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DESCRIPTIONsurvey and analysis of Big Data use in large mid market organizations around the world. Research report available upon request.
An exclusive survey and report on: Midmarket companies moving fast toward big data reliance
Larry MarionSpring 2014
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
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.
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.
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
Commentary: • +/- 5.5% margin of error • We only highlight data that clearly have statistical significance , i.e.,
exceed 6 percentage point deltas
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
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
IT Involvement Business
C-Level or VP Director or Manager
Application performance management,
including cloud-based apps
IT security systemsIT governance and policies, including
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.
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
Identify and take advantage
of business opportunities
Improve quality and speed of
Quickly respond to competitive
threats or other inputs
Improve effectiveness of our marketing
Predict future trends that may
imperil business goals
Better understand constituent sentiments
51% 51% 50%45% 44% 44% 44% 43% 41%
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
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%
% Responding "Extremely Valuable Now" % Responding "Extremely Valuable in Two Years"
Real-time processing of data and 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
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
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
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
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?
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
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
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
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
Decision-making top beneficiary of Big Data
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
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