The energy industry is well known to be laggard adopters of new technology. However, industry challenges such as aging assets & workforce, increased regulatory scrutiny, renewable energy sources, depressed commodity prices, changing customer expectations, and growing data volumes are pushing companies to explore new technologies to help solve these problems. Learn how energy companies are leveraging Hortonworks Open and Connected Data Platforms to provide the predictive analysis and data insights to optimize performance for the energy industry. Speaker Kenneth Smith, General Manager, Energy, Hortonworks

1. Hortonworks Industrial Data Platform
IIoT & Predictive Analytics in the Energy
Industry
Kenneth Smith – General Manager, Energy
@KennethSmith99

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Internet of Things Data Sources
 User Generated Content (Web & Mobile)
– Twitter, Facebook, Snapchat, YouTube
– Clickstream, Ads, User Engagement
– Payments: Paypal, Venmo
 Internet of Anything (IoAT)
– Wind Turbines, Oil Rigs, Cars
– Weather Stations, Smart Grids
– RFID Tags, Beacons, Wearables
What generates data?

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Industrial IoT Market Opportunity Estimates
“In other words, the industrial internet will be worth more than twice the consumer internet”
https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of-
internet-of-things-forecasts-and-market-estimates-2016/#ad68a67292d5

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Why Hortonworks for IIoT??
 Technology to deliver the only end-to-end OPEN SOURCE IoT data platform for “industrials”.
 It’s not just about time-series data; it’s the ability to collect, manage, and analyze all
pertinent structured & unstructured data sets related to an industrial asset, operation,
process, piece of equipment, etc. in in addition to time-series.
 Open Connected Data Platforms enables OT/IT/ET convergence to build descriptive,
predictive, & prescriptive applications.
 An open source IIoT platforms allow operators to maintain control over their data and
analytics vs. a ”closed” OEM’s IIoT product telling them when their own equipment needs
replacing.
 An open IIoT platform is applicable across all asset intensive industries with “moving metal”;
oil & gas, utilities, mining, manufacturing, automotive, transportation, agriculture, etc.
 “Data is not a competitive advantage. It’s the algorithms you build to analyze your data
that will differentiate you from your competitors.”

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Is the Energy Industry Ready to Embrace an Open Model?
http://www.lockheedmartin.com/us/news/press-releases/2016/january/160114-mst-us-exxonmobil-awards-
lockheed-martin-next-generation-refining-and-chemical-facility-automation-system-contract.html
ExxonMobil representatives
express frustration when
observing step change
improvements in adjacent
industries enabled by open
technologies. Those adjacent
industries have deployed
significantly higher function
software that have lowered
lifecycle cost and delivered
higher return on investment.
The explosive growth of technologies driven by the Internet of Things (IoT) including
cloud computing, mobile computing, embedded computing, and consumer electronics
makes it obvious that the mainstream industrial automation industry can deliver
more value with the adoption of an open, multi-vendor platform approach.
http://www.automation.com/automati
on-news/article/exxonmobil-to-build-
next-generation-multi-vendor-
automation-architecture

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Industrial Processes Create Large Amounts of Data
Always on, always connected devices generate a
constant stream of data related to the operations of
industrial businesses
These datasets contain:
• What happened?
• Why something happened or not?
• Quantification of events
These datasets go by many names:
• “SCADA Data”
• “Control System Data”
• “Historian Data”
• “Machine Data”
• “Measurement Logs”
How are my …
People?
Processes?
Equipment?

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Why Collect this Data with Hadoop?
• Scale and Flexibility
– Linear & low cost scale out of storage &
processing
– Bring compute to data
– Multi tenant environment that allows multiple
modes of simultaneous ingestion & interaction
• Increased value of data
– Reduce the friction of data access “everything is
accessed in one place”
– Simplified or new analytic applications
• Democratize data
– Single point of access
– Simplified access & security controls
DATASYSTEMSOURCES
SCADA ERP EPM
Governance
&Integration
Security
Operations
Data Access
Data Management
APPLICATIONS
Business
Analytics
Advanced Process
Control
Operations
Planning Suites
AG. Image source "© Siemens AG 2015, All rights reserved"

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My Industrial Dataset is in Hadoop! Now what?
Uses for Industrial Datasets
• Condition Based Monitoring
• Single View of an Asset
• Dashboards & Mobile Applications
• Statistics & Predictive Analytics
• Event Based Surveillance
• Remote Operations Support

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Field Data Capture Office or Datacenter
Hortonworks Industrial Data Analytics Platform – In Practice
OPC UA/DA, WITSML
Video, Audio
Commodity Market
Weather, Environmental
Social Media
IoT, Machine Data, Historians
Central HDP Cluster
Hive
Central HDF Cluster
NiFi
Kafka
Storm
Streaming
Options
HBase Solr
YARN
HDFS
Location 1
NiFi
Location n
NiFi
Data Center
Data Ingestion Framework
End users
DATA IN MOTION – H DF DATA AT R EST – H DP
HDF Edge (MiNiFi + NiFi)
 Reliable collection
 Small footprint
 Edge processing
 Data provenance
 Integrates with core
policies
HDF Core (NiFi with Streaming)
 Processing at larger scale
 Distributed stream processing
HDP
 Security and data governance
 Monitoring, management, operations
 Applications
 Analytics
Structured / Unstructured Data Sets

