GE’s Industrial Data Lake Platform

Optimized for the Industrial Internet:
GE’s Industrial Data Lake Platform

2 GESoftware.com | @GESoftware | #IndustrialInternet
Agenda
Opportunity Solution Result
GE Data Lake
Challenges

Big opportunities
with Industrial Big Data
The power of 1%
Increasing
freight utilization rail
Predictive
maintenance healthcare
Predictive
diagnostics power
Driving outcomes that matter
$27B
Industry value by
reducing system
inefficiency
$66B
Industry value
with efficiency
improvements in
gas-fired power
plant fleets
$63B
Industry value by
reducing process
inefficiency
Note: Illustrative examples based on potential one percent savings applied across specific global industry sectors over 15 years. Source: GE estimates

Industrial Big Data – fast and vast
50BMachines will be
connected on the
internet by 2020
2XIndustrial data
growth within
next 10 years
*Sources: IDC, Ericsson, Wikibon, Fast Company,
ComputerWeekly
CRM, ERP,
etc. Logs
Social
network
data
Geo-location
data
In practice only
3%of potentially useful
data is tagged
and even less
is analyzed*
9MM
Data points
per hour for each
locomotive
500GB
Data per blade
by gas
turbines
Sensor
data
Content
(images, videos,
manuals, etc.)
Historian
data
Machine
data
35GB
Data per day
from each
Smart Meter
50X
Data growth
in healthcare
(2012 – 2020)
1TB
Data per
flight

Case study – GE Aviation
Asset productivity,minimize disruptions,improvedforecasting
25 Airlines
3.4M Flights
340TB Data
10X Cost reduction
7 days Time-to-market for
new analytic app
2000X Performance
improvement
ü Isolate root causes
ü Identify sub-optimal
performance parts
ü Minimize disruptions
Note:Illustrative Aviation example based on Predix solution currently in development.Estimates based on data exploration,simulation and assetutilization models.

Use case
Quality – Predict the quality of the manufactured
part using predictive analytics
Machine health – Collect data from machine
tools, that enable predictive maintenance
Energy Consumption – Save and optimize
energy costs. Predict future costs. Use historical
data to identify energy consumption trends, and
how external conditions impact energy
consumption
Process Optimization – Process, effects, and
environmental variables for monitoring analytics
by operation/machine/area, increasing machine
uptime.
Business Value
• Reduction in defects
• Eliminate unplanned downtime
• Reduce energy costs
• Increase throughput
GE Manufacturing – Brilliant Factories

GE Oil & Gas – Intelligent Pipeline Solution (IPS)
Use case
Analyze real-time and historical pipeline
operational and integrity data:
1) Equipment/asset registry
2) Dashboard visualization of equipment and
pipeline networks (geospatial), raw data
types and key performance indicators
(operational/investigational)
3) Analysis of variability in throughput
(pressure). Requires support for multi-user
capability, and future accessibility by
mobile devices
Data sources: OSI PSI, GIS, Dig Tickets,
Risk, Navigate, Maximo, NOAA.

80% of an analytics project typically involves gathering
and then preparing the data for analysis*
Today’s approaches are not prepared
for onslaught of Industrial Big Data
*Source: IDC
Too
slow
Too
rigid
Too
expensive

All over the place
Data across multiple
locations
Snapshot
Limited to narrow
snapshots and time
Limited data types
Mostly structured and
semi-structured data types
Logs Social network
data
Geo-location
data
CRM, ERP,
etc.
Yesterday’s data warehouse
architecture
TRADITIONAL DATA WAREHOUSE
What is it
telling me?
How does
it look?
How is it
doing?
Data scientist Field operations Business analyst
ONE STATIC DATA MODEL
1
2
3

All data
Access to real-time data
and historical data and not
limited to snapshot of data
Any data
Handing of all data types
including documents, images
machine data, sensor data
One place
Access to all data in one
place to quickly respond to
the speed of business change
1
2
3
Rapid access to all data for analytics
How long will
it last without
failures or
maintenance?
Is my asset
ready when
there is market
opportunity?
Is my asset
performing
optimally?
How to configure
for best
operational
results?
FLEXIBLE DATA MODELS
Industrial Data Lake architecture
Underpinned by data governance appropriate to Business and Location
INDUSTRIAL DATA LAKE
Data scientist Field operations Business analyst
Sensor
data
Content
(images, videos,
manuals, etc.)
Machine
data
Historian
data
CRM,
ERP,
etc.
Logs,
click
streams
Geo-
location
data
Social
network
data

Data
governance
Analytics and
operations
Data
collection
Data
ingestion
New wayCurrent situation
Data
loading
Add
semantic
metadata
Replica
of source
data
A day in the life – data management
Agility
Data
scientist
Rigid
Field
operations
Business
analyst
INDUSTRIAL
DATA LAKE
Agile
Data
scientist
Field
operations
Business
analyst
Cost
Data
collection
Data
ingestion
Data
governance
Analytics and
operations
Cost
CRM, ERP,
etc.
Logs Geo-location
data
Social network
data
INDUSTRIAL
DATA
LAKE
Real-time
ingestion
Replica
of source
data
Add
semantic
metadata
Data
collection
Data
ingestion
Data
governance
Analytics and
operations
Time
Time to analyze
Data
scientist
Field
operations
Business
analyst
Data
scientist
Field
operations
Business
analyst
CRM,
ERP,
etc.
Logs,
click
streams
Geo-
location
data
Social
network
data
Sensor
data
Content
(images, videos,
manuals, etc.)
Machine
data
Historian
data

