jeremy stanley - the rise of the data scientist -collective
DESCRIPTION
Jeremy Stanley, Chief Technology Officer, Collective His presentation at the first Data Summit in NYC. More information: http://datasummit.aaaa.org/TRANSCRIPT
col lective | t h e a u d i e n c e e n g i n e ®
The Rise of the Data Scientist
10/15/13
1. CHALLENGES FACING MARKETERS
2. THE RISE OF THE DATA SCIENTIST
3. BUILDING A DATA SCIENCES TEAM
Overview
Top Challenges Facing Marketers
Source: IBM CMO C-Suite Series, “From Stretched to Strengthened”
Data explosion
Social media
Growth of channel and device choices
Shifting consumer demographics
Financial constraints
Decreasing brand loyalty
71%
68%
65%
63%
59%
57%
DATA SCIENCE IS CRITICAL TO ADDRESSING THESE
Source: IDC, “The Digital Universe”
DataExplosion
VOLUMEExponential growth
VERACITYVarying data quality
VARIETYDiversity of sources
VELOCITYMillisecond decisions
YOU ARE HERE
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
40,000
30,000
20,000
10,000
THE DIGITAL UNIVERSE: 50-fold Growth from the beginning of 2010-2020
EXABYTES
DataExplosion
VOLUMEExponential growth
VERACITYVarying data quality
VARIETYDiversity of sources
VELOCITYMillisecond decisions
AUDIENCE(Who)
PLACEMENT(Where)
TIME(When)
CREATIVE(What)
1 Second(1,000 ms)
Elapsed Time
DE
VIC
E
DataExplosion
VOLUMEExponential growth
VERACITYVarying data quality
VARIETYDiversity of sources
VELOCITYMillisecond decisions
EXCHANGEPUBLISHE
R AD SERVER
Real Time Bidding
ADVERTISER AD SERVER
CREATIVE CDN
Serve Ad(200 ms)
Decide
Bid(50 ms)
Source: Collective
DataExplosion
VOLUMEExponential growth
VERACITYVarying data quality
VARIETYDiversity of sources
VELOCITYMillisecond decisions
PROVIDER #3
Age 20-25
Age 65-70
PROVIDER #1
PROVIDER #2
Age 65-70
Age 20-25
Age 65-70
Age 20-25
Growth of Channel & Device choices
Unified view of the
consumer? Choreographed
experiences? Consistent measurement?
DAYTIME LATE NIGHTPRIME TIMEEARLY FRINGEEARLY MORNING
RiseOf the Data Scientist
Data Scientist
Chief Marketing Officer
20132011 2012201020092007 200820062005
Source: Google Trends
Population Growth
Algorithms
Data
Data Scientist
Computers
Models
Predictions
Purpose
Visualizations
Insights
Evolutionary Tree
Data Scientist
Business Analytics
Business Intelligence
Domain Expert
Detective
Historian
Reporting
Visualization
Machine Learning
Data Mining Statistics
Science
Mathematics
Optimization
Artificial Intel.
Data Hacker
Computer Programmer
Data Developer
Data Admin.
Data Capture
Logician
Engineer
Source: Google Trends, Data Science Central, “Data Scientist Demographics”
NEW YORK
LONDON
BANGALORE
MOSCOW
SAN FRANSISCO
Demographics
<18
18-24
25-34
35-44
45-54
55-64
65+
50
100
150
0
AVERAGE AGE INDEXEDUCATION INDEX
NO COLLEGE
100
200
300
0COLLEGE GRAD SCHOOL
MALE vs. FEMALEINDEX
158
46
OPERATING SYSTEM
LINUX
Windows
MODERN LEGACY
Tool Usage
Open source
Community supported
State of the art
Steeper learning curve
MODERN TOOLS
DATA MANAGEMENT
HADOOP
Oracle
SASDATA ANALYSIS
R
VISUALIZATION TECHNOLOGY
GGPLOT Excel
Source: Wikipedia, “List of biases in judgment and decision making”
Critical Weaknesses
CONFIRMATIONSeeking what you believe
SAMPLE SIZEMistaking noise for real patterns
INFORMATIONSometimes, data is useless
SELECTIONIgnoring how the data was collected
NORMALCYIgnoring the ‘black swan’ possibility
Building a Data Science team
Where to put them
Chief Technology Officer
Data Sciences
Engineering & Operations
TECHNOLOGY
Product Management
PRO Tight integration into platforms
CON Weak business unit connections
PRO Connection to applications
Marketing Operations
Chief Marketing Officer
Data Sciences
Marketing Strategy
FUNCTIONAL
CON Limited scope and potential for bias
Chief Data Officer
Data Sciences
Data Management
DIVISIONAL
CON Data must be a top 3 board priority
PRO Focus &Sponsorship
Data Governance
ROCK STARPRINCIPALJUNIOR SENIOR
50%
Requiresclose supervision
Transformational Impact
Driven & accomplished
Must be mentored
35% 14% 1%
COMP
POPULATION %
ABILITY
CAT ANALOGY:
Levels of experience
Where they Congregate
EVENTS:
ONLINE:
Defining Success
INSIGHTSInfluencing big decisions
TRANSFORMATIONALNew products or strategies
ALGORITHMSAutomating micro decisions
OPERATING CHANGESRedesigned processes & technology