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Hortonworks Connected Data Platforms in Energy & Utilities
Source: https://www.cm-collaborative-tech.com/wp-content/uploads/2016/11/Smart-grid-A-1.jpg
Predictive MaintenanceFraud DetectionExternal Sources
(Weather, Social
Media, GPS, etc.)
Single View of Customer

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Time Series Analytics for Power Generation Anomaly Detection
 Two week engagement – no direct knowledge of existing systems
 Two days were able to isolate problem down from 5000 potential
causes to 19 using standard data science algorithms
 Company investigated findings and found a valve was installed
backwards causing plant to shutdown
 Plant failure hasn’t occurred since, saving millions of dollars in
unplanned shutdowns
 VP of Engineering – “I never thought we would see a solution like
this”

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Vertically Integrated Utility’s Data Journey
Accelerating Revenue Protection with an Open Analytics Platform
 One of the largest electric power holding companies in the US that supplies electricity to approximately 7.4 million
customers and operates natural gas distribution services serving more than 1.5 million customers.
 Revenue Protection Use Case: Protect revenue from theft, malfunctioning meters, and misconfigured meters.
 Why HDP: The only cost effective platform able to do parallel / multi-node analytics on large data sets.
 Currently have loaded 200 Billion rows of meter data across 80 nodes of HDP growing to 1.4 Trillion by 2020 from all of
their service areas.
 Previous energy theft data science process: Predictive model was run on a laptop 1x per week for 10K accounts at a time
and produced 100 leads weekly for investigation. At that rate, it would have taken them 6 months to process one state’s
data (all states/enterprise data would take much longer)
 Current process: Leveraging HDP the run time to analyze one state’s data has been reduced from 6 months to less than
an hour, producing theft leads from the entire data set in minutes.
 Expected realized business value from the Revenue Protection use-case to be tens of millions of dollars by 2020.
 Other use-case include predictive equipment maintenance on a time-series data ingested from OSIsoft Pi and a “Next
Best Action” program for cross-selling opportunities on goods and services.

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Utility Big Data Journey – Crawl, Walk, Run
Arrears & Credit
Collections*
•Better identify customers
prior to going into
Arrears to start payment
plans
Revenue Protection*
•More quickly identify theft,
malfunctioning meters and
misconfigured meters across
entire customer base with HDP
•Estimated business value –
millions of dollars in previously
unrecognized revenue
360 Degree View of
Customer*
•Aggregate customer data
across enterprise: usage,
billing, profile info,
surveys, call center logs,
order history, social media
sentiment, etc.
•Develop customer
segmentation models &
KPI’s to improve customer
service, reduce call center
volumes/times, feed next
best action programs, etc.
Predictive
Maintenance*
•Ingest time-series data
from control systems
and previous
maintenance records to
identify patterns in
malfunctioning
equipment
•Shift from time-based
maintenance to
condition-based to
prioritize and optimize
maintenance resources
and operations.
Outage Detection
& Prevention*
•Identify outages in
real-time, notify
customers of outages
and reduce time to
resolution
•Better forecasting
models = lower service
costs, reduced truck
rolls, increased
revenue, and higher
customer satisfaction
• Start Small, Think Big
• Improve Top-line and bottom-line revenue
• Develop In-house talent
*AMI data is the foundation for both T&D and customer-focused use-cases.

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Manufacturing Data Lake for Global Operations
Capabilities
• Capture new and breakdown existing
operational data silos
• Democratize data access to a wider audience
• Flexible architecture to incorporate the latest
Apache open source/3rd party/customer
innovations
• Foster community
“Not an ops historian but a enterprise
historian of ALL PROCESS DATA”
Design
• Embedded analytics and visualizations
• Embedded open source graphical data ingest
• Proven at scale – 1M tags / minute
• Comply with existing security, governance and
operations
• Built for extension

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Develop: Using HDP and HDF for Industrial IOT
Hortonworks Customer
• Schlumberger Drilling Technology
Application Area
• Real Time Drilling Data Delivery - WITSML
A Few Requirements
• Provenance – knowing where the data came
from is crucial (and often missing) to real time
decision making especially when dealing with
3,000 wells per month
• Visualization – the ability to visualize the data
flow at a granular level aids in troubleshooting
and operational understanding
• Reduced overhead leveraging NiFi vs. previously
built custom-coded solution
http://www.slideshare.net/HadoopSummit/from-zero-to-data-flow-in-hours-with-apache-nifi-64032731

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Real-time Remote Surveillance
Requirement – A New Business Model:
• Fluid and flexible data platforms that can quickly integrate raw data
and deliver actionable intelligence to people and processes
• Ability to operate when network connectivity with a data center or
the shore is intermittent, latent and provide minimal bandwidth
• Analysis of large volumes of data and avoid data being stranded and
out of reach for analysts and support teams.
• Move from an operations posture of reacting and suffering from
unnecessary downtime, equipment failures, efficiency losses, and
safety risks
• Increases the collective expertise available to support safer and
more efficient operations
Solution and Outcomes – New Sources of Value:
• HDF aggregates, prioritizes, compresses and encrypts control system
data before sending it over a 64 KB/sec satellite link to the data
center in real-time
• Data from top drives, BOPs and other equipment is in HDP and every
data consumer from data scientist to BI users can be serviced from
their tool of choice
• Key data consumption patterns enabled include KPI dashboards,
condition-based monitoring and maintenance, event-based
surveillance, and traditional BI reporting; ensuring safer more
efficient operations

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Open source is a way to enable a group of
collaborative people to further their
individual interests while contributing back to
the community for the common good.
Open source