the industrial internet, data-as-a-service - shankar sengupta, ge corporate, it executive - ivsz...
TRANSCRIPT
Case study – GE Aviation Asset productivity, minimize disruptions, improved forecasting
25 Airlines
3.4M Flights
340TB Data
10X Cost reduction
7 days Time-to-market for new analytic app
2000X Performance improvement
Isolate root causes
Identify sub-optimal performance parts
Minimize disruptions
Note: Illustrative Aviation example based on Predix solution currently in development. Estimates based on data exploration, simulation and asset utilization models.
Industrial Big Data – fast and vast
50B Machines will be connected on the internet by 2020
2X Industrial 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
12 GESoftware.com | @GESoftware | #IndustrialInternet
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
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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
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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 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
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