Shankar Sengupta – Executive Director, Industrial Data Lake

5 GESoftware.com | @GESoftware | #IndustrialInternet

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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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

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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 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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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.