Big Data has been a "buzz word" for a few years now, and it's generated a fair amount of hype. But, while the technology landscape is still evolving, product companies in the software, web, and hardware areas have actually led the way in delivering real value from data sources like weblogs, sensors, and social media as well as systems like Hadoop, NoSQL, and Analytical Databases. These organizations have built "Big Data Apps" that leverage fast, flexible data frameworks to solve a wide array of user problems, scale to massive audiences, and deliver superior predictive intelligence. Join this webinar to learn why product managers should understand Big Data and hear about real-life products that have been elevated with these innovative technologies. You will hear from: - Ben Hopkins, Product Marketing Manager at Pentaho, who will discuss what Big Data means for product strategy and why it represents a new toolset for product teams to meet user needs and build competitive advantage - Jim Stascavage, VP of Engineering at ESRG, who will discuss how his company has innovated with Big Data and predictive analytics to deliver technology products that optimize fuel consumption and maintenance cycles in the maritime and heavy industry sectors, leveraging trillions of sensor data points a year. Who Should Attend Product Managers, Product Marketing Managers, Project Managers, Development Managers, Product Executives, and anyone responsible for addressing customer needs & influencing product strategy.

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Big Data for Product Managers
AIPMM WEBINAR
SERIES

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Use: #AIPMM #ProdBOK
#BigData
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Ben Hopkins
Product Marketing Manager - Pentaho
Jim Stascavage
Vice President of Engineering - ESRG

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① Data Trends & Data Types
② Big Data Challenges & Technology Solutions
③ Big Data & Product Innovation
④ Case Study: ESRG
Quick Agenda

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Pentaho
We Enable & Empower Data-Driven Businesses
Customer momentum
• Over 1,500 commercial customers
• Over 10,000 production deployments
Innovation through open source
• Open, pluggable, purpose-built for the future
• Sustained leadership in Big Data ecosystem
with technology innovation
Modern, cohesive business analytics & data integration platform
• Full spectrum of analytics for all key roles
• Embeddable, cloud-ready analytics
• Broadest and deepest Big Data integration

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2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
40000
30000
20000
10000
ExabytesofData
Source: IDC’s Digital Universe Study, sponsored by EMC, April 2014
We are
ONLY here!
77% of data
relevant to
enterprises will
be unstructured
At the Beginning of a Data Revolution
40%
Machine Data
50X
Growth

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The Most Compelling Insights Come from
Blending Data
38% 38%
16%
4% 4%
Existing
Underutilized
“Dark Data”
More Customer
& Supplier
Detail
Social Media
Content
Commercially
Available Data
Publicly
Available Data+ + + +
Big ROI Opportunity
“Which source of data represents the most immediate opportunity to transform your business?”
Summary of Analyst Surveys on Big Data: Gartner, Forrester and Ventana Research

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Traditional ERP, CRM, and transactional data
Web logfile, clickstream and social media post data
Human language, text, email, audio, video, and image data
Sensor, machine-to-machine, network, and geospatial data
More Data Types of Interest

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Addressing New Challenges
Flexibility to land a wide variety of
data types in the data store thanks
to schema-on-read
Highly varied data structures are
difficult to bring into relational DBs
and blend for analysis
‘Divide and conquer’ with distributed
storage and processing on
affordable hardware
Explosive growth in data volume is
straining existing data warehouse
infrastructure
Leverage high performance random
read/write access, streamline
analytic queries for speed
Time-sensitive data is being rapidly
generated, and there is pressure to
deliver faster analytics
Big Data Challenges Technology Solutions

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Big Data Technologies
Not an Exhaustive List
Hadoop
• Distributed file
system and
MapReduce
framework
• Ideal for high
volume, diverse
data processing
NoSQL
• Broad group of
DBs with flexible
data models:
graph, key/value,
document, etc
• Often ideal for
rapid ingestion,
random
read/write
access
Analytic DB
• Relational DB
designed for high
performance BI
• Ideal for complex
analytic/OLAP
queries

