Overview of finding from Enterprise Strategy Group (ESG) finding of survey about Big Data

1. TM
Enterprise Strategy Group | Getting to the bigger truth.
The Convergence of Big Data and
Integrated Infrastructure
Research Report Snapshot
Evan Quinn, Senior Principal Analyst
July, 2012
©2012 Enterprise Strategy Group

2. Survey Overview
Respondents
• 399 IT and LoB decision makers responsible for their organization’s
BI/analytics, data management and related infrastructure environments
• 22% line-of-business; 32% “analysts” including data scientists, business
analysts, data analysts and report administrators
Organizations
• 54% enterprise (>999 employes), 46% midmarket (100-999 employees)
• North America
• All primary vertical industries represented except tech:
 18-to-6% range: manufacturing, financial services, government, comms/media,
business services, retail/wholesale, healthcare
Questionnaire
• A wide sweep across big data, analytics, data management, infrastructure
• Subjects: Business/IT priorities, meaning/impact of Big Data, data volume
and diversity, integration techniques, analytics solutions, storage impact
© 2012 Enterprise Strategy Group 2

3. Research Objective Snapshot
Key Survey Research Questions
• How important is the enhancement of analytics capabilities relative to an
organization’s business and IT priorities?
• What is associated with the term “big data?”
• What is the current and planned usage of Hadoop MapReduce
• Regarding largest data sets used for analytics:
 What is the largest size, how many sources, what are the types, how frequently updated, are
there geographic distribution challenges?
• What tools are used for data integration in relation to big data?
• What data analytics and/or processing challenges do organizations face?
• What data analytics platforms have been/will be deployed for big data
• What are key data management features needed to support analytics
• What storage technologies are used to support analytics; which are most
pervasive and how will this change going forward?
• How much downtime can be tolerated for analytics?
• What data protection technologies are in place to support analytics and
related processing?
© 2012 Enterprise Strategy Group 3

4. Key Finding: Analytics a Top 5 Priority
Relative to all of your organization’s business and IT priorities over the next
12-18 months, how would you rate the importance of enhancing data
processing and analytics activities? (Percent of respondents, N=399)
Importance of enhancing data processing and analytics activities relative to all business priorities
Importance of enhancing data processing and analytics activities relative to all IT priorities
50%
45%
45%
40% 38%
35%
30% 28%
25%
21%
20% 18% 19%
15%
11% 10%
10%
6%
5%
4%
1%
0%
Our most important One of our top 5 One of our top 10 One of our top 20 Not among our top 20 Don’t know
priority priorities priorities priorities priorities
Source: ESG Research Report, The Convergence of Big Data Processing and Integrated Infrastructure, July 2012
© 2012 Enterprise Strategy Group 4

5. Key Finding: Strong Demand for New Analytics Platforms
Does your organization have plans to deploy a new data analytics platform in
the next 12-18 months in support of its fastest growing data set? (Percent of
respondents, N=399)
Don't know, 14%
Yes, 39%
No, 46%
Source: ESG Research Report, The Convergence of Big Data Processing and Integrated Infrastructure, July 2012
© 2012 Enterprise Strategy Group 5

6. Key Finding: Hadoop MapReduce Heating Up
How would you rate your organization’s interest in implementing a MapReduce
framework to address data analytics challenges? (Percent of respondents, N=399)
We currently use MapReduce technology to support our largest data
2%
set
We currently use MapReduce technology in a limited production
2%
capacity (e.g., small data analytics tasks)
We are currently testing MapReduce technology 5%
We plan to deploy MapReduce technology in the next 12-18 months 1%
Very interested 20%
Somewhat interested 32%
Not at all interested 12%
Not familiar with MapReduce framework technology 18%
Don’t know 8%
0% 5% 10% 15% 20% 25% 30% 35%
Source: ESG Research Report, The Convergence of Big Data Processing and Integrated Infrastructure, July 2012
© 2012 Enterprise Strategy Group 6

7. Conclusions to Big Data and Integrated Infrastructure
1. Organizations view improving analytics capabilities as critical
2. “Big Data” means dealing with very large data sets (57%)
3. No clear leaders for big data commercial analytics have emerged
• Unlikely this will change over the next 12-18 months, but demand and interest for
new analytics platforms is strong at 39%
• The high cost and difficulties of using existing analytics solutions for big data is the
primary driver towards new analytics related purchases
4. Security, data integration and data quality are the biggest hurdles
5. Improved business agility is the most sought-after benefit for
deploying a new analytics solution
6. Hadoop MapReduce adoption has been limited to date, but
• There will be a strong shift to commercial distributions of Hadoop MapReduce based
solutions among the next wave of adopters; <40% don’t know or have no interest
• 17% are interested in public cloud-based big data solutions
7. Big data analytics infrastructures should excel at availability,
performance/bandwidth and information management
© 2012 Enterprise Strategy Group 7

8. IT Advisory for Big Data Analytics
1. Small promises, small wins
Look for vendors who want to help evolve your organization towards big
data and are willing to leverage existing resources; avoid those who
promise big results or say that it will be easy; big data requires an
educational investment for IT and most business/data analysts
2. Your current vendor(s) may have your best big data answer
Despite the “newness” of big data, many established database and
analytics vendors have stayed abreast of the technology; if you like your
current vendor they may be your best option
3. Improve overall data management practices for big data
If your current practices around data integration, governance, security
and information management are lacking, big data projects will expose
those weaknesses
© 2012 Enterprise Strategy Group 8