collective |

the audience
engine®

The Rise of the Data Scientist
10/24/2013
Overview
1. CHALLENGES FACING MARKETERS

2. THE RISE OF THE DATA SCIENTIST
3. BUILDING A DATA SCIENCES TEAM
Top Challenges
Facing Marketers
Data explosion

71%

Social media

68%

Growth of channel and device choices

65%

Shiftin...
Data
Explosion
VOLUME

THE DIGITAL UNIVERSE:

Exponential growth

50-fold Growth from the beginning of 2010-2020

EXABYTES...
Data
Explosion
AUDIENCE
(Who)

VOLUME
Exponential growth

VARIETY
Diversity of sources

VELOCITY
Millisecond decisions

TI...
Data
Explosion
VOLUME

CREATIVE
CDN

Exponential growth

Serve
Ad

Millisecond decisions

VERACITY
Varying data quality

(...
Data
Explosion
VOLUME
Exponential growth

Age 65-70

PROVIDER #3

VARIETY
Diversity of sources

Age 20-25

VELOCITY

Age 6...
Growth of Channel
& Device choices

EARLY MORNING

DAYTIME

EARLY FRINGE

Unified view of the consumer?
Choreographed expe...
Rise
Of the Data Scientist
Population Growth

Data
Scientist

Chief
Marketing
Officer

2005

2006

Source: Google Trends

2007

2008

2009

2010

201...
Purpose
Algorithms
Data

Computers

Data
Scientist

Visualizations

Models

Insights

Predictions
Evolutionary Tree
Logician
Engineer

Data Admin.

Data Capture

Computer
Programmer

Optimization

Artificial Intel.

Data...
Demographics

MOSCOW
LONDON
NEW YORK
SAN
FRANSISCO

BANGALOR
E

158
46

MALE
vs.
FEMALE
INDEX
AVERAGE AGE INDEX

EDUCATION...
Tool Usage
MODERN

OPERATING
SYSTEM

DATA
MANAGEMENT

LEGACY

LINU
X

Windows

HADOOP

Oracle

MODERN TOOLS

Open source

...
Critical Weaknesses
CONFIRMATION
Seeking what you believe
INFORMATION
Sometimes, data is useless
SAMPLE SIZE
Mistaking noi...
Building a Data Science team
Where to put them
DIVISIONAL
Chief Data Officer

Data
Management

Data
Governance
Data
Sciences

TECHNOLOGY
Chief Technolo...
Levels of experience
JUNIOR

SENIOR

PRINCIPAL

ROCK STAR

Must
be mentored

Requires
close supervision

Driven &
accompli...
Where they Congregate

EVENTS:

ONLINE:
Defining Success

INSIGHTS
Influencing big decisions
ALGORITHMS
Automating micro decisions
OPERATING CHANGES
Redesigned pr...
Thank You!
CONTACT: jstanley@collective.com
Jeremy Stanley - The Rise of the Data Scientist -Collective
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Jeremy Stanley - The Rise of the Data Scientist -Collective

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Jeremy Stanley, Chief Technology Officer, Collective

His presentation at the first Data Summit in NYC. More information: http://datasummit.aaaa.org/

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  • MB Notes:Add a graphic. Might be too genericCome back to title
  • Icons
  • From http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03433usen/GBE03433USEN.PDFFace-to-face interviews with 1,734 CMOs, spanning 19 industries and 64 countriesIBM#1 Data explosion#3 growth channel and device choices----- Meeting Notes (10/10/13 13:49) -----DARK GRAY AND LIGHT GRAY FOR GRAPH
  • http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf4VsData is growing
  • Enormous variety of data coming from various sources
  • RTB FrameworkAll happens in once second
  • Data from proprietary study of online age data provider accuracyBetter/more clear/simpler way to explain these?Need to add color
  • ThematicAudienceframentations----- Meeting Notes (10/10/13 13:49) -----ORANGE IS BAD CANT SEE SHIT
  • http://connectedthefilm.com/wp-content/uploads/2011/01/EvolutionofManStill.jpg----- Meeting Notes (10/10/13 13:49) -----TRY TO FIND BETTER STUFFFIX DROP SHADOW
  • http://www.google.com/trends/explore?q=data+scientist+chief+marketing+officer#q=chief%20marketing%20officer%2C%20%20data%20scientist&cmpt=qhttp://radar.oreilly.com/2011/09/building-data-science-teams.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+oreilly%2Fradar%2Fatom+%28O%27Reilly+Radar%29&utm_content=My+Yahoohttp://www.indeed.com/jobanalytics/jobtrends?q=%22Data+Scientist%22&l=%5D
  • Vingettes around images
  • Vingettes around images
  • ----- Meeting Notes (10/10/13 13:49) -----JAMMING GOIN ON IN THE LEFT
  • Strong point
  • http://en.wikipedia.org/wiki/List_of_biases_in_judgement_and_decision_makingCognitive BiasesTendencies to think in *certain* waysOf the 94 decision making biases on Wikipedia, these are most risky for data scientists:Confirmation bias – seeking what you believeInformation bias – data for data’s sakeSample size bias – mistaking noise for real patternsNormalcy bias – missing the ‘black swan’ possibilitySelection bias – ignoring how the data was collected
  • http://connectedthefilm.com/wp-content/uploads/2011/01/EvolutionofManStill.jpg
  • Two-thirds of firms report having appointed a senior figure such as a :chief data officer” to lead management and analytics in the last 18th monthshttp://www.accenture.com/SiteCollectionDocuments/us-en/landing-pages/analytics-in-action/accenture_analytics_in_action_survey.pdf
  • Vingettes around images
  • Kaggle main focus
  • Icons
  • Cover needs to work with the presentationCombine the map and demo info into one slideResults
  • Jeremy Stanley - The Rise of the Data Scientist -Collective

