Karl Rexer, PhD
President
Rexer Analytics
www.RexerAnalytics.com
2010 Data Miner Survey Highlights
… The Views of 735 Data Miners
Predictive Analytics World
Washington, DC
2010 Data Miner Survey: Overview
•
Fourth annual survey
•
47 questions
•
10,000+ invitations emailed
plus newsgroups, vendors,
and snowball referrals
•
Respondents:
735 data miners
from 60 countries
33%
31%
12%
5%
19%
Corporate
Consultants
Note: Data from tool vendors was
excluded from many analyses
Academics
NGO / Gov’t
Vendors
45%
36%
12%
North America
• USA 40%
• Canada 4%
Europe
• Germany 7%
• UK 5%
• France 4%
• Poland 4%
Asia Pacific
• India 4%
• Australia 3%
• China 2%
Central & South
America (4%)
• Columbia 2%
• Brazil 1%
Middle East & Africa (3%)
• Israel 1%
• Turkey 1%
10% 10% 10% 11% 13% 13% 14% 15% 15% 25% 29% 41%
0%
10%
20%
30%
40%
50%
Government
Internet-based
Manufacturing
Medical
Technology
Pharmaceutical
Retail
Telecommunications
Insurance
Academic
Financial
CRM / Marketing
Fields Applying Data Mining
Question: In what fields do you TYPICALLY apply data mining? (Select all that apply)
•
CRM / Marketing, Financial and Academic are the most commonly
reported fields. This has been consistent since the 2007 survey.
8% 9% 9% 11% 12% 13% 14% 16% 21% 21% 22% 25% 26% 27% 31% 32% 60% 68% 69% 0% 10% 20% 30% 40% 50% 60% 70% MARS Uplift Modeling Link Analysis Genetic Algorithms Social Network Analysis Rule Induction Survival Analysis Anomoly Detection Bayesian Support Vector Machines Ensemble Models Association Rules Text Mining Factor Analysis Neural Nets Time Series Cluster Analysis Regression Decision Trees
Data Mining Algorithms
•
Decision trees, regression, and cluster analysis continue to form a triad of core
algorithms for most data miners. This is very consistent, year to year.
•
However, a wide variety of algorithms are being used.
Question: What algorithms/analytic methods do you TYPICALLY use? (Select all that apply)
Corporate Consultants Academic NGO / Gov’t
10% 12% 4% 5%
Ensemble Models
Uplift Modeling
Corporate Consultants Academic NGO / Gov’t
Text Mining
STATISTICA Text Miner 19%
IBM SPSS Modeler 17%
SAS Text Miner 9%
IBM SPSS Text Analytics 7%
Rapid Miner 6%
Provalis Wordstat 2%
GATE 2%
KXEN 2%
Oracle Text or ODM 1%
Megaputer Text Analyst 1%
Autonomy 1%
Other 35%
Text Miners
•
About a third of data miners
currently incorporate text
mining into their analyses,
and another third plan to.
Software Used
Plan to Start
Text Mining
No Plans to
Conduct Text
Mining
0% 20% 40% 60%The focus of our text mining is to extract key themes (sentiment analysis) We use text fields as inputs / predictors in a larger model We use text mining as part of social network analyses
30%
34%
36%
55%
59%
21%
35%
24%
49%
39%
26%
18%
7%
0% 60%Computing Environments
•
A lot of data mining happens on desktop and laptop computers.
•
Frequently the data and processing is local
(not on servers, mainframe or cloud).
•
Only a small minority of data mining is on the cloud.
Question: What are the computing environments/platforms on which data mining/analytics occurs at your company/organization? (Check all that apply)
C
orp
ora
te
C
on
su
lta
nt
Aca
de
mi
c
N
G
O
/
G
ov’
t
V
en
do
r
5% 10% 7% 3% 14% 20% 16% 14% 32% 26% 28% 30% 19% 29% 45% 48% 36% 25% 47% 39% 43% 49% 58% 58% 35% 29% 24% 15% 32% 37% 28% 36% 46% 42% 44%Cloud Computing
Centralized Mainframe/Server
Local Server
Desktop PC/Workstation (with data &
processing on server, mainframe or cloud)
Desktop PC/Workstation (with
data & processing locally)
Laptop PC (with data & processing
on server, mainframe or cloud)
Laptop PC (with data &
processing locally)
Analytic Capability & Data Quality
•
Analytic capability:
–
There’s room to improve if we’re going to “Compete on Analytics”.
Data Quality Question: How do you rate the quality of data available for analysis at your company/organization?
•
Data quality:
–
48% rate it “strong” or “very strong” (same as last year)
–
16% rate it “poor” or “very poor” (13% last year)
Analytic Capability Question: How do you rate the analytic capabilities of your company/organization?
13%
35%
30%
20%
8%
40%
35%
13%
Overcoming Challenges: Best Practices
•
Top challenges facing data miners:
–
Dirty data
: #1 challenge every year, 2007-2010
–
Explaining data mining to others
: always in the top 4 challenges,
2007-2010
–
Difficult access to data
: always in the top 3 challenges, 2007-2010
•
This year survey respondents provided “Best
Practices” for overcoming these challenges.
–
E.g., Dirty Data: Use anomaly detection to flag records to put before
subject matter experts.
–
E.g., Dirty Data: All projects begin with low-level data reports showing
counts of records, verification of keys (uniqueness, widows/orphans), and
distributions of field contents. These reports are echoed back to the data
content experts.
–
See the list of Best Practices at www.RexerAnalytics.com in early
November.
Data Mining Software
Survey Questions: • What Data mining/analytic tools did you use in 2009? (rate each as “never”, “occasionally”, or “frequently”)• What one Data Mining software package do you use most frequently?
Overall
Corporate
Consultants
Academics
NGO / Gov’t
•
The average data miner reports using 4.6 software tools.
•
R is used by the most data miners (43%).
Satisfaction with Data Mining Tools
Question: Please rate your overall satisfaction with your primary Data Mining software package.
2010
2009
Sample size < 20
•
STATISTICA received the highest satisfaction ratings. Consistent with
the 2009 findings, R and SPSS Modeler users are also quite satisfied.
–
About 80% of STATISTICA and R users also report that they are extremely likely to
stay with these primary tools over the next 3 years. This is reported by only 42-45%
of SAS, SPSS Statistics, and SAS-EM users; and only 18% of Weka users.
Continued Use question (not graphed): What is the likelihood that you will continue to use this tool as your primary Data Mining software package over the next 3 years?
Data Mining and the Economy
Question: How will the number of data mining projects your organization conducts in 2010 compare to what has been typical in the past few years?
There is a strong market for data mining:
•
73% of data miners foresee increases in the number of data mining projects.
•
Offshoring of data mining is also increasing: It is reported by 14% of data
miners this year (8% last year).
Offshoring Question (not graphed): Has your company moved any data mining or other analytics to another country to take advantage of lower wages in the destination country?
