Ventana Research Best Practices in Big Data and Predictive Insights Presentation at Teradata...

© 2015 Ventana Research1 © 2015 Ventana Research

Best Practices and Trends in Big Data and

Predictive InsightsMark Smith, CEO & Chief Research Officer

Tony Cosentino, VP & Research Director

@ventanaresearch In/ventanaresearchblog.ventanaresearch.com

@marksmithVR

@tonycosentinoVR

https://twitter.com/%23%21/TonyCosentinoVR

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

http://tonycosentino.ventanaresearch.com/

https://twitter.com/marksmithvr


© 2015 Ventana Research2 © 2015 Ventana Research2

Big Data and Predictive Insights


Digital Business Generates Big Data


Big Data is Now Big Deal


Big Data has Big Business Potential


Business Analytics and Big Data

Analytics is ticket to success• New methods for discovery

and exploration• Predictive analytics now are

exploitable by business• New forms of presenting and

interacting analyticsBig Data is a new platform

• Embracing all types of data for operations and analytics

• New technologies to exploit the potential of big data

• Varying approaches now provide big data analytics


Big Data To Innovate Insights

Why You Must Use:• Because 21% of

organizations have improved their business processes significantly with big data analytics.

• Large potential to find new insights but streamline analytics and analysts effectiveness.

• Underpins new generation of business technology.

Source: Ventana Research Big Data Analytics Benchmark Research


The Future of Business is Now


Predictive Analytics for Insights

Improvement and Benefits• Almost a third (31%) find

significant improvement to activities and processes.

• Organizations achieving competitive advantage (57%) and creating new revenue opportunities (50%).

Larger Impact is Real• Almost half find significant

positive impact (49%) and third (32%) find transformational impact.

Source: Ventana Research Next Generation Predictive Analytics Benchmark Research


Predictive Analytics for Insights

Insights for Everyone• Old methods become easier for

everyone to adapt and use. • Predictive analytics are

exploitable by business.• Overall importance is indicated

for 83 percent of companies.

Knowing What You Need• Usability is top evaluation

criteria for organizations (67%).• Most often used in marketing

and sales but applies anywhere.



Analytic Personas Shape Success

A big data foundation must meet the following roles and responsibilities:

Information Consumers• Digest information and perform basic

interactionsKnowledge Workers

• Utilize and interact analytics to drive actions and decisions.

Designers• Enable the design and use of

information across roles.Analysts

• Mashup data and design analytics to provide foundational insights for business.

Data Geeks• Enable big data to be exploited in an

immature world through Data Scientists.


Analytics Require Range of Data & Methods

Spectrum of Methods:• Event: Use of streams

of events from applications, and machine data like IoT. (36%)

• Data: Utilizing data to better understand it. (58%)

• Visual: Presenting data in a for visual interaction. (25%)

• Information: Harvesting content and text for interactions. (49%)

(Percentages indicate organizations that require big data integration.)

Source: Ventana Research Big Data Integration Benchmark Research


Frequency of Integrating Data to Big Data

Related research facts:•Nearly a one-in-four companies integrate data into big data stores in Real time, whereas, a one-in-three businesses do it Every day.

o Midsize (44%) companies integrate data every day, whereas.

o Data is integrated every day by Services (40%) segment, whereas, Manufacturing(19%) integrates every hour.


24%

16%

7%

30%

9%

Real time

Every hour

Every day

Weekly

Monthly

How often does your organization need to integrate data into your big data store?


Big Data is Shifting to Events


43% integrate event-centric data to big data.28% integrate network traffic and monitoring.

41%state relevance of Internet data sources.Another 25% indicate relevance of location sources.

19%state importance of Sensor and RFID data.Another 17% indicate relevance of Internet-based event cloud.


Sensor Technology

EventOrientation

Industry Standards

Fast and Big Data

Advanced Analytics

Security and Privacy

Big Data and Analytics are Key for (IoT)


Big Data Requires Integration

© 2015 Ventana Research16


Challenge of Effective Big Data Integration

Inefficient Processes• Use data-based business

insights improves competitiveness.

