An exclusive survey and report on: Midmarket companies moving fast toward big data reliance

Larry MarionSpring 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.

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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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.

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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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

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

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%

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.

Picking the low hanging fruit comes firstQuestion 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 9

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

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)

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

Big Data system in production

BD initiative in development but not yet in production

89%

49%

10%

42%

2%

9%

Improved decision makingNot yet improved decision makingNot sure

Commentary: • Of the 123 mid market in production with at least one Big Data system, overwhelming endorsement of benefit

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

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

23%

20%

27%

32%

23%

44%

40%

46%

49%

50%

In productionIn 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.

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 13

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

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

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

29%

30%

32%

33%

37%

41%

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

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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For More Information

Larry Marion, Editorial Director– lmarion@triangle-publishing.com

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