Data Lake Consumption Patterns
Integration &
visualization
Advanced
Analytics
Real-time
analytics
Use case patterns
Search + API
Structured Data
Batch / CDC
based replication
Simple
transformation w/
mastering
Reporting tools
Structured +
Unstructured
Batch / CDC
based replication
Machine
Learning,
Predictive
Modeling
Reporting tools &
self discovery
tools
Structured +
Unstructured
Batch / CDC
based replication
Machine
Learning,
Predictive
Modeling
Search &API
access to
visualization
Structured +
Unstructured
Real time
ingestion
Simple real time
processing
API based real
time access
1
2
3
4

Industrial Data Lake
Optimized for industrial workloads
Optimized
for mission-
critical
workloads
for addressing key
SLAs such as
Security, resiliency
etc. for Industrial
Internet applications
Fast ingestion,
storage and
compute
including
machine data to
support multiple
schema and
data types
High-
performance
analysis
using massively
parallel processing
architecture
supporting Apache
Hadoop
Data
governance
and federation,
with geographically-
dispersed deployment
options

Big Data without Governance
ü Dumping data into Big Data lake without repeatable processes
and data governance will create messy, uncontrollable data
environment
ü Insights harvested from ungoverned data lake, is not reliable
and trustworthy
ü If the insights can not be fully trusted, it’s difficult to
make business decisions confidently.
Solutions for Industrial
Internet, deep domain
expertise

GE as a Custodian of Customer Owned
Data & Services
Custodian Roles
Enforcement &
Measurement
Infrastructure
Protection
Privacy
Data
Management
a person who has responsibility
for or looks after something
Custodian
Synonyms: keeper, guardian, steward,
protector
"the custodian of the relic"
Access Controls – Visibility–
Metrics…
Customer Owned Data

Governance Disciplines
Metadata
Data Dictionary
Directory of all assets
Classification and Tagging
Lifecycle
Provenance
Lineage
Retention
Quality
Accuracy
Completeness
Consistency
Auditing
Monitoring
Logging
Log Analysis
Compliance
Regulatory
Corporate
Protect, Manage and Improve Information

• Outcomes
• Data
• Parties
Full Governance Enforcement
Data Lake
Onboarding
Tool
Deployment
API
Process
Qualification
Fit Gap
Analysis
Design &
build
Quarantine OnBoard
Platform
Engineering
Certified
Patterns
DevOps
Data
Vault
• Compliance
• Contractual
• Security
• Risk
• Design
• Testing
• DevOps

Capability Description
Metadata Catalog
A repository of metadata describing data elements, lineage and
relationships of the data within the lake
Business Glossary
Glossary of business terms, definitions and related properties built on
top of the technical metadata catalog
Policy Store
Centralized store of access & usage policies and rights by data object
Policy Enforcement
Engine to provide security enforcement as a service – abstracted
from consumption method
Collaboration &
Governance
Portal providing data stewards, providers, custodians and consumers
with collaboration, issue resolution, enhancement request and change
management capabilities
Data Governance – Key Technology Capabilities

Security Risk for Big Data
ü More data implies higher risk of exposure
ü New data types may give rise to new security breach scenarios
ü Evolving and experimental analysis implies security policies
are less likely to be in place
ü Linkage to other data already under compliance may create
scenarios where compliance could be violated.

Top Opportunity Areas for Security
Perimeter:
Infrastructure
Communication
protocols
Key management
Protection:
Encryption
Access policy
based encryption
Searching /
filtering
encrypted data
Secure
outsourcing of
computation
Access Control:
Privacy
Secure
dissemination
Secure data
collection
/ aggregation
Secure
collaboration
Visibility: Data
Management
Data
integrity/Provena
nce
Proof of data
storage

Platform Validation via Information Security Policy
Framework (ISPF)
Main Design Characteristics
• Full ISO 27001/2 Certification
• End-to-end Infrastructure Visibility
• Extend Compliance for GE and its Customers
• Unified Monitoring Framework
• Central Controls – Separate Monitors
• Complete Suite – LayeredApproach
• ISMS (Information Security Mgmt System)
• Common Control Set
• ISMS Management
• Governance & Ops
One Framework – Multiple Certifications

Thank you
General Electric reserves the right to make changes in specifications and features, or discontinue the product or service
described at any time, without notice or obligation. These materials do not constitute a representation, warranty or
documentation regarding the product or service featured. Illustrations are provided for informational purposes, and
your configuration may differ.
This information does not constitute legal, financial, coding, or regulatory advice in connection with your use of the
product or service. Please consult your professional advisors for any such advice.
GE, the GE Monogram, Predix, Predictivity are trademarks of General Electric Company.
©2014 General Electric Company – All rights reserved.

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