Analytics in the cloud is becoming more popular as organizations look for ways to increase the value of their analytics investments and lower the total cost of ownership (TCO) of their analytics projects. At the same time, organizations may lack the insights required to make the business case for transitioning analytics to the cloud. By understanding the business and technical drivers of cloud analytics platforms and by evaluating the common use cases, organizations can make informed decisions and gain the buy-in they need to leverage analytics in the cloud. Join Pivotal and EMA to gain insight into how cloud analytics can enhance your organization’s ability to get business value out of data. This web seminar will help organizations understand the value proposition of cloud analytics adoption and provide insights into the following: - The key drivers of analytics in cloud adoption, including business, technical, and financial - Perceptions of cloud vs on-premise solutions - Cloud success factors and business benefits - The combination of cloud access and open source - The importance of agility and workloads to create efficient analytics in the cloud environment - Use cases identifying key success factors

1. Cloud Analytics: Getting the most
out of cloud implementations
April 2016

2. Lyndsay Wise, Research
Director, EMA
Jeff Kelly, Data Evangelist,
Pivotal
Ian Andrews, VP of
Products, Pivotal
Today’s Speakers

3. Submit your questions to the panel using:
@wiseAnalytics @jeffreyfkelly #cloudAnalytics
© 2016, Enterprise Management Associates
Join the Conversation

4. NEED STANDARD WEBINAR LOGISTICS SLIDE HERE

5. • Business Drivers and Perception of
Cloud Analytics
• Financial Drivers and TCO
• Business Agility and Smart Applications
• Cloud Analytics Success factors
• Cloud Analytics Use Cases
• Summary & Q&A
Agenda for Today’s Webinar

6. Topic 1: Business Drivers and
Perceptions of Cloud Analytics

7. Cloud is better They are the
same
On-premises is
better
Ease of Adoption 47.1% 43.3% 9.6%
Ease of Technical Distribution 48.6% 38.9% 12.5%
Functionality 45.7% 44.2% 10.1%
Time to Implementation 47.6% 41.3% 11.1%
Total Cost of Ownership 53.4% 35.6% 11.1%
Perceptions of Cloud vs. On-Premises Use

8. 42.8%
Data security
37.5%
Lower
administration
33.7%
Ease of
integration
Cloud Benefits

9. Infrastructure1
Platform2
Software3
A SEPARATION OF CONCERNS
Modern cloud platforms enable software to
be developed & deployed without regard
to the underlying infrastructure

10. Modern Software Methodology Modern Cloud Platform
BEING A CLOUD NATIVE COMPANY
Is about building high-quality software—at start-up speed—
leveraging a modern cloud platform, a modern development
process, and the power of Big Data to continuously drive
innovation
Big Data

11. Stream + Batch Processing
Programming + Operating Model
Cloud-Native Platform
Microservices FrameworkPlatform Runtime
Hadoop
DW
Spark
Microservices and Polyglot Persistence
IMDG
K/V Store
Relational DB
Big Data &
Machine Learning
Modern Cloud-Native Data Architecture
Cloud Infrastructure

12. 18.6%
Better information
visibility/higher
efficiencies
16.6%
Improved speed to
implementation
Top Business Drivers

13. Speed of Deployment
Speed to Insight
Agility and Adaptability
Data Gravity/Data Movement

14. Topic 2: Financial Drivers and TCO

15. Why Organizations are Looking at Cloud

16. 19.0%
Time savings
18.4%
ROI
17.0%
Effective use of
biz resources
Delivering Value Through Cloud – Moving
Towards Revenue Growth

17. Top Financial Drivers and TCO

18. Great software companies leverage Big Data
to fundamentally change the consumer
experience and pioneer entirely new
business models

19. DISRUPTIVE AND CONVERGING TRENDS IN BIG DATA
(Data)
Microservices
Loosely coupled
services architecture,
bounded by context
Cloud-Native
Platforms
Enabling continuous
delivery & automated
operations
Open Source
Database
Innovation
Extreme scale &
performance advantages,
built for the cloud
Machine
Learning
Use of predictive
analytics to build
smart apps

20. Topic 3: Business Agility and Smart
Applications

21. Importance of Agility

22. Business Goals

23. Scale-out
analytic
database
Model API Cloud Native
Application
Platform
Data
Sources
0 5
Smart Apps: Models Manifesting as Microservices

24. 1) Engage
2) Capture3) Model
4) Suggest
A Recipe for Smart Apps

25. Topic 4: Cloud Success Factors

26. 64.8%
Successful
3.3 %
Not successful
13.7%
Neither successful,
nor unsuccessful
Success Levels of Cloud Deployments

27. Levels of Technical Success

28. Workloads

29. App
Development
Data
analytics
Cloud-native
App platform
Data
Science
& Model
building
Data
Microservice
APP
Must support scale-out
query processing
Must deliver as an API
Must embrace agile development,
focus on outcomes
Must support
microservices, agile dev, and
connect to big data analytics
A Real-World Example

30. Topic 5: Cloud Use Cases

35. Summary and Q&A

36. Question and Answer

37. http://enterprisemanagement.com/ http://springoneplatform.io/
More resources at pivotal.io/resources and https://blog.pivotal.io/
Resources

38. Let’s build something
MEANINGFUL