Market Pulse Research:
Big Data Storage & Analytics
METHODOLOGY & RESEARCH OBJECTIVES
Sample
Method
Survey Goals
Field Work
This survey was fielded
from December 15, 2014 to
December 22, 2014
Total Respondents
120 qualified completes
Collection
Online Questionnaire
Number of
Questions
13 (excluding screeners and
demographics)
Audience
To complete this survey,
respondents were required to be
personally involved in purchase
decisions regarding Big Data
storage and analytics initiatives at
an organization that has some
plans to deploy or implement Big
Data storage and analytics
projects.
The purpose of this survey is to gain a better understanding of Big Data storage and
Total Respondents
Organization Size
120
Top Represented Job Titles
Top Represented Industries
10,000+ employees 33% 500 - 9,999 employees 34% <500 employees 33% Mean 10,066 employees 23% 13% 11% 10% 9% 6% Technology/Computer related Government (Federal, State, or Local) Financial Services & Insurance
Healthcare Education Manufacturing: Discrete or Process
RESPONDENT PROFILE
IT Management (NET) 69% CIO, CTO 12% CSO 3% Director 13% IT Architect 10% Manager 14% Supervisor 3% Database management 6% Applications management 2% Technical Consultant 7%Business Management (NET) 19%
CEO, COO, Chairman, President 8% Executive VP, Senior VP, VP, GM 3%
Director 3%
Consultant (Non-Technical) 3%
Involvement in purchase decisions regarding Big Data storage and analytics
73% 69% 70% 47% 64% 54% 33% Determine needs Determine features/requirements Evaluate products/services for purchase Evaluate where to buy products/services Recommend or specify types of products/services for purchase Recommend or specify brands or vendors for purchase Authorize purchases/approve expendituresRESPONDENT PROFILE
(continued…)
S1: How are you personally involved in purchase decisions regarding Big Data storage and analytics at your organization? (P lease select all that apply.)
In this survey we are defining Big Data as an increase in data volume, variety, and velocity (meaning the speed at which data needs to be processed and available for analysis) coupled with
28%
14%
28%
15%
15%
We have already deployed/implemented one or more Big Data storage and analytics projects
We are in the process of implementing or pilot testing Big Data storage and analytics projects
We have plans to deploy or implement Big Data storage and analytics projects over the next 12 months
We are considering deploying or implementing Big Data storage and analytics projects within the next 13-24 months
We are likely to implement Big Data storage and analytics projects in the future but are struggling to find the right strategy or solutions
Currently Implementing, Planning or Considering Enterprise-Wide
Big Data Storage and Analytic Projects
S2: Is your company currently implementing, planning or considering enterprise-wide Big Data storage and analytics projects (I.e., devising strategies and projects to generate more value from existing data)?
Base: 120 qualified respondents
KEY FINDINGS
• Organizations are looking to Big Data storage and analytics initiatives to efficiently handle increasing amount
of data, users, and applications, as well as to improve the quality of their data.
• Top drivers for Big Data and storage analytics initiatives revolve around speed of information, including faster
decision-making, anticipation of customer needs, real-time tracking of financial data and the ability to quickly
identify new business opportunities.
• A limited IT budget is the top barrier slowing or even preventing Big Data storage and analytics projects.
Beyond budget, integration issues and missing technical expertise are frequently slowing these initiatives.
• CIOs and other IT management are the primary sponsors of Big Data storage and analytics strategy. When it
comes to executing on that strategy, IT management is the most involved although 23% report that a Database Architect
is responsible for strategy execution.
• Larger companies (1,000+ employees) are more likely than smaller companies to report Line-of-Business
Analysts are responsible for executing Big Data storage and analytics strategy.
• Centralized data warehouses, in-database analytics, high-performance computing and complex event
processing are the Big Data storage and analytics architectures most often requiring support.
• The ability to support multiple users simultaneously is the most critical consideration when designing
infrastructure to support Big Data storage and analytics projects. Respondents also place high importance on the
ability to query structured and unstructured data, and to load large datasets quickly.
• When evaluating solutions to support Big Data storage and analytics projects, the ability to use standard tools
and scalability are the most sought after features.
• Almost half (47%) either have converged systems as part of their infrastructure today or will consider
converged systems over the next year. Larger companies (1,000+ employees) are more likely to have converged
KEY FINDINGS
(continued…)
• Improved IT staff efficiency and improved utilization of IT resources are viewed as the top benefits of
converged systems.
• Three-quarters (76%) of organizations are either already using converged systems (10%) or are at least
somewhat likely to consider them (66%) when designing their infrastructures to support Big Data storage and
analytics projects.
