big data analytics & electronics system design data analytics & electronics system design ankan...

Big Data Analytics & Electronics System Design

Ankan Mitra Vice-President

SMTA India Chapter

India Chapter, Bangalore

SMTA India Chapter Hello Bangalore, How do you do?

bengaluru ?

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SMTA Kick-Off Meeting

Create a technology platform

Bring in Advanced Electronics Manufacturing best practices

Ensure SME growth to enhance Indian Supplier base

SMTA India Chapter Page http://smta.org/chapters/chapters_detail.cfm?chapter_id=123

http://smta.org/chapters/chapters_detail.cfm?chapter_id=123

Todays Discussion

Big Data Analytics & Electronics System Design

Electronics Industry Characteristics Through eyes of the CIO

Open Source Data

Operational Data

R&D Data

Core Organizational

Data IP Data

Design Data Business Model Data

Process Data Quality Data

Supply Chain Data

Design Data Customer Data

University Laisoning Data

Market Data

Competitiveness Depends on how fast organizations interprets the Data to Information

Electronics Industry Characteristics Through eyes of the CIO

Open Source Data

Operational Data

R&D Data

Core Organizational

Data IP Data

Design Data Business Model Data

Process Data Quality Data

Supply Chain Data

Design Data Customer Data

University Laisoning Data

Market Data

Organizations are more and more Data Driven!

Everyone is talking about

Big Data!!!

Organizations Universities

Advertisements

Business Engagements

Government Policies

Economists Corporations

Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

Weather Forecasting

Supercomputers Sequoia & Blue

Waters

What is it?

Big Data!!!

Key Characteristics

Big Data!

Volume

Variety

Velocity

Veracity


Key Characteristics

Big Data!

Volume

Variety

Velocity

Veracity

Generation of Data (SCALE!) 43 Tr. Gb Data created by 2020 Billions of equipments deployed Estimated Data Generated per Day = 2.5 Quintillion Bytes (2.3 Tr. Gb) from IT Hardware only Existing Data = 100,000 Gb (most US companies) Imagine possibilities as Internet of Things is catching up


Key Characteristics

Big Data!

Volume

Variety

Velocity

Veracity

Forms of Data 8 bn+ types of data accessed daily by organizations daily 20 bn Institutional content sharing: Internal Social Media Platforms Unified Collaboration Solutions increasing variety!


Key Characteristics

Big Data!

Volume

Variety

Velocity

Veracity Analysis of Streaming Data

15 TB of Data Analysis Engineering Support Centers (Cumulative) per session Sensors in Satellite, Ships, Aircraft, Public Transport Streaming Decision Support created in Ecosystem


Key Characteristics

Big Data!

Volume

Variety

Velocity

Veracity

Uncertainty of Data Accuracy of Data being considered Spike in Data Volume / Criticality of Information Quality of Data Standard Data Harmonization being driven specific for Industries


Areas of Interest Electronics Industry Professionals

Design

Physical Application

Details

Latest Industry Innovations

Manufacturing & Quality

Component Data and

Possible Issues

Expected Quality

Performance

Supply Chain

Lead-time and Price

Sustenance Considerations

Management

Industry Intelligence

Potential

near-miss Challenges

Converging Relation

Contextual Data

Mining

Electronics Industry

& Big Data!

Contextual Data Mining Data measured can provide: Sensor type Manufacturer Calibration date for a given measurement channel Revision Designer details Model number for an overall component under test

In fact, the more context that is stored with raw data, the more effectively that data can be traced throughout the design life cycle, searched for or located, and correlated with other measurements in the future by dedicated data post-processing software.

Reference: Electronic Design Magazine

Intelligent DAQ Nodes Data can be acquired in:

Structured

Un-structured

Streaming

Data acquisition applications are incredibly and increasingly diverse.

Engineers and scientists invest critical resources into building advanced acquisition systems, but the raw data produced by those systems is not the end game.

Instead, raw data is collected so it can be used as an input to analysis or processing algorithms that lead to the actual results system designers seek.

Data can be highly dynamic: Automotive crash tests Gigabytes of data in a few tenths of a second (speeds, temperatures, forces of impact, and acceleration).

Data with very slow acquisition rate with periodic bursts: Applicationsin the environmental, structural, or machine condition monitoring spaces Intelligent DAQ nodes keeps acquisition speeds low and minimizes logged data while allowing sampling rates that are adequate enough for high-speed waveforms when necessary in these applications.

Integration with Cloud Storage

The unification of DAQ hardware & onboard intelligence Systems increasingly embedded for remote monitoring. Internet of Things is finally unfolding before our very eyes as the

physical world is embedded with intelligence and humans now can collect data sets about virtually any environment around them.

The ability to process and analyse these new data sets about the physical world will have profound effects across a massive array of industries

Intelligent DAQ

Cloud Storage

Intelligent Algorithms

Organizations are faced with the challenge: What Information should we keep?

With reduced storage costs this is certainly not a barrier to entry, but this has been cited as the number one challenge.

Many businesses to the save everything conclusion for fear that latent information not previously known.

With the paradigm more data beats better algorithms Algorithms are becoming increasingly intelligent to process:

Existing data On how to handle transfer of data for eventual hardware end-

of-life cycles

Integrated Multi-platform Storage

Old concept Store information in multiple formats in equipments and acquire when there are possible issues

Online streaming of data from multiple platforms specially in electronics industry is an upcoming area

Inviting collaboration among formerly walled-off functional units, and even seeking information from external suppliers and customers to cocreate products.

In advanced-manufacturing, suppliers from around the world make thousands of components.

More integrated data platforms now allow companies and their supply chain partners to collaborate during the design phase -- a crucial determinant of final manufacturing costs.

Long relation with Semiconductor Industry

Why Fab-houses are not existing targets?

Industry already had very mature data collection practices

Fab data is well structured, no need for analytics

High data growth rate which has been foreseen and planned

Future Plateau and possible growth

Increasing design complexity has resulted growth in electrical test data

Technologies below 40nm require advanced process control and data collection

Growing interest to analyze additional sensor data to move from preventive to predictive techniques

Reference: IBM

Electronics Design Automation & Big Data

Multiprotocol Support

Multi file format and OS support in both Licensed & Open OS

High Performance

Powerful storage helps to hold large simulation and testing file

Availability

Recovery of deleted or corrupted data & online expansion of space

Product Data Management

Scalability and ease of retrieving PDM files

Reference: Netapp

Potential Challenges Storage and

Transportation

Data Management

Data Processing

Data Ownership

Compliance and Security

Distributed Data and

Distributed Processing

Reference: 2013 46th Hawaii International Conference on System Sciences

Summary As product differentiation is becoming key any

related information even from digital dirt becomes crucial

Compliance requirements require enhanced data acquisition and quality during qualification and performance measurement

Intelligence from Market Data will drive Supply Chain Organizations

Big Data & Electronics Industry set-forth for enhanced relationship

Wrap-up

[email protected]

THANK YOU

[email protected]

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