Big Data Analytics & Electronics System Design

Transcription

1 India Chapter, Bangalore Big Data Analytics & Electronics System Design Ankan Mitra Vice-President SMTA India Chapter

2 Hello Bangalore, How do you do? SMTA India Chapter ಹಲ ಬ ಗಳ ರ ನ ವ ಹ ಗ ದ ದ ರ? హల బ గళ ర ఎల ఉన న వ? ह ल bengaluru कस आह त? હ લ લ બ ગલ ર તમ ક વ ર ત છ? ஹல ப ங கள ர ந ங கள எப ட இர க க ற ர கள? ہیلو بنگلور تم کیسے ہو হ য লল ব ঙ গ ল র আপন ব ম আল? ह ल ब गल र आप क स ह?

3 SMTA Kick-Off Meeting Create a technology platform Ensure SME growth to enhance Indian Supplier base Bring in Advanced Electronics Manufacturing best practices

4 SMTA India Chapter Page

5 Today s Discussion Big Data Analytics & Electronics System Design

6 Electronics Industry Characteristics Through eyes of the CIO Process Data Quality Data Supply Chain Data Open Source Data Operational Data Market Data R&D Data Design Data Customer Data University Laisoning Data Core Organizational Data IP Data Design Data Business Model Data Competitiveness Depends on how fast organizations interprets the Data to Information

7 Electronics Industry Characteristics Through eyes of the CIO Process Data Quality Data Supply Chain Data Open Source Data Operational Data Market Data R&D Data Design Data Customer Data University Laisoning Data Core Organizational Data IP Data Design Data Business Model Data Organizations are more and more Data Driven!

8 Everyone is talking about Organizations Universities Government Policies Weather Forecasting Big Data!!! Business Engagements Supercomputers Sequoia & Blue Waters Economists Corporations Advertisements Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

9 What is it? Big Data!!!

10 Key Characteristics Volume Veracity Big Data! Variety Velocity Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

11 Key Characteristics Veracity Volume Big Data! 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 Variety possibilities as Internet of Things is catching up Velocity Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

12 Key Characteristics Volume Veracity Big Data! Variety Forms of Data 8 bn+ types of data accessed daily by organizations daily Velocity 20 bn Institutional content sharing: Internal Social Media Platforms Unified Collaboration Solutions increasing variety! Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

13 Key Characteristics Volume 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 Big Data! Velocity Variety Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

14 Key Characteristics 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 Veracity Volume Big Data! Variety Velocity Reference: McKinsey Global Institute, Twitter, Cisco, Gartner, EMC, SAS, IBM, MEPTEC, GAS

15 Areas of Interest Electronics Industry Professionals Design Manufacturing & Quality Supply Chain Management Physical Application Details Component Data and Possible Issues Lead-time and Price Industry Intelligence Latest Industry Innovations Expected Quality Performance Sustenance Considerations Potential near-miss Challenges

16 Converging Relation Contextual Data Mining Electronics Industry & Big Data!

17 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

18 Data can be acquired in: Structured Un-structured Intelligent DAQ Nodes 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: Applications in 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.

19 Integration with Cloud Storage Intelligent DAQ 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

20 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 endof-life cycles

21 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.

22 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

23 Electronics Design Automation & Big Data Multiprotocol Support High Performance Availability Product Data Management Multi file format and OS support in both Licensed & Open OS Powerful storage helps to hold large simulation and testing file Recovery of deleted or corrupted data & online expansion of space Scalability and ease of retrieving PDM files Reference: Netapp

24 Potential Challenges Storage and Transportation Distributed Data and Distributed Processing Data Management Compliance and Security Data Processing Data Ownership Reference: th Hawaii International Conference on System Sciences

25 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

26 Wrap-up

27 THANK YOU