google.com/table/moto-monitor:usage.bq_view

Beyond Queries, we also scan Tables

bigquery.projects.list

List projects visible

bigquery.tables.list

List tables within a dataset

bigquery.datasets.list

List datasets within a project

bigquery.tables.get

Get details about a table

datastorequeries

information

user email

store details

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A snapshot of Table statistics is kept as wellmoto-monitor:usage.daily_table (daily snapshot) or moto-monitor:usage.snapshot (latest manual snapshot with self-destruction after 3 days)

CreationTime INTEGER Table Creation Time (Unix)

Description STRING Table Description

Etag STRING NULLABLE

ExpirationTime INTEGER Expiration Time (Unix)

FriendlyName STRING Friendly name

Id STRING Unique Id

LastModifiedTime INTEGER Last Modified Time (Unix)

NumBytes INTEGER Number of bytes

NumRows INTEGER Number of rows

Fields STRING Schema definition

ProjectId STRING Project Id

DatasetId STRING Dataset Id

TableId STRING Table Id

Type STRING TABLE or VIEW

View STRING View Query

User STRING Last user who populated

Query STRING Last query used to populate

JobId STRING Last Job Id to populate

RefreshedTime INTEGER Last time it was populated

SnapshotTime STRING Snapshot timestamp

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https://bigquery.cloud.google.com/table/moto-monitor:usage.daily_table

https://bigquery.cloud.google.com/table/moto-monitor:usage.snapshot

How did the size of a dataset grow over time?

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How much space are my tables using?

Bigquery Storing Cost = $0.02 per GB per month,

i.e. $6.83 per TB per day , i.e. $2,458 per TB per year

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How much does my usage of BigQuery cost?

$0.02 per GB per month$6.83 per TB per day

$2,458 per TB per yearStorage Cost

Query Cost $5 per TB$20,000 per month

for 5 GB/s unit,i.e. $1.58 per TB*

On-demand Reserved capacity

* Note: for continuous usage of the 5 GB/s bandwidth

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How much does my usage of BigQuery cost?Assuming that the Motorola bandwidth is elastic, i.e. we always pay for the optimal number of units (5 Gb/s), we can use $1.58 per TB as a proxy

Caveat: API Volume ~ Billing Volume

<> Real Volume Used

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Weekly Email to largest BQ users

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Usage statisticsCaveat:

API Volume ~ Billing Volume <> Real Volume Used

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Usage statisticsCaveat:


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BigQuery outage?Caveat:


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What’s next with Moto Monitor ?

Alert & Exceptions Report

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ExamplesData Issue (illustrative)

time (day)

# Active Users

Normal Band

Number of Active Users using their camera in US

Possible Root Causes

● Some files don’t get loaded properly in BigQuery, creating gaps in user count.

● The instrumentation changed on the device

● Customer behavior

Business Issue (real life)

# System

Restarts

Number of System Restarts in Brazil in Oct ‘14

Real life Root Cause

A buggy Android app (Color Notes) doesn’t handle the timezone change in Brazil properly, crashing the devices.

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Approach1. Define a multi-dimensional cubes with real data. For example: Day, Product, Market, # Users taking a picture

2. Each cell becomes then a time series

3. Clean the data (remove seasonality, weekday cycle and any other know perturbation)

* Note: (Bayesian likelihood with knowledge base)

4. Fit trend and establish volatility band (2 std deviations)

5. Measure variance versus prediction for each cell (e.g. market/product/metric) and trigger an exception if outside band

6. Collect all exceptions into a matrix and apply fuzzy logic* to propose potential root causes (prescriptive analytics)

mar

kets

BR

productsmetr

ics

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Exceptions Report POC

https://moto-monitor.appspot.com/fcst/matrixhttps://moto-monitor.appspot.com/fcst/matrix?d=2015-01-06

Note: During POC, access is strictly restricted

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https://moto-monitor.appspot.com/fcst/matrix?d=2015-01-06

https://moto-monitor.appspot.com/fcst/matrix?d=2015-01-06

Exceptions Report POCReal life example with Moto E in Spainhttps://moto-monitor.appspot.com/fcst/trend?market=Spain&product=Moto%20E

Trend: where we should have been

Actuals: where we are

Story

Investigating with the Spain GTM team, this large increase is seasonal and due to “The Three Kings Day” (Día De Los Reyes Magos) where sales are usually larger than pre-Holidays.

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https://moto-monitor.appspot.com/fcst/trend?market=Spain&product=Moto%20E

Demo Exception ReportDaily Email on Exceptions/Anomalies Online Report & Drilldown Immediate Learning & Findings

Juno Storm impact on Daily ActivationsDaily Activation WoW on Jan 27th 2014

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● Demos






● Q&A

Agenda

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How to democratize daily/weekly email

report with an App Engine solution?

Existing Situation

- Numerous teams use Spreadsheet to send weekly/daily email- Enable very agile development of email body- Ease of connection to Big Query- Can’t enable easily customization and open-rate tracking at a user level- Can’t leverage advance statistics (R in GCE)

Self-Service Email System

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Self-Service Email SystemEmail Widget

HTML Header template

HTML Body template

HTML Footer template

SQL to produce data for Body

gDrive Objects (image, attachment)

Underlying Widgets

App

Eng

ine

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● Demos






● Q & A

Agenda

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Analytics at Motorola

Data & Analytics

Transcript of Analytics at Motorola