Enterprise Data & AI Strategies with Awinash Sinha, CIO at Zoom and Bask Iyer, Former CIO at VMware, Dell, Juniper Networks, and Honeywell & Advisor at Zoom
09/05/23 • 51 min
This episode of The Analytics Edge, sponsored by NetSpring, features an interview with Awinash Sinha, CIO at Zoom, and Bask Iyer, former CIO at VMware, Dell, Juniper Networks, and Honeywell & Advisor at Zoom, an all-in-one intelligent collaboration platform that makes connecting easier, more immersive, and more dynamic for businesses and individuals.
Responsible for the company’s information technology, data science and analytics, and business application organizations, Awinash brings to Zoom over 20 years of experience in enabling business outcomes and scaling fast-growing companies. He has completed an executive program in Business Administration and Management from Stanford University Graduate School of Business.
Having served as an advisor and mentor to Zoom since 2016, Bask is a renowned technology executive, leading enterprise-wide transformations in digital systems at numerous companies in Silicon Valley. He is the CEO of BaskMind.com, an experienced team offering hands-on digital transformation and operations excellence services to traditional companies and advisory services to high growth technology companies. Iyer holds a Master’s degree in Computer Science from Florida Institute of Technology.
In this exclusive episode, Awinash and Bask dive deep into the impact of AI at Zoom, and provide advice for data leaders on generative AI, how to enhance the customer experience in product-led companies, and the importance of hiring “outcome enablers."
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Key Quotes
"In subscription business you have to really closely follow the life cycle of customers. And it's a infinity loop, right? Even existing customers, you may be looking at feature adoptions, you may be looking at churn, you may be looking at how we can create more value, make them aware about new products and offerings coming together. And internally, for the sales department, marketing department, or customer support department, providing the insight, both coming from product. Basically with this cloud architecture we have an ability to look at product telemetry data. As well as business transaction data and intersecting them. The magic is, the real insights are when we intersect these two data, join these two data and do a cohort analysis. Cohort analysis at product level, at segment level, particularly for large enterprise companies our size and bigger, will have a customer segmentation, will also will have some flavor, some in geodes dimensions or other dimensions. Once we connect the data around product and then customer lifecycle from business transactions, it could be slice and dice from many different perspective, and that's where the insights come." - Awinash Sinha
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Episode Timestamps
(01:39) Approaches for CIO’s and Data leaders to consider regarding AR and VR
(03:31) Zoom’s strategy for AR and VR collaboration
(06:29) AI-powered innovations improving user experience on Zoom
(07:49) Zoom IQ explained
(11:25) AI's role in addressing COVID-driven challenges for engagement in online education and business interaction
(16:59) Advice to C-level executives on generative AI strategy
(22:05) Approach of starting initiatives despite data quality concerns
(27:29) Future of the "data scientist" title amid AI techniques and evolving roles
(30:54) Warehouse-centric data approach and its impact on data leaders
(31:57) Advantages of data warehousing being on cloud
(37:03) Strategies for product-led analytics leaders to boost customer experience
(39:52) Value of information and time on data
(42:38) Consumer-centric approach and its impact on business
(45:05) Call to action for data leaders
(51:58) Takeaways
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Links
This episode of The Analytics Edge, sponsored by NetSpring, features an interview with Awinash Sinha, CIO at Zoom, and Bask Iyer, former CIO at VMware, Dell, Juniper Networks, and Honeywell & Advisor at Zoom, an all-in-one intelligent collaboration platform that makes connecting easier, more immersive, and more dynamic for businesses and individuals.
Responsible for the company’s information technology, data science and analytics, and business application organizations, Awinash brings to Zoom over 20 years of experience in enabling business outcomes and scaling fast-growing companies. He has completed an executive program in Business Administration and Management from Stanford University Graduate School of Business.
Having served as an advisor and mentor to Zoom since 2016, Bask is a renowned technology executive, leading enterprise-wide transformations in digital systems at numerous companies in Silicon Valley. He is the CEO of BaskMind.com, an experienced team offering hands-on digital transformation and operations excellence services to traditional companies and advisory services to high growth technology companies. Iyer holds a Master’s degree in Computer Science from Florida Institute of Technology.
In this exclusive episode, Awinash and Bask dive deep into the impact of AI at Zoom, and provide advice for data leaders on generative AI, how to enhance the customer experience in product-led companies, and the importance of hiring “outcome enablers."
