survey and analysis of Big Data use in large mid market organizations around the world. Research report available upon request.

1. An exclusive survey and report on:
Midmarket companies moving
fast toward big data reliance
Larry Marion
Spring 2014
Presented to
Prepared by
Midmarket interest in Big Data and data analysis rivals that
of enterprise firms. Budgets are rising, with IT and sales and
marketing taking the lead.

2. Agenda
 Introductions
 Survey Objectives
 Methodology
 Demographics
 Universal agreement on Big Data’s importance
 Improving quality currently the biggest benefit
 Data volumes, budget limitations top Big Data
challenges
 Strong ties between business and IT critical to success
 Best practices
 Next steps—report themes
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3. Team background
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Triangle Publishing Services Co. Inc. (TPSC) is a leading provider of content
about information technology for business and technology publications and vendors. It
has produced hundreds of research reports, web sites, feature articles, case studies and other forms of
content. Triangle consists of a team of 60 business and technology journalists, designers, audio and video
experts around the world.
Beacon Technology Partners LLC helps leading
companies better understand the needs of their customers
and prospects. The company's client base includes both business-to-business and business-to-consumer
companies in other sectors. It offers both quantitative and qualitative research methods, depending on the
client's need to know for their strategic decision-making as well as additional research on brand
positioning, communications architecture, customer satisfaction, pricing strategy, market segmentation,
marketing effectiveness and employee engagement.

4. Survey Objectives
 Understand business drivers and anticipated
results for Big Data initiatives in midmarket
companies.
 Document technical and business challenges
the midmarket faces with Big Data.
 Explore tools and technologies midmarket firms
need to implement Big Data projects, and
lessons learned.
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5. Methodology
 Triangle Publishing Services Co. posted a 15-
question questionnaire on a website accessible only
to executives in midmarket companies familiar with
Big Data
 Survey conducted over 4 days in November, 2013
 300 responses received
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Commentary: • +/- 5.5% margin of error
• We only highlight data that clearly have statistical significance , i.e.,
exceed 6 percentage point deltas

6. Executive Summary
 80% agree that they need Big Data
 41% have one or more big data projects in place; another 55% are starting one
 Budgets on the rise
 Biggest drivers of success: IT/Business collaboration, proper skills, and
performance management
 Biggest causes of failure: Lack of IT/Business cooperation, lack of tools and
skills
 Most effective suppliers: Best of breed and integrated full service providers
 Most influential in Big Data projects: IT closely followed by sales/marketing
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Overview
Biggest and Most—Key Highlights
Commentary: • Improving quality of products/services a bigger driver than cost cutting
• Sentiment analysis and social media not yet important
• Wide variety of data types, data volumes and budgets are big challenges
today

7. Respondent Profile
Functional Areas
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Title
300 Respondents
67%
33%
IT Involvement Business
50%50%
C-Level or VP Director or Manager
Responsibilities
Application
performance
management,
including cloud-
based apps
IT security systemsIT governance and
policies, including
budgeting
55%
47%
42%

8. Growing adoption
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Commentary: • Over half of midmarket companies are just getting started
• 55% in Asia have one or more projects, followed by 41% in NA, only 26% EMEA
• EMEA’s relative paucity of Big Data activity is a recurring theme through the results
Question 5. Please select the ONE best response below that most accurately describes whether your
organization currently has a Big Data initiative in place.
55%41%
4%
My organization is just
getting started with a Big
Data project.
My organization has one
or more Big Data
initiative(s) in place.
My organization has no Big Data initiative in
place but has discussed implementing such a
program in the foreseeable future.

9. Picking the low hanging fruit comes first
Question 1: How important is Big Data to meeting the following strategic or tactical goals in relation to
meeting the business goals of your organization?
(% responding “very important”)
Commentary:
• Note prominence of tactical, near-term goals
• Weakness of “understand constituent sentiments” implies Big Data analysis of
social media not yet a strong use case among mid-market companies
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Improve quality
of our products
and services
Obtain better
and deeper
understanding
of customer
needs
Identify and
take advantage
of business
opportunities
Improve quality
and speed of
decision
making
Quickly
respond to
competitive
threats or other
inputs
Improve
effectiveness of
our marketing
programs
Predict future
trends that may
imperil
business goals
Reduce
operating
expenditures
Better
understand
constituent
sentiments
51% 51% 50%
45% 44% 44% 44% 43% 41%
38%
Enable managers
to have a better
understanding of
the profitability -
and profit potential
– of each
customer, product
and line of
business

