Analytic Discovery: Barrier or Opportunity to Gain Insight from Information

© 2014 Ventana Research1 © 2014 Ventana Research
Analytic Discovery:
Barrier or Opportunity
to Gain Insight
from Information
Mark Smith, CEO & Chief Research Officer
and Tony Cosentino, VP & Research Director
@ventanaresearchvr In/ventanareseachventanaresearch.com

© 2014 Ventana Research2 © 2014 Ventana Research2
Ventana Research
Value in Working with Ventana Research
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• Members (250,000+) and Reach to 3m+ Professionals
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Ventana Research is the leading benchmark research and strategic advisory
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are based on our rigorous research-based benchmarking, business,
technology and best practices services. Our unique approach to covering
people, process, information and technology in organizations across business
and IT is unique to our analyst firm.

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 Reflects the current state and
direction of business and
technology best practices
across business and IT.
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technology metrics.
 Assess and measure people,
process, information and
technology dimensions.
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technology.
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Process
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Research
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IT Coverage
Our Research Agenda is driven by our analysts’ deep understanding of the
buyer’s business requirements and our knowledge of vendor solutions and
technology. All research builds on structured analysis of the people, process,
information and technology dimensions that describe business issues.

State of the Market

Business Intelligence
Mature Market:
• Dashboards
• Reports
• Query and Present
• Analysis
• Spreadsheets

Business Analytics
Evolving Discipline:
• Empowering analysts to
perform analytics more
effectively.
• Apply Predictive Analytics
(64%), take action on
outcomes (48%) and
present data visually
(45%) are top capabilities
requested.
• Perform faster analysis
(49%) is advantage of
visualizing big data.
Source: Ventana Research Big Data Analytics
Benchmark Research

1. Business can not buy without IT
2. Data scientists control data / analytics buying
3. Analytics is responsibility of IT
4. Business intelligence will resolve all needs
5. Spreadsheets are not heavily used today
6. Needs addressed by dashboards & reports
7. Mobile access to BI is not important
8. Cloud computing is not important to BI
9. Visual and data discovery is next BI
10. Location is just a pretty picture on data
Myths on BI and Business Analytics

Understanding Analytical Discovery

Analytic Discovery: Approaches
Spectrum of Methods:
• Event: Leveraging streams
of events from applications,
systems and machine data.
• Data: Utilizing data to better
understand the good, bad
and ugly of what is analyzed.
• Visual: Presenting the data
in a simple and sophisticated
manner through simple to
sophisticated methods.
• Information: Harvesting the
content and text in our
enterprise to enhance
the presentation and
insights.

Changing Priorities: User and Buyer Criteria
Related research:
•Usability and
Functionality are the most
important product and
vendor considerations in
selecting software to
design and deploy big
data and business
analytics.
Usability
Functionality
Reliability
Manageability
Adaptability
TCO/ROI
Validation
Category
% selecting
Very Important
63%
50%
50%
42%
32%
31%
20%
Source: Ventana Research Big Data Analytics Benchmark Research
User experience and
simplicity is most
critical.

Barrier to Analytical Discovery
Largest areas where time is wasted with analysts:
Preparing Data for
Analysis
47%
Reviewing data for quality and
consistency
45%
Waiting for analysts to
assemble data
39%
Interpreting information for
use by others
33%
Waiting for data and information
from IT
32%
Source: Ventana Research Information Optimization Benchmark Research

Who Should Use Analytical Discovery

Business Analytics: Usage Personas
These are common types of roles and
responsibilities:
Information Consumers
• Digest information and perform basic
interactions on data and analytics.
Knowledge Workers
• Utilize and interact with analytics and
data to drive actions and decisions.
Designers
• Enable the design and use of
information and analytics across roles.
Analysts
• Engage with data and design analytics for insights and actions.
Data Geeks
• Enable data to be accessed and exploited.

Important Analyst and End User Capabilities
39%
37%
34%
30%
24%
Most Important analyst capabilities:
Extract information
Design and Integratee metrics
Develop policies for info
access
Perform analytics to
determine interest
Provide search
capabilities
Source: Ventana Research Information Optimization
Benchmark Research
36%
27%
25%
25%
Most Important end user capabilities:
Drill down into information
Provide search capabilities
Collaborate on Information
Navigate and retrieve
information
Access applications via a
mobile device
Source: Ventana Research Information Optimization
Benchmark Research
37%

Big Data Analytics Skills Gap
Related research facts:
• Most have implemented big
data analytics with custom
builds (54%), but in the
future the largest percentage
plan to purchase dedicated
or packaged software (44%).
• Organizations are more
successful if led by experts
such as data scientists (88%)
or consultants (86%) rather
than LOB (78%) or IT (73%).
Big data analytics skills available versus
needed:
73%
47%
34%
35%
36%
35%
Business skills
76%
42%
58%
31%
28%
57%
Needed Available
Statistical skills
Spreadsheet skills
Mathematical
skills
Visual analysis
skills
SQL skills

