1. The document discusses how organizations can leverage data, analytics, and insights to fundamentally change and pioneer new business models. 2. It emphasizes that data analytics cannot be accomplished in a silo and must involve the entire organization. Modern cloud platforms, software methodologies, and data tools are needed. 3. Examples are provided of how various organizations have used tools like Pivotal Greenplum to gain insights from data to solve problems in areas like predictive maintenance, risk management, and national security.

1. Innovating with Data and Analytics
Jacque Istok
@jstok

2. © Copyright 2017 Dell Inc.2
The New Normal
DATA DEVICES
Law
Enforcement
Media
Banks
Delivery
Services
Marketers
Government
Private
Investigators
/Lawyers
Individuals
Employers
Data
Users/Buyers
Analytic
Services
Advertising
Catalog
Co-ops
List
Brokers
Websites
Information
Brokers
Credit
BureausMedia
Archives
Data
Aggregators
FINANCIAL
GOVERNMENT
PHONE/
TV
INTERNETMEDICAL
RETAIL

3. 3© 2017 Pivotal Software, Inc. All rights reserved.
Great organizations leverage software,
analytics, and insights to take better actions
and fundamentally change and pioneer entirely
new operational business models

4. Analytics Within Digital Transformation
Cannot Be
Accomplished
In A Silo

5. © 2017 Pivotal Software, Inc. All rights reserved.
Open Source Innovation
Massively Parallel
Processing
Data Science and
Machine Learning
Multi-Cloud and
Flexible Deployment
Agile
Development
Our View on Next Generation Data Platforms

6. DATA IS IMPORTANT
How can my organization capitalize on
this trend?

7. Using Our Process For Solving Analytics
Pair Programming / Solutioning
Retros
Iterative Development
Greenplum Open Data Platform ANSI-compliant SQLStandups
User Centric Data Science

8. TRANSFORMING INTO A
MODERN SOFTWARE COMPANY
Building high-quality software at start-up speed
requires modern software methodologies, cloud
platform, and data tools
Modern
Cloud Native Platform
01
Modern
Software Methodology
02
Modern
Data Tools
03

9. Modern Cloud-Native Platform
Building high-quality software at start-up speed
requires modern software methodologies, cloud
platform, and data tools
01

10. Provide the Business With Solutions
“You’ve got to start with
the customer experience
and work back toward
the technology - not the
other way around.”
- Steve Jobs
02

11. Modern Software Methodologies
A modern platform requires a modern approach
to software development
Building a strategy for
product development
Building a quality product
at startup speed
An organization optimized
to respond to disruption
1MODERN SOFTWARE
METHODOLOGY
Agile Software
Methodologies
2MODERN SOFTWARE
METHODOLOGY
Lean Startup
Techniques
3MODERN SOFTWARE
METHODOLOGY
Optimized for
Change
02

12. METHODOLOGIES TIP
Start with a
technology solution
and look for a
problem to solve
Start with a business
problem or
opportunity and map
to technology solution
02

13. User Centered Design
“A design approach that supports the entire development
process with user-centered activities, in order to create a
product that is easy to use and of added value to the intended
users.”
www.usabilitynet.org
02

14. Is It Useful?
usage = value rarely used = waste
02

15. © Copyright 2017 Dell Inc.15
03

16. ANALYTICAL
APPLICATIONS
NATIVE INTERFACES
MULTI-
STRUCTURED DATA
SOURCES &
PIPELINES
Structured Data
JDBC, ODBC
SQL
ANSI SQL
USERS
FLEXIBLE
DEPLOYMENT
Local
Storage
Other
RDBMSes
SparkGemFire
Cloud
Object
Storage
HDFS
JSON, Apache AVRO, Apache Parquet, XML, & More
Teradata SQL
Other DB SQL
Apache MADlib
ML/Statistics/Graph
Python. R,
Java, Perl, C
Programmatic
Apache SOLR
Text
PostGIS
GeoSpatial
Custom Apps BI / Reporting Machine Learning AI
IT Dev
Business
Analysts
Data
Scientists
On-Premises
Public
Clouds
Private
Clouds
Fully
Managed
Clouds
MODERN CLOUD
ANALYTICS PLATFORM
KafkaETL
Spring
Cloud
Data Flow
Massively
Parallel
(MPP)
PostgresSQL
Kernel
Petabyte
Scale
Loading
Query
Optimizer
(GPORCA)
Workload
Manager
Polymorphic
Storage
Command
Center
SQL
Compatibility
(Hyper-Q)
Modern Cloud Analytics Platform03

17. Common Business Problems
With Potential for High ROI Using Data
FRAUD MANAGEMENT RISK MANAGEMENT
CYBERSECURITY MANUFACTURING
PREDICTIVE MAINTENANCE
ELECTRICITY GRID

18. Modern Data Tools
Common Functional Requirements
1. High speed ingestion (sensors, financial transactions, etc)
2. Data consolidation (join and cross reference)
3. SQL for structured data
4. Process higher level data constructs: Graph, Geospatial, Text
5. SLA driven queries on big data
6. Real time access for consumers and applications
03
CAPTURE ANALYZE APPLY

19. Modern Data Tools
Common Non-Functional Requirements
1. Open Source Software
2. Platform Agnostic
3. Production Proven
4. Scale out Growth
5. Security Centric
6. Cloud Ready
OPEN
PRODUCTION
PROVEN
SCALE OUT SECURE
03