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Why Does it Matter for Product Management?
New Opportunities for
Valuable Products
Potential for Built-in
Intelligence
Future-Proof for Scale
and Growth

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Big Data Products
Source: Aaron Kimball, “The secrets of designing and building big data apps,” venturebeat.com, 12/24/2013
• Data structured in pre-defined
(relational) way
• Designed for specific problem; data
access, interfaces & protocols
reflect that purpose
• Application data often isolated from
other relevant data
• Example: CRM system for storing
customer info, prioritizes calls
based on purchase data
Traditional Application
• Accommodates various data
structures (and speeds)
• Framework that can potentially
solve multiple problems
• Includes process for ingesting new
data sources and building &
iterating on predictive models
• Example: Adds in prioritization of
calls from predictive model on
customer behavior & purchase data
Big Data Application

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To create completely
individualized apps
• Understand all relevant
user intents, actions
• Leverage mobile,
behavioral, & profile data
• Incorporate more user
data for a more complete
view
• Predictive modeling to
anticipate and respond
Big Data + Predictive Analytics
Source: “Predictive Apps are the Next Big Thing in Customer Engagement,” Forrester, 6/25/2013

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Architecture Patterns We See
Weblog & social
media data
Machine, sensor, &
device data
Customer profile data
Existing application
data
Unstructured &
Semi-structured
Structured &
Relational
Hadoop
Cluster
Relational
Database
NoSQL Store
Analytic
Database
Client-side User
Interface
Web-based experience
including embedded
visual analytics
NoSQL
‘Massive
Archive’
‘Operational
Speed Layer’
‘Existing System
of Record’
‘Powerful BI
Performance’

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In Brief - Use Cases & Products
“Personalization Engines”
• Using predictive analytics on web-
sized data sets to individualize
customer relationships
• Example: RichRelevance delivers
personalized content to customers
online and in-store, based on Big
Data predictive analytics on over
50 mln shopping sessions per day.
• Tech: Hadoop, Hbase, Hive,
others
“Machine Data Analysis”
• Monitoring and analyzing sensor
and network data to understand
equipment/device performance
• Example: Ruckus Wireless
leverages Big Data to provide
decade-long analysis of semi-
structured WiFi network data for
telecom carriers and enterprises
• Tech: Hadoop, Vertica, others

16. ESRG Customer Case Example
Industrial Predictive Analytics
Jim Stascavage – VP, Engineering

17. Presentation overview
• Introduction
• OstiaEdge industrial predictive analytics and Pentaho
integration
• Marine case example: Saving fuel and avoiding failure
• Liquid packaging case example: Predicting time to failure
• Conclusion
17

18. Founded 2000
10+ years product development
History
Expertise
Product
Reliability engineering
Software & architecture
Big-Data
Currently remotely monitoring 3,000+ assets daily
Comprehensive, tiered solution
Markets
served
Defense & Commercial
Maritime, Process, Power Gen
ESRG overview
Focus &
results
Turn data into actionable information; examples:
• $70,000 annual savings per gas turbine
• Defer ~60% of in-person equipment assessments

19. Industrial Analytics to create
significant value…
19
Cisco estimates 50B
devices connected,
creating additional $14T
in profits over next
decade
McKinsey & Co.
estimates up to $6.2T
in annual value
created annually by 2025
General Electric
estimates the market for
industrial internet
technology and
services to grow to
$500B by 2020
Sources: Manyika, James, others, “Disruptive technologies: Advances that will transform life, business and the global economy” McKinsey
Global Institute, May 2013
Chambers, John, “Internet of Evertyhing”, Cisco, February 21, 2013
General Electric press release, June 18, 2013

20. …ESRG uses data to improve
return on industrial assets
Source: Bringing the industrial internet to the maritime industry and ships into the cloud
;http://www.esrgtech.com/company/ESRGcontent/ 20
Avoid
breakdowns &
downtime
Optimize
maintenance
Improve
environmental
compliance
Improve
operations
Improve
energy efficiency
Marine Vessel
$500K-$1.5M
Per year savings potential
Liquid filling
10-20% uptime
10-50% maint cost
Per year savings potential