How to Use Big Data for Analytics

Big Data: Technological Choices
Appliances
Flat Files
In-Memory
Hadoop
NoSQL
RDBMS
Specialized DB

Big Data: Adoption Growing
Source: Ventana Research Information Optimization Benchmark Research
94%
of organizations intend to use big data.
23% have used for more than a year.
56% have started deploying in past 12 months or will begin
to use within the next 12 months.
45%
of organizations are either using Hadoop
or plan to use Hadoop over the next year.
Another 26% are still evaluating the technology.

Big Data Analytics: Important Types
•Predictive analytics ranks
number five in analytic
capabilities currently available in
the organization (57%), lagging
more descriptive approaches of
query and reporting (74%).
•Top analytic methods are pivot
tables (46%), classification
(39%), and clustering (37%).
•Visual analytics is used for
contextual understanding (48%)
and root cause analysis (40%).
18%47%
Advanced /
predictive
26%13%
Descriptive
analytics
20%16%
Real-time
analytics
13%9%Visual Analytics
In-database
analytics
In-memory
analytics
SecondFirst
Importance
15%9%
7%4%

Current Analytic Methods for Big Data
• Current analytic methods
focus around exploratory
analytic methods reflected by
predictive capabilities
currently available in the
organization (57%), lagging
more descriptive approaches
of query and reporting (74%).
46%
39%
37%
35%
32%
Methods currently in use for big data
analytics
Pivot Tables
Classification or Decision
Trees
Clustering
Linear Regression
Time Series Analysis

Why Adopt Analytic Discovery

Business Wants Insights and Action
Choices:
• Less data and more insights
to act on for improving.
• Less visualization and more
interpretation of what is
relevant.
• Gain value from Big Data
investments.
• More dialogue and relevant
collaboration among
business.

Need to Access and Analyze Big Data
• IT places more emphasis on
finding patterns in Hadoop
(60% vs 51%) and doing real-
time stream processing (48%
vs 43%) while business users
place emphasis on analyzing
data from all sources, not just
one (80% vs 72%).
• Business users take the view
that there are multiple
versions of the truth and up to
the user to understand the
data and business context
(40% vs 32%).
76%
56%
55%
44%
40%
How organizations define big data analytics
Analyze data from all sources, not
just one
Find patterns in large, diverse
data sets in Hadoop
Analyze all data, not just a
sample of it
Do real-time processing on
stream of data
Visualize in seconds large,
structured data sets

The End Game: Time-to-Value (TTV)
Must provide:
• Give business a short or
medium term ROI.
• Focus on business outcomes,
not just technology.
• Show smarter use of
resources and time with
savings that are explicit.
• Provide specific competitive
advantage or operational
efficiency gains.
• Unlock the value of data for
business insights and action.

Benefits of Adoption

Advantages of Visualizing Big Data
•Perform faster analysis
followed by Understand
context better are considered
to be the key analytical
advantages of visualizing big
data.
•Visualization provides part of
the analytical needs but not all
of them.
49%
48%
40%
40%
35%
Advantages of Visualizing Big Data :
Perform Faster Analysis
Understand Context Better
Perform Root Cause Analysis
Display Multiple Result Sets
Deal With Outliers

Differing Benefits of Big Data Analytics
• 21% of organizations have
improved their business
processes significantly with
big data analytics.
• Prior to implementation
response to opportunities and
threats, improving efficiency
and improved customer
experience were most
mentioned.
24%
18%
17%
10%
6%
Benefits of Big Data analytics post
implementation:
Better communication and
knowledge sharing
Better management and
alignment of business
Gained competitive
advantage
Faster response to
opportunities and threats
Decreased time to
market

Best Practices in Analytical Discovery
Start with benefits including time-to-value, communication
and knowledge sharing, and increased productivity.
Address personas and responsibilities with right analytics
for business and time allowed.
Determine specific data sources to integrate including
transactional, engagement, demographic and attitudinal.
Consider usability, manageability and reliability with analytics
to gain efficiencies and competitive advantage.
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Questions?
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@marksmithvr
@tonycosentinovr
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http://marksmith.ventanaresearch.com
http://tonycosentino.ventanaresearch.com

© 2014 Ventana Research31 © 2014 Ventana Research
Analytic Discovery:
Barrier or Opportunity
to Gain Insight
from Information
Mark Smith, CEO & Chief Research Officer
and Tony Cosentino, VP & Research Director
@ventanaresearchvr In/ventanareseachventanaresearch.com

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