• Of the many important potential outcomes of Big Data storage and analytics projects, improved
decision-making is at the top of the list. Respondents also cite a unified view of information and improved collaboration among
50% 47% 34% 33% 31% 27% 19% 5% 2%
Handling increasing amounts of data, users, and applications in quick and efficient manner Improving data quality (accuracy, completeness, and consistency) Improving system response time Integrating and managing siloed data and applications Defining standards for information infrastructure & data management Eliminating data redundancy Reducing data latency Other Don’t know
Top Pain Points Seeking to Address with Big Data Storage and Analytics Initiatives
Q1: What are the top pain points that your organization is looking to address with its Big Data storage and analytics initia tives? (Please select up to three.) Base: 120 qualified respondents
Organizations are looking to Big Data storage and analytics initiatives to
efficiently handle increasing amount of data, users, and applications, as well as
39% 29% 29% 26% 26% 24% 22% 21% 14% 13% 3% 1%
Creating executive dashboards for more informed, faster decision-making Predicting and responding to customer needs in real-time Real-time tracking of financial data (cost, pricing, time to market) Having the ability to quickly identify new business opportunities Providing self-service capabilities to end users Responding to changes in the marketplace before a trend is established Driving/designing/launching new product or service offerings Acquiring new customers and engaging current customers Preparing for integration with the Internet of Things (IoT) Entering new market segments Other Don’t know
Top Business Objectives Driving Big Data Storage and Analytics Initiatives
Q2: What are the top business objectives driving your organization’s Big Data storage and analytics initiatives? (Please select up to three.)
Top drivers for Big Data and storage analytics initiatives revolve around speed
of information, including faster decision-making, anticipation of customer
62% 48% 44% 38% 25% 20% 3% 3% 1% IT budget Integration with current data/storage infrastructure Technical expertise (i.e., no Data Scientist on staff) Time constraints Current software of analytics solution capability Hardware capability Other None Don’t know
Primary Barriers Slowing Big Data Storage and Analytics Projects
Q3: What are the primary barriers slowing or preventing Big Data storage and analytics projects at your organization? (Plea se select all that apply.) Base: 120 qualified respondents
A limited IT budget is the top barrier slowing or even preventing Big Data
storage and analytics projects. Beyond budget, integration issues and missing
technical expertise are frequently slowing these initiatives.
IT budget is more likely to be a barrier among those
who are still in the planning stages with
respect to Big Data storage and analytics.
Companies likely to implement Big Data storage
projects in the future but struggling to find the right strategy are more likely to report limited technical
37% 27% 11% 10% 3% 1% 9% 3% CIO IT Management CTO CEO CFO CMO Other Don’t know
Primarily Responsible for Big Data Storage and Analytics Strategy
Q4: Who in your organization is primarily responsible for (or executive sponsor of) your Big Data storage and analytics stra tegy?
73% 23% 18% 14% 13% 8% 1% IT Management Database Architect Line-of-Business Analyst Development Lead Data Scientist(s) Other Don’t know
Responsible for Executing Big Data Storage and Analytics Strategy
Q5: Who in your organization is responsible for executing your Big Data storage and analytics strategy? (Please select all that apply.) Base: 120 qualified respondents
When it comes to executing on that strategy, IT management is the most
involved although 23% report that a Database Architect is responsible for
strategy execution.
Larger companies (1,000+ employees)
are more likely than smaller companies to report Line-of-Business Analysts are responsible for executing Big Data storage
46% 43% 41% 38% 31% 28% 26% 6% 12%
Centralized data warehouses In-database analytics High-performance computing Complex event processing Analytic appliances In-memory processing Hadoop Other Don’t know
Big Data Storage and Analytics Architectures Requiring Support
Q6: Which of the following Big Data storage and analytics architectures do or will need to be supported at your organization ? (Please select all that apply.)
Centralized data warehouses, in-database analytics, high-performance
computing and complex event processing are the Big Data storage and
analytics architectures most often requiring support.
Larger companies (1,000+ employees) are more likely than
smaller companies to report Hadoop needs or will need to be supported at
37% 25% 28% 30% 22% 15% 13% 43% 49% 43% 36% 39% 32% 33% 15% 21% 23% 26% 32% 33% 34% 3% 3% 5% 6% 4% 10% 10% 3% 1% 1% 1% 4% 4% 1% 1% 2% 3% 6% 6%
Supporting multiple users simultaneously Ability to execute queries on structured and unstructured data Loading large datasets quickly Speed to provide user queries with significatnly higher levels of I/O throughput Complex processing requests Tying together and integrating the worlds of relational and Hadoop data Executing ODBC calls
Critical Very important Somewhat important Not very important Not at all important Don't know
Q7: How important are the following considerations at your organization when designing your infrastructure to support Big Da ta storage and analytics projects? Base: 120 qualified respondents
The ability to support multiple users simultaneously is the most critical
consideration when designing infrastructure to support Big Data storage and
analytics projects.
79% 74% 71% 66% 61% 47% 46% % Critical/ Very importantImportant Considerations When Designing Infrastructure to Support
24% 37% 29% 28% 23% 22% 18% 18% 56% 42% 38% 38% 41% 38% 43% 41% 18% 18% 28% 27% 25% 28% 33% 33% 2% 3% 3% 3% 7% 8% 6% 8% 1% 1% 3% 1% 1% 1% 2% 3% 4% 2% 1%
Ability to use standard tools
Scalability
Leading performance
Fast time to value
Leading price
Deep integration
Collaborative support approach
Modularity
Critical Very important Somewhat important Not very important Not at all important Don't know
Important Features When Evaluating Solutions to Support
Big Data Storage and Analytics Projects
Q8: How important are the following features when evaluating solutions to support your Big Data storage and analytics proje cts?