    1. 1. collective | the audience engine® The Rise of the Data Scientist 10/24/2013
    2. 2. Overview 1. CHALLENGES FACING MARKETERS 2. THE RISE OF THE DATA SCIENTIST 3. BUILDING A DATA SCIENCES TEAM
    3. 3. Top Challenges Facing Marketers Data explosion 71% Social media 68% Growth of channel and device choices 65% Shifting consumer demographics 63% Financial constraints 59% Decreasing brand loyalty 57% DATA SCIENCE IS CRITICAL TO ADDRESSING THESE Source: IBM CMO C-Suite Series, “From Stretched to Strengthened”
    4. 4. Data Explosion VOLUME THE DIGITAL UNIVERSE: Exponential growth 50-fold Growth from the beginning of 2010-2020 EXABYTES 40,000 VARIETY 30,000 Diversity of sources VELOCITY Millisecond decisions YOU ARE HERE VERACITY 10,000 Varying data quality 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Source: IDC, “The Digital Universe” 20,000
    5. 5. Data Explosion AUDIENCE (Who) VOLUME Exponential growth VARIETY Diversity of sources VELOCITY Millisecond decisions TIME CREATIVE (When) (What) VERACITY Varying data quality PLACEMENT (Where)
    6. 6. Data Explosion VOLUME CREATIVE CDN Exponential growth Serve Ad Millisecond decisions VERACITY Varying data quality (1,000 ms) DEVICE Diversity of sources 1 Second (200 ms) VARIETY VELOCITY ADVERTISE R AD SERVER Elapsed Time Decide Bid (50 ms) PUBLISHE R AD SERVER EXCHANGE Real Time Bidding
    7. 7. Data Explosion VOLUME Exponential growth Age 65-70 PROVIDER #3 VARIETY Diversity of sources Age 20-25 VELOCITY Age 65-70 Millisecond decisions VERACITY PROVIDER #2 Varying data quality Age 20-25 Age 20-25 PROVIDER #1 Age 65-70 Source: Collective
    8. 8. Growth of Channel & Device choices EARLY MORNING DAYTIME EARLY FRINGE Unified view of the consumer? Choreographed experiences? Consistent measurement? PRIME TIME LATE NIGHT
    9. 9. Rise Of the Data Scientist
    10. 10. Population Growth Data Scientist Chief Marketing Officer 2005 2006 Source: Google Trends 2007 2008 2009 2010 2011 2012 2013
    11. 11. Purpose Algorithms Data Computers Data Scientist Visualizations Models Insights Predictions
    12. 12. Evolutionary Tree Logician Engineer Data Admin. Data Capture Computer Programmer Optimization Artificial Intel. Data Developer Data Hacker Science Mathematics Data Mining Visualization Statistics Machine Learning Data Scientist Reporting Business Intelligence Detective Historian Domain Expert Business Analytics
    13. 13. Demographics MOSCOW LONDON NEW YORK SAN FRANSISCO BANGALOR E 158 46 MALE vs. FEMALE INDEX AVERAGE AGE INDEX EDUCATION INDEX 30 0 20 0 10 0 0 15 0 10 0 50 NO COLLEGE COLLEGE GRAD SCHOOL 0 <18 18-24 25-34 3544 45-54 55-64 65+ Source: Google Trends, Data Science Central, “Data Scientist Demographics”
    14. 14. Tool Usage MODERN OPERATING SYSTEM DATA MANAGEMENT LEGACY LINU X Windows HADOOP Oracle MODERN TOOLS Open source Community supported DATA ANALYSIS R SAS State of the art Steeper learning curve VISUALIZATION TECHNOLOGY GGPLOT Excel
    15. 15. Critical Weaknesses CONFIRMATION Seeking what you believe INFORMATION Sometimes, data is useless SAMPLE SIZE Mistaking noise for real patterns NORMALCY Ignoring the „black swan‟ possibility SELECTION Ignoring how the data was collected Source: Wikipedia, “List of biases in judgment and decision making”
    16. 16. Building a Data Science team
    17. 17. Where to put them DIVISIONAL Chief Data Officer Data Management Data Governance Data Sciences TECHNOLOGY Chief Technology Officer Product Management Engineering & Operations Data Sciences FUNCTIONAL Chief Marketing Officer Marketing Operations Marketing Strategy Data Sciences PRO Focus & Sponsorship PRO Tight integration into platforms PRO Connection to applications CON Data must be a top 3 board priority CON Weak business unit connections CON Limited scope and potential for bias
    18. 18. Levels of experience JUNIOR SENIOR PRINCIPAL ROCK STAR Must be mentored Requires close supervision Driven & accomplished Transformational Impact 50% 35% 14% 1% COMP ABILITY POPULATION % CAT ANALOGY:
    19. 19. Where they Congregate EVENTS: ONLINE:
    20. 20. Defining Success INSIGHTS Influencing big decisions ALGORITHMS Automating micro decisions OPERATING CHANGES Redesigned processes & technology TRANSFORMATIONAL New products or strategies
    21. 21. Thank You! CONTACT: jstanley@collective.com

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