21. Our technology monitors 3,000+ assets
~3,000 total assets monitored
AC plants
Hydraulic systems
Compressors
GT engines
GT lube oil systems
Diesel engines
GT Generators
Reduction gears/transmissions
Refrigeration
Desalinization
Fuel flow meters
Misc
3/6/2015 ESRG Confidential 21

22. 22
Mechanical equipment
Fuel/energy consumption
Control & operations
Emissions, Discharges, etc
1000s of sensors per asset… …across an enterprise …automated analytics avoid need for
large Department to analyze “Big Data”
Automated analytics & Experts overcome Big Data challenges
Once per second data = 86,400 points/day
1000 data points = 2.6B points/month
100 ships = 3.1 trillion
data points per year
Automated analytics provide fuel/energy
efficiency and equipment health
ESRG uses analytics to turn
Big Data into actionable information

23. OstiaEdge overview
23
On-site / Onboard
Local
Central / Shore
Central or Cloud
Plant Edition Central Edition Business Intelligence
• Local data acquisition &
qualification
• Local analytics and
presentation
• Real-time data viewer
• Machine state and
condition analytics
• Embedded Kettle (PDI)
• Web/cloud presentation
• Workflow management
• Security/user mgt
• Dashboards &
analyzers
• ETL
• Email reports
• Mobile
• NEW: PDI: R & Weka
embedded

24. Example data flow and integration
3/6/2015 ESRG Business Sensitive 24
Data Input
External
ETL
PDI
RUL Algorithm
& Engine
DSP R-Plugin
Customer
maintenance
system
External
OstiaEdge
Analytics
OstiaEdge
Presentation
Analyzers &
Dashboards
Custom
Dashboards
BI Cube(s)
Email Reports
Configuration
Management
New

25. Saving Fuel and Avoiding Failure
Case Example: Maritime
25
Optimize generators
$50K-$250K
Tune equipment
$50K-$150K
Avoid failure
$10K-$500K
Large ship owner trying to reduce fuel & failures/downtime:
• $5-10M in fuel cost per year
• $10,000 per day for vessel downtime
Solution
Situation
Use OstiaEdge & embedded Pentaho ETL to make better operations &
maintenance decisions
• Embedded Pentaho ETL for generator optimization & dashboards
• OstiaEdge analytics for failure avoidance

26. New: Predicting Time to Failure
Case Example: Liquid packaging
26
R & Weka based RUL algorithms
Predict Failure
Standard & Custom Dashboards
Exec Transparency
Global liquid packaging OEM with two goals:
• Improve customer uptime
• Reduce unnecessary maintenance and extra parts consumption
Solution
Situation
Leverage Pentaho PDI + OstiaEdge to predict Remaining Useful Life (RUL)
• ETL to bring in enterprise level data
• Data Science Pack (R & Weka) used to design algorithms to predict RUL
• Customized embedded dashboard

27. Industrial Analytics Opportunity
27
OstiaEdge +
Pentaho
• ETL
• Diagnostics
• Analyzers &
Dashboards
• New:
Prognostics
with R &
Weka
Lower Cost &
Faster
• Small team
• Rapid & agile
algorithm
development
• Easy integration
• Flexible
implementation
Avoid
breakdowns &
downtime
Optimize
maintenance
Improve
environmental
compliance
Improve
operations
Improve
energy efficiency

28. Q and A …
Ask Questions. Our team is standing
by to help.
The webinar slides will be posted to
our website and our
Slideshare.net/aipmm page.
The webinar recording will be posted
at AIPMM.net for members.

29. Product Management Body Of
Knowledge
We will pick one
winner from our
attendees.

30. 15 Competitive Intelligence Questions that Product Managers Need To Ask Mar 13
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