• Our research finds that more than half (55%) of organizations are only somewhat confident or not at all confident in their ability to process large data volumes.

• Even more (58%) doubt their ability to process data that arrives at high velocity.


Barriers to Big Data Success

1. Reviewing data for quality and consistency (52%)

2. Preparing data for integration (46%)

3. Connecting to data sources (39%)

4. Deploying integration tasks (36%)

5. Designing tasks for integration (35%)



Examine New Approaches to Integration

Assess Environment• Only one-third of

organizations are satisfied with their current technology.

• More than half of organizations said their current infrastructure is not fast enough or flexible enough; almost half said the technology is simply inadequate.

• About half of organizations turn to their existing database or data integration vendor.


Best Practices


Best Practices for Big Data and Predictive Analytics• Determine where predictive

is needed.• Realize predictive analytics

require skills.• Overcome technical

obstacles.• Ensure business and IT

work together.• Consider using big data for

predictive.


Determine Where Predictive is Needed

Key facts from research:•Predictive analytics is most used in marketing (48%), operations (44%) and IT (40%).

•Companies that achieve competitive advantage more often:

• Support the deployment of in business processes (66% vs. 57% overall)

• Use business intelligence and data warehouse teams to design and deploy (71% vs. 58%)



Realize Predictive Analytics Require Skills

Related research facts:• Top reasons an organization’s

not being fully satisfied is that there are not enough skilled resources (62%).

• Those most often primarily responsible for designing and deploying predictive analytics are data scientists (in 31% of organizations), and business intelligence and data warehouse team (27%).

52%

48%

34%

29%

28%

What barriers exist to making desired changes in your predictive analytics technology?

Lack of resources

Lack of awareness

Business Case not strong enough

Too much training or skills required

No budget



Overcome Technical ObstaclesVariety of Challenges Exist

Integrating into architecture

50%

Cannot access necessary data30%

Algorithms not appropriate

26%

Results are not accurate enough

21%

Too hard to use20%



Ensure Business and IT Work Together

67%say that usability is the top buying criteria for predictive analytics software.59% say that functionality is very important.


27%say that they fund predictive analytics initiatives from LOB IT budgets; up from 19% 29% say they fund from general business budget; down from 44%.

29%Prefer to purchase predictive analytics as a stand-alone technology.Down from 44% in previous research.


Consider Using Big Data for Predictive

Related Research:• Organizations

historically have used flat files.

• Growth is distributed across many categories.

• Establishment of NoSQL and growth of Hadoop is noted.

12%63%Flat files

9%63%RDBMS

13%52%DW ApplianceIn-MemoryDatabases

Hadoop

NoSQL

Current

Deployment

24%31%

29%25%

20%25%


Plan in Next 24 Months

Specialized DBMS 10%20%


Make the Business Case

Simple Steps to Building a Business Case1. Understand your audience.2. Paint the vision for predictive analytics.3. Craft the case for predictive analytics.4. ROI benefits comes from multiple sources.5. Be specific with facts.

A business case captures the reasoning for initiating a project or task. A compelling business case adequately captures both the quantifiable and unquantifiable characteristics of a proposed project.


The Potential of Big Data and Predictive


Questions?

Twitter@ventanaresearch@marksmithvr@tonycosentinvr

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

Bloghttp://blog.ventanaresearch.com

© 2015 Ventana Research30 © 2015 Ventana Research

Best Practices and Trends in Big Data and

Predictive InsightsMark Smith, CEO & Chief Research Officer

Tony Cosentino, VP & Research Director

@ventanaresearch In/ventanaresearchblog.ventanaresearch.com

@marksmithVR

@tonycosentinoVR

https://twitter.com/%23%21/TonyCosentinoVR

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

http://tonycosentino.ventanaresearch.com/



Ventana Research Best Practices in Big Data and Predictive Insights Presentation at Teradata...

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