This document outlines the agenda for a panel discussion on the amalgamation of IoT and big data. The 75-minute event will include opening remarks, 10-minute presentations from each of 5 panelists, and 3 rounds of 15-minute questions. Panelists will represent industries like healthcare, energy, and manufacturing. Key discussion themes are how machine learning can streamline IoT analytics, data governance challenges, and using intelligent automation in IoT systems. The closing will assess challenges in IoT data collection, maturity of ML techniques used, best practices for data governance, and use cases for sectors like smart cities and healthcare.

1. SCIT NATIONAL SEMINAR
PANEL DISCUSSION ON THE
AMALGAMATION OF IOT AND BIG DATA

2. AGENDA
TIME TABLE FOR THE PANEL DISCUSSION
▸ Overall Duration - 75 minutes
▸ Context Setting by Moderator - 5 minutes
▸ Point of View by Each Panelist - 10 minutes
▸ First Question to each panelist - 15 minutes
▸ Second Question to each panelist - 15 minutes
▸ Third Question to each panelist - 15 minutes
▸ Closing Remark by Each Panelist - 10 minutes
▸ Conclusion by Moderator - 5 minutes

3. AMALGAMATION OF IOT AND BIG DATA
KEY THEMES FOR THE PANEL DISCUSSION
▸ How can machine learning streamline Big Data Analytics
for IoT systems and solutions
▸ What are the Data Governance Challenges in IoT Systems
▸ How can Intelligent Automation empower IoT Systems and
Solutions
▸ What are the best practices for Big Data Repository Design
in IoT Systems and Solutions

4. DR. SHEELA SIDDAPPA, CHIEF DATA SCIENTIST AT BOSH
QUESTIONS TO DR.SHEELA SIDDAPPA
▸ Questions based on the expertise of panelist in Industry
4.0, Smart City, Manufacturing, Healthcare, Energy
▸ How do you analyse the role of an integrated IoT Big Data
Analytics in a Smart City ecosystem in the Indian Context
▸ What are the best practices to ensure data security for an
IoT Big Data Analytics platform implementation in areas
like Nuclear Energy, Healthcare etc.
▸ What are the recommended Data Governance Strategies
for end to end IoT Datasets
▸ What would be the impact of GDPR on the IoT Big Data
Analytics implementations across Healthcare sector.
▸ What are the prominent IoT data aggregation and data
management techniques used in Manufacturing sector.
▸ What are the best practices to ensure scalability,
heterogeneity, agility and flexibility in Industrial IoT
platforms.

5. DR.PRADEEP BILURKAR, VP, MACHINE LEARNING, BARCLAYS
QUESTIONS TO DR PRADEEP BILURKAR
▸ Questions based on the expertise of panelist in ML and Power and
Energy Sector
▸ How do you evaluate the maturity of different analytics capabilities
such as real time analytics, interactive analytics, predictive analytics
and batch analytics for IoT systems
▸ What are the most promising Machine Learning use cases and
reference implementations in Power and Energy sector across the
globe
▸ Can machine learning and IoT be used to build a greener future for
energy sector
▸ How can machine learning help in time series analysis of IoT
datasets ?
▸ What are the machine learning techniques popularly used for
analysing streaming data from IoT data sources
▸ How do you see the promise of embedded analytics in Industrial IoT
landscape
▸ It is said that IoT is not delivering full potential because of data
challenges. What are the important measures to address this concern.

6. SOURABH MUKHARJEE, VP, DATA MANAGEMENT, ACCENTURE TECHNOLOGY
QUESTIONS TO MR. SAURABH MUKHARJEE
▸ Questions based on the expertise in Big Data,
Information Management , Data Strategy
▸ What are the challenges in leveraging MDM platforms for
IoT Big Data landscape
▸ What are the innovations in MDM and Big Data
Architecture that can help to manage IoT Data.
▸ How can we leverage Big Data Analytics for Real Time IoT
Data Streaming Systems
▸ From a comprehensive data management perspective,
how different are Big Data, Streaming Data, Fast Data,
Event Data, Device Data, Sensor Data etc.
▸ What are the best practices for seamless integration
between Big Data Bridges and IoT Sensor Gateways
▸ What are the important factors in designing big data
repositories for sensor data sets.

7. KAPIL GANDHI, HEAD OF ARTIFICIAL INTELLIGENCE, EY
QUESTIONS TO MR.KAPIL GANDHI
▸ What are the unique and differentiating characteristics of
IoT based data sets from a machine learning
perspectives.
▸ How much of Robotic Process Automation is possible in
the context of IoT Systems and Solutions
▸ What is the current state of progress in sensor data
analytics using artificial intelligence ? What are the
recommended implementation best practices ?
▸ How are we integrating machine learning based
algorithms with big data systems in the context of IoT
based data sets.
▸ It is said that IoT is not delivering full potential because of
data challenges. What are the important measures to
address this concern.
▸ What are the emerging business models around IoT Big
Data Analytics landscape?

8. CLOSING REMARKS
EXPECTED OUTCOMES FROM THE PANEL DISCUSSION
▸ An assessment of the challenges in Data Collection
techniques used in IoT Big Data Analytics landscape
▸ An assessment of the maturity of Machine Learning
Techniques used in IoT Big Data Analytics landscape
▸ A compilation of Data Governance best practices in IoT Big
Data Analytics landscape
▸ A summary of Smart City, Healthcare, Energy, Manufacturing
use cases related to IoT Big Data Analytics offerings