-----------
Key Quotes
"In subscription business you have to really closely follow the life cycle of customers. And it's a infinity loop, right? Even existing customers, you may be looking at feature adoptions, you may be looking at churn, you may be looking at how we can create more value, make them aware about new products and offerings coming together. And internally, for the sales department, marketing department, or customer support department, providing the insight, both coming from product. Basically with this cloud architecture we have an ability to look at product telemetry data. As well as business transaction data and intersecting them. The magic is, the real insights are when we intersect these two data, join these two data and do a cohort analysis. Cohort analysis at product level, at segment level, particularly for large enterprise companies our size and bigger, will have a customer segmentation, will also will have some flavor, some in geodes dimensions or other dimensions. Once we connect the data around product and then customer lifecycle from business transactions, it could be slice and dice from many different perspective, and that's where the insights come." - Awinash Sinha
-----------
Episode Timestamps
(01:39) Approaches for CIO’s and Data leaders to consider regarding AR and VR
(03:31) Zoom’s strategy for AR and VR collaboration
(06:29) AI-powered innovations improving user experience on Zoom
(07:49) Zoom IQ explained
(11:25) AI's role in addressing COVID-driven challenges for engagement in online education and business interaction
(16:59) Advice to C-level executives on generative AI strategy
(22:05) Approach of starting initiatives despite data quality concerns
(27:29) Future of the "data scientist" title amid AI techniques and evolving roles
(30:54) Warehouse-centric data approach and its impact on data leaders
(31:57) Advantages of data warehousing being on cloud
(37:03) Strategies for product-led analytics leaders to boost customer experience
(39:52) Value of information and time on data
(42:38) Consumer-centric approach and its impact on business
(45:05) Call to action for data leaders
(51:58) Takeaways
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Links
Previous Episode
The Impact of Generative AI on Business with Amit Prakash, Co-Founder and CTO at ThoughtSpot
This episode features an interview with Amit Prakash, Co-Founder and CTO at ThoughtSpot, a market leading business intelligence platform that helps anyone explore, analyze, and share real time business analytics and data easily with AI powered analytics.
Amit has deep experience in building large-scale analytics systems. Prior to ThoughtSpot, Amit led multiple analytics engineering teams in the Google AdSense businesses, contributing $50M+ quarter-on-quarter growth to the business by improving analytical algorithms for AdSense. Previously, Amit was a founding engineer in the Bing team at Microsoft, where he implemented the pagerank algorithms for search from scratch. Amit received his PhD in Computer Engineering from the University of Texas at Austin and a Bachelor of Technology in Electrical Engineering from the Indian Institute of Technology, Kanpur.
In this episode, Amit talks about his journey in helping create ThoughtSpot, how data leaders should specifically be thinking about large language models, and the impact generative AI will have on business.
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Key Quotes
“The trick is to capture the mental model of the end user so that you know what they're already anticipating. If they're already anticipating an increase during Christmas season, you can see that from the previous trend. If they're already anticipating the amount of revenue they produce from a particular state to be proportional to the number of stores in that state, can you capture that and then that way you can de-noise these insights and meaningful insights surface a lot more. And that requires a two-way conversation between the algorithm and the end user. And these are the kinds of things that you can do with LLM that wasn't possible before.” - Amit Prakash
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Episode Timestamps
(02:29) How data leaders should be thinking about LLMs and generative AI
(06:46) Amit’s career journey and helping create ThoughtSpot
(15:06) Considerations around building a trustworthy AI system
(18:39) Unpacking large language models
(29:57) Institutional knowledge for good data reasoning and intelligence
(34:45) Modern data stack and data warehousing
(37:25) Moving towards generative AI and the impact it will have on business
(41:35) ThoughtSpot Generative AI Meetup
(43:57) Hosts’ after-thoughts
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Links
Next Episode
Key Trends in Databases with Nikita Shamgunov, Founder and CEO at Neon
This episode features an interview with Nikita Shamgunov, legendary founder of MemSQL (now SingleStore). His latest endeavor, Neon, offers serverless Postgres as a fully managed multi-cloud database that separates storage and compute, with auto scaling, branching, and bottomless storage.
Nikita is also a Partner at Khosla Ventures, where he is incubating Neon and raised $104M to date. He is passionate about deep tech, data infrastructure, and system software. Prior to Neon, Nikita co-founded MemSQL (now SingleStore), a unicorn data and analytics company valued at over $1.3 billion. He served as a founding CTO, and then CEO, successfully scaling the company to over $40 million in ARR. Prior to SingleStore, he worked as a senior engineer at Facebook and at Microsoft on SQL Server. Nikita earned a Ph.D. in computer science from the National Research University in St. Petersburg, Russia.
In this episode, Nikita recounts the founding stories behind both MemSQL and Neon, and elaborates on the key trends driving database technologies today, from serverless and generative AI, to open data and the convergence of transactional and analytical workloads.
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Key Quotes
Amplitude and Mixpanel, they basically are a time series database underneath with the UI. Time series data tends to be, you know, ‘write once’, most of it. And so, you need to take advantage of those techniques that data warehouses are basically born with, right? They are in the business of storing data relatively cheaply. And every enterprise, unless it's not like an archaic enterprise, should have a data warehouse. So it makes only too much sense to put this into a data warehouse rather than either a custom database, you know, like a platform like Datadog, Mixpanel, Amplitude. Plus you have additional benefits from it because you can cross reference that data with the rest of the business data." - Nikita Shamgunov
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Episode Timestamps
(01:41) Founding stories behind MemSQL and Neon
(03:39) Addressing new challenges for databases
(09:20) Criteria for evaluating databases
(12:36) HTAP and zero ETL between transactional and analytical applications
(19:07) Evolving standards around table formats
(24:07) Thoughts on Generative AI and LLM-native in the data warehouse
(26:38) Warehouse centric approaches to data storage
(29:45) Open source for data warehouses
(33:54) Potential for new applications to be built around real time applications
(38:10) Managing large volumes of data
(40:59) Serverless Postgres is as easy as Stripe
(45:40) Takeaways
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Links
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