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

https://twitter.com/#%21/TonyCosentinoVR

http://www.linkedin.com/profile/view?id=1437752&locale=en_US&trk=tyah

http://tonycosentino.ventanaresearch.com/

© 2014 Ventana Research2 © 2014 Ventana Research2

Ventana Research

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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 IT2. Data scientists control data / analytics buying3. Analytics is responsibility of IT4. Business intelligence will resolve all needs5. Spreadsheets are not heavily used today6. Needs addressed by dashboards & reports7. Mobile access to BI is not important8. Cloud computing is not important to BI9. Visual and data discovery is next BI10. 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

http://www.linkedin.com/company/ventana-research

http://blog.ventanaresearch.com/

http://marksmith.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

https://twitter.com/#%21/TonyCosentinoVR

http://www.linkedin.com/profile/view?id=1437752&locale=en_US&trk=tyah


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