20. Pivotal Data Suite
Maps to Common Business Requirements
Pivotal Greenplum
Comprehensive
Open source
Data Warehouse
Pivotal HDB
Open source Hadoop Native,
high-performance, scale-out SQL
analytics - Apache HAWQ
Pivotal GemFire
Open source application &
transactional database
based on Apache Geode
------- BIG DATA ------- ------- FAST DATA -------
03

21. COMMON DEPLOYMENT PATTERNS

22. High Speed Ingestion
Data Capture
Problem: Fast incoming data needs to be captured
Example use cases:
1. Financial transactions
2. Sensors from industrial equipment
3. People using public resources: tracking on Bridge/Tunnel, etc
Data needs to be captured fast in memory
Data needs to be modified, consolidated, updated in memory
Data needs to be streamed and loaded to consolidated platform
Sensor Data

23. Hot
Analytical Processes That Need Fresh Data
Overnight ETL / ELT jobs can be too slow
App 1 App 3
App 2
Transactional
System
High
Speed
Micro
Batching
TRANSACTIONS ANALYTICS• Analytical processes
need access to the
latest data
• ETL/ELT processes
are expensive and hard
to maintain
• A continuous flow of
input keeps analytics
up to date
MPP

24. Consumer Facing Analytics
Recommendation Engine Push
Problem: Big Data Analytics Access for Consumers
Example use cases:
1. eCommerce
2. Financial Recommendations
3. Entertainment Online
Big Data Needs to be Stored & Analyzed
Machine learning algorithm generates recommendations
Consumer scale app needs real time recommendation
Consumer Specific Recommendations

25. Operationalized insights need to be real time
Combination of SQL Analytics and NoSQL event-driven transactions is needed
App 1 App 3
App 2
Real Time
System
TRANSACTIONS ANALYTICS• Data Insights must be
immediately pushed to
applications
• Apps should be able to
react in real-time to
analytical findings
Machine Learning
Advanced Analytics
ANSI SQL
APIs /
NoSQL
Data Insights
MPP

26. Exemplified Use-Case - Predictive Maintenance
Evaluates live data
“According to historical trends, there’s an
80% chance this equipment would fail in
the next 12 hours”
Learns with historical trends
"How were the temperature and vibration
sensors reading when the latest failures
happened?"
Live data becomes
historical over time
Sensor data
Historical
Real-time
Take action
Smart
system

27. Reference Architecture
Sensors
Update with
findings
Consumer
Application
Machine
Learning
Real-time
data streams
Apply
Improve
Management
Dashboard
Reporting

28. Global Energy Corp. – Predictive Maintenance
Challenges
• Failing turbines causing issues with power
generation
• Unable to store & process fire-hose of data
Solution
• High velocity data ingestion from Turbines
• Store PBs of turbine data
• Machine learning and SQL analytics
• very low latency and high speed data access
Time
EquipmentCondition
Broken
Cost to repair
Failure starts to
occur
Early signal 1
Early signal 2
Early signal 3
Audible noise
What we want to avoid

29. Telco Network Quality KPIs
29
Fast Data Ingest and Real-time Analytics
Challenge:
• Provide network quality KPIs to Brazil's National Telco Agency
• Capture, analyze and guarantee data consistency for millions
of data points / sec.
• Enable near real-time analytics over the high volume and fast
data being transacted
Solution:
• Pivotal GemFire and Greenplum allow combined fast
low-latency in-memory transactions and near real-time
analytics
• Real-time data insights on cell phone network quality KPIs
• Data Science engagement for improving the network quality

30. Firm Wide Risk Management
Customer: US Based Top Investment Bank
Business Problem
Global platform for storage, query and analytics of firm’s
trades, orders, assets, and risk
Challenges
High speed incoming transactions
Petabytes of data storage
Advanced machine learning and data science
Operational risk fed back to trading systems real time
Firm wide risk view to executive team
Solution
Gemfire for high speed transaction data
Greenplum for storage, reporting, analytics
Live feedback loop

31. National Security
Customer: Mid Size Country With Security Concern
Government Problem
Need to monitor national security for protection of citizens
Challenges
Petabytes of data storage
Analysis of structured data, geospatial data, text data and
social graphs in consolidation
Need real time response for public safety officers
Solution
Greenplum for data consolidation and joining
Greenplum for geospatial, text, graph and machine learning
Gemfire for distribution of analytics result to field officers

32. Pivotal Office Tour

33. Breakfast
• Gets people into the office at the same time
• People tend to have lunch at the same time
• Maximizes pairing and collaboration time

34. Pair Programming
● Two heads are better than one
● Combination of experience
● Continuous code review
● Great for training
● Rotation of pairs → no knowledge silos

35. Continuous Integration
● Information radiators
● Test Driven Development
● Source of truth
● Smart dependency test graph
● Unified code base

36. Standup
● Minimize the meetings
● Focus on shared understanding
● Share concerns
● Encourage continuous improvement

37. 1. Data is a growing asset
2. Need tools to derive business value from data
3. Platform Assumptions: Security, Open Source,
Platform Agnostic, Scalable, Performant
4. Agile Methodology: fast feedback, learn, adapt
5. Let’s work together to add value
Key Points Summary

38. Pivotal Greenplum: Learn More
Find out more about Pivotal Greenplum at
https://pivotal.io/pivotal-greenplum
OR learn more about the open source at
http://greenplum.org/
OR give it a try yourself at
Amazon AWS or Microsoft Azure or via Download

39. Let’s build something
MEANINGFUL