When evaluating solutions to support Big Data storage and analytics projects,
the ability to use standard tools and scalability are the most sought after
features.
80% 78% 67% 66% 64% 60% 60% % Critical/ Very important 58% Larger companies (1,000+ employees) are more likely3%7%
28%
31%
24%
8%
1 to less than 2 months2 to less than 3 months 3 to less than 6 months 6 to less than 12 months 12 months or longer Don’t know
Reasonable Timeframe to Implement Big Data Storage and Analytics Solutions
On average, 7 months is cited as a reasonable timeframe for implementing a
Big data storage and analytics solution; over half (59%) consider 3 to 12
months to be a reasonable period of time.
Q9: In your opinion, what is a reasonable timeframe to implement a Big Data storage and analytics solution? Base: 120 qualified respondents
In this survey we define converged systems as purpose-built systems
which combine physical server hardware, software, networking, storage
and unified management and support on one single box.
29%
18%
19%
25%
8%
Converged systems are part of our infrastructure today
We will consider converged systems over the next 12 months
We are researching and/or evaluating converged systems
We have no immediate plans to evaluate converged systems
Don’t know
Organization’s Current Attitude Towards Converged Systems
Q10: What is your organization’s current attitude towards converged systems?
Almost half (47%) either have converged systems as part of their infrastructure
today or will consider converged systems over the next year.
In this survey we define converged systems as purpose-built systems which combine physical server hardware, software, networking, storage and unified management and support on one single box.
Larger companies (1,000+
employees) are more likely to have
converged systems part of their infrastructure today (39% vs
15%) while smaller companies are more likely to still be researching
58% 54% 46% 43% 40% 31% 27% 3% 12%
Improved IT staff efficiency Improved utilization of IT resources Improved disaster recovery Lower Total Cost of Ownership (TCO) Improved business agility Fewer management tools Lower data center/power and cooling costs None Don’t know
Most Important Potential Benefits of Converged Systems
Q11: In your opinion, what are the most important potential benefits of converged systems? (Please select all that apply.) Base: 120 qualified respondents
Improved IT staff efficiency and improved utilization of IT resources are
viewed as the top benefits of converged systems.
Companies that have plans to deploy Big Data storage and analytics projects in the next 12-24 months are more likely than others to consider
lower total cost of ownership (TCO) and improved business agility as important potential
10%
15%
23%
28%
17%
3%
5%
Already using one or more converged systems Extremely likelyVery likely Somewhat likely Not very likely Not at all likely Don’t know
Likelihood of Considering Converged Systems When Designing Infrastructure to
Support Big Data Storage and Analytics Projects
Three-quarters (76%) of organizations are either already using converged
systems or are at least somewhat likely to consider them when designing their
infrastructures to support Big Data storage and analytics projects.
Q12: How likely is your organization to consider converged systems when designing your infrastructure to support Big Data st orage and analytics projects? Larger companies (1,000+ employees)
53% 40% 38% 36% 35% 33% 32% 30% 29% 28% 24% 21% 11% 3% Improved decision-making Single view of information across the enterprise Improved collaboration/information sharing Providing self-service capabilities to end users Increased IT efficiencies and time savings Better ad-hoc data analysis Increased ROI Faster query speed Added reliability and security Reduced burden on IT Improved ability to scale-up/scale-down to accommodate business needs Lower TCO Reduced provisioning times Other
Most Important Potential Outcomes of Big Data Storage and Analytics Projects
Q13: What are the most important potential outcomes of Big Data storage and analytics projects for your organization? (Plea se select up to five.) Base: 120 qualified respondents
Of the many important potential outcomes of Big Data storage and analytics
projects, improved decision-making is at the top of the list.
Larger companies (1,000+
employees) are more likely than smaller companies to think that
improved ability to scale-up/scale-down to accommodate
business needs and reduced provisioning times are important
Number of Employees
Industry/Business
24% 9% 5% 3% 6% 13% 8% 7% 26% 20,000 or more 10,000 - 19,999 7,500 - 9,999 5,000 - 7,499 2,500 - 4,999 1,000 - 2,499 500 - 999 250 - 499 Fewer than 250 23% 13% 11% 10% 9% 6% 5% 5% 3% 3% 3% 2% 2% 1% 1% 6% Technology/Computer related Government (Federal, State, or Local) Financial Services & Insurance Healthcare Education Manufacturing: Discrete or Process Energy, Gas & Oil Professional Services Nonprofit Retail Telecommunications Aerospace/Defense Engineering/Construction Entertainment Life Sciences- Pharmaceuticals, Biotech OtherD1: Approximately how many people are employed in your entire organization or enterprise? (Please include all plants, divisions, branches, parents and subsidiaries worldwide.) D3: Which one of the following best describes your organization's industry or business?
Base: 120 qualified respondents
Respondents range in organization size and industry.
Job Title
D2: What is your primary job title?
Most respondents hold IT management job titles, such as Manager, Director,
CIO/CTO, and IT Architect.
8% 1% 3% 3% 1% 3%
