Check out this presentation from Pentaho and ESRG to learn why product managers should understand Big Data and hear about real-life products that have been elevated with these innovative technologies. Learn more in the brief that inspired the presentation, Product Innovation with Big Data: http://www.pentaho.com/resources/whitepaper/product-innovation-big-data

1. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75551
Ben Hopkins
Product Marketing Manager - Pentaho
Jim Stascavage
Vice President of Engineering - ESRG
Big Data for Product
Managers

2. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75552
① Data Trends & Data Types
② Big Data Challenges & Technology Solutions
③ Big Data & Product Innovation
④ Case Study: ESRG
Quick Agenda

3. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75553
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

4. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75554
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

5. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75555
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

6. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75556
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

7. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75557
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

8. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75558
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

9. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75559
Why Does it Matter for Product Management?
New Opportunities for
Valuable Products
Potential for Built-in
Intelligence
Future-Proof for Scale
and Growth

10. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755510
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

11. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755511
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

12. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755512
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’

13. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755513
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

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

15. Presentation overview
• Introduc)on
• Os)aEdge
industrial
predic)ve
analy)cs
and
Pentaho
integra)on
• Marine
case
example:
Saving
fuel
and
avoiding
failure
• Liquid
packaging
case
example:
Predic)ng
)me
to
failure
• Conclusion
15

16. Founded
2000
10+
years
product
development
History
Exper6se
Product
Reliability
engineering
So>ware
&
architecture
Big-­‐Data
Currently
remotely
monitoring
3,000+
assets
daily
Comprehensive,
6ered
solu)on
Markets
served
Defense
&
Commercial
Mari6me,
Process,
Power
Gen
ESRG overview
Focus
&
results
Turn
data
into
ac6onable
informa6on;
examples:
• $70,000
annual
savings
per
gas
turbine
• Defer
~60%
of
in-­‐person
equipment
assessments

17. Industrial Analytics to create
significant value…
17
Cisco
es)mates
50B
devices
connected,
crea)ng
addi)onal
$14T
in
profits
over
next
decade
McKinsey
&
Co.
es)mates
up
to
$6.2T
in
annual
value
created
annually
by
2025
General
Electric
es)mates
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

18. …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/ 18
Avoid
breakdowns
&
down6me
Op6mize
maintenance
Improve
environmental
compliance
Improve
opera6ons
Improve
energy
efficiency
Marine
Vessel
$500K-­‐$1.5M
Per
year
savings
poten6al
Liquid
filling
10-­‐20%
up6me
10-­‐50%
maint
cost
Per
year
savings
poten6al

19. 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
Reduc)on
gears/transmissions
Refrigera)on
Desaliniza)on
Fuel
flow
meters
Misc
3/24/15 ESRG Confidential 19

20. 20
Mechanical
equipment
Fuel/energy
consump)on
Control
&
opera)ons
Emissions,
Discharges,
etc
1000s
of
sensors
per
asset…
…across
an
enterprise
…automated
analy6cs
avoid
need
for
large
Department
to
analyze
“Big
Data”
Automated
analy6cs
&
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
analy)cs
provide
fuel/energy
efficiency
and
equipment
health
ESRG uses analytics to turn
Big Data into actionable information

21. OstiaEdge overview
21
On-­‐site
/
Onboard
Local
Central
/
Shore
Central
or
Cloud
Plant
Edi)on
Central
Edi)on
Business
Intelligence
• Local
data
acquisi)on
&
qualifica)on
• Local
analy)cs
and
presenta)on
• Real-­‐)me
data
viewer
• Machine
state
and
condi)on
analy)cs
• Embedded
Keble
(PDI)
• Web/cloud
presenta)on
• Workflow
management
• Security/user
mgt
• Dashboards
&
analyzers
• ETL
• Email
reports
• Mobile
• NEW:
PDI:
R
&
Weka
embedded

22. Example data flow and integration
3/24/15 ESRG Business Sensitive 22
Data
Input
External
ETL
PDI
RUL
Algorithm
&
Engine
DSP
R-­‐Plugin
Customer
maintenance
system
External
Os)aEdge
Analy)cs
Os)aEdge
Presenta)on
Analyzers
&
Dashboards
Custom
Dashboards
BI
Cube(s)
Email
Reports
Configura)on
Management
New

23. Saving Fuel and Avoiding Failure
Case Example: Maritime
23
Op)mize
generators
$50K-­‐$250K
Tune
equipment
$50K-­‐$150K
Avoid
failure
$10K-­‐$500K
Large
ship
owner
trying
to
reduce
fuel
&
failures/down)me:
• $5-­‐10M
in
fuel
cost
per
year
• $10,000
per
day
for
vessel
down)me
Solu6on
Situa6on
Use
Os)aEdge
&
embedded
Pentaho
ETL
to
make
beber
opera)ons
&
maintenance
decisions
• Embedded
Pentaho
ETL
for
generator
op)miza)on
&
dashboards
• Os)aEdge
analy)cs
for
failure
avoidance

24. New: Predicting Time to Failure
Case Example: Liquid packaging
24
R
&
Weka
based
RUL
algorithms
Predict
Failure
Standard
&
Custom
Dashboards
Exec
Transparency
Global
liquid
packaging
OEM
with
two
goals:
• Improve
customer
up)me
• Reduce
unnecessary
maintenance
and
extra
parts
consump)on
Solu6on
Situa6on
Leverage
Pentaho
PDI
+
Os)aEdge
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

25. Industrial Analytics Opportunity
25
Os6aEdge
+
Pentaho
• ETL
• Diagnos)cs
• Analyzers
&
Dashboards
• New:
Prognos?cs
with
R
&
Weka
Lower
Cost
&
Faster
• Small
team
• Rapid
&
agile
algorithm
development
• Easy
integra)on
• Flexible
implementa)on
Avoid
breakdowns
&
down6me
Op6mize
maintenance
Improve
environmental
compliance
Improve
opera6ons
Improve
energy
efficiency

26. 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.

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

28. 15
Compe66ve
Intelligence
Ques6ons
that
Product
Managers
Need
To
Ask
Mar
13
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