In the Season 1 finale of The Analytics Edge (sponsored by NetSpring), we are delighted to have Sanjeevan Bala, Group Chief Data & AI Officer from the British media giant ITV, on the show to discuss the role and impact of Data and AI in the Media & Entertainment industry. Sanjeevan goes in-depth on how ITV's commitment to being data-driven has propelled the business forward. He shares ITV’s targets for doubling monthly active users (MAU) through marketing activities, doubling viewing hours within the product, and doubling digital revenue on the commercial front. In order to achieve these objectives, while we often talk about the data literacy of business teams, Sanjeevan exposes the need for business literacy on his data teams. This helps him determine the level of self-service vs. offering the “silver services” of an embedded team of analysts. Either approach presents their own set of last mile challenges, and he explains how a cloud data warehouse and having a single source of truth, can help align business teams around a shared view of customer segmentation and unified definitions of cross-functional business metrics.Join us as Sanjeevan shares the triumphs and challenges for Data and AI at ITV, and leaves us with his expert predictions on how AI will continue to disrupt the Media & Entertainment industry!Bio:Sanjeevan Bala is a leader in the field of data and artificial intelligence, currently serving as the Group Chief Data & AI Officer at ITV. Recognized as the Most Influential Person in Data on the DataIQ 100 list for 2023, Sanjeevan has over a decade of expertise in the Media & Entertainment industry, having contributed significantly at ITV and previously at Channel 4. With a bachelor’s degree in management & computing from King's College London, his influence extends beyond his current role, as he holds multiple board and advisory positions, including engagements with Bakkavor, Evanta, and DataIQ.Key Quote: “Across Product, Marketing, and Commercial you sometimes get very verticalized KPIs. For example, Marketing often will look at cost per acquired user. Historically, the conversation would be we’ve acquired them – they don’t go and watch something, that’s not our issue! But increasingly what’s happening is Marketing will look at cost per acquired hour of a viewer. We start to join up parts of the organization with these unified metrics, driving the right kind of behavior thinking about the next step in that journey.”- Sanjeevan BalaEpisode Timestamps  (Segment 1) (:56) Challenges(1:29) Career journey in data leadership(2:56) Integrated publisher/broadcaster model(4:20) ITV’s unique data requirements (9:38) ITV's modern data stack architecture(12:02) Multi-cloud strategy for different workloads(13:05) Challenges to making data-driven decisions(15:25) How data warehouses address last mile challenges(Segment 2) (17:07) Solutions(17:30) Organization of data team to support business units(20:44) Standard data models in Media & Entertainment (23:26