10. Real-time processing, predictive
analytics are most valuable tools
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Question 15: How valuable are each of the following tools or technologies to help your organization
optimize its Big Data initiative(s), now and in two years?
Commentary: • Real-time processing not surprising due to trend toward more timely analysis
• Data cleansing, data dashboards, visualization see significant uptick in two years
• Financial Services (64% answering “extremely valuable”) values real-time processing
the highest of all industries right now
• In two years, Manufacturing ranks data dashboards the highest at 64%
57%
51%
56%
61%
58%
60%
49%
50%
53%
56%
58%
60%
% Responding "Extremely Valuable Now" % Responding "Extremely Valuable in Two Years"
Real-time processing of data and
analytics
Predictive analytics
Data visualization to convert processed
data into actionable insights
Use of cloud computing to provide
anytime, anywhere data and applications
access at lower cost
Data aggregation that spans multiple
databases, including Big Data platforms
such as Hadoop
Data dashboards (desktop self-service
data integration)

11. Big Data proves its worth once deployed
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Question 16: What impact, if any, has your organization’s Big Data initiative(s) had on improving
decision making? Comparing answers of respondents in development vs. production
89%
49%
10%
42%
2%
9%
Big Data system in production
BD initiative in development but not yet in production
Improved decision making
Not yet improved decision making
Not sure
Commentary: • Of the 123 mid market in production with at least one Big Data system,
overwhelming endorsement of benefit

12. How Big Data Was Successful
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Question: How well do you currently perform this task…?
Respondents saying very well without BD vs. with BD
23%
20%
27%
32%
23%
44%
40%
46%
49%
50%
Quickly sense and respond to competitive threats or other
inputs
Reduce capital or operating costs
Understand customer needs
Improve product quality
Quality and speed of our decision making
In production
In development
Commentary: • Larger the customer, the more satisfied they are with Big Data.
• Manufacturing reports higher satisfaction in most Big Data areas.
• Consistent gap of 10-20 points between “improvement” and
“considerable improvement” in all areas.

13. Data volumes, budget limitations top
Big Data challenges
Question 3. Which of the following are among the biggest challenges facing your organization in using
data and analytics tools to achieve its business goals?
(% responding)
Commentary:
• Top two concerns related to data and infrastructure, big areas for IT
• Concerns over sheer volume of data point to need for scalable tools
• Number One, with unusual consistency across geographies, was, “wide variety of new data types
and structures”
• C level almost as aware of it (35%) as director manager (40%) and business (33%) not too
far behind IT (43%)
• Number Two is “sheer volume of data slows processing,” cited by 30%-40% of respondents
across all geographies, and between 31% and 36% of both business and IT respondents
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24%
25%
25%
25%
26%
27%
29%
32%
34%
40%Wide variety of new data types and structures
Determining what data (both structured and unstructured, and internal
and external) to use for different business decisions
Getting business units to share information across organizational silos
Inaccurate data
Understanding where in your company we should focus our Big
Data investments
Not enough trained staff to analyze the data
Analytics tools are lacking and many potential users do not have access
Lack of easy-to-use, cost-effective data cleansing tools
Sheer volume of data slows processing
Budget limitations to improve our data analysis capabilities

14. Strong ties between business and
IT a path to success
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Question 9: In general, what are the top three reasons why, in your view, Big Data or data analytics
projects succeed.
Please select up to three reasons
Commentary:
• Note, top two reasons for success and failure are organizational – speak to business/IT
alignment
• Importance of link between data analytics/performance management especially strong
for those with experience, rising from 17% to 41%
• While not in the top three, “business requirements are complete and accurate” also
showed strong jump with experience, from 17% to 34%
• EMA ranked user adoption of tools, complete business requirements more important
than other regions
29%
30%
32%
33%
37%
41%
Data center tools are quite capable
Server and storage capacity is readily available
Business requirements are complete and accurate
Required IT skills, such as data scientists, are readily found in
the organization
Strong connection between data analytics and performance
management in the organization
Strong cooperation or collaboration between business and IT

15. Decision-making top beneficiary
of Big Data
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Question 16: What impact, if any, has your organization’s Big Data initiative(s) had on improving
decision making? Please select the ONE best response below.
Commentary: • EMEA reports significantly more respondents saying decision making
not improved
• Business and IT report similar levels of satisfaction (67% IT and 63%
business) with 78% C-level saying decision-making improved
• Energy and Manufacturing report greatest improvements; Financial
Services, Government and Health Care report the least
65%
29%
6%
My organization’s Big Data
initiative(s) has improved
decision-making in the
business.
My organization hasn’t had time
to evaluate the results from its
Big Data initiative(s).My organization’s Big Data
initiative(s) hasn’t yet
improved decision-making
in the business

16. Other data sets
 Big Data drivers—which business problems triggered
investments
 Storage demands now and in the future
 Data types involved—structured vs. unstructured
– Internal vs. external data explored, too
 Why Big data projects fail
 Which departments control Big Data projects
 Big Data funding growing
 Most important Big Data technologies
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17. For More Information
 Larry Marion, Editorial Director
– lmarion@triangle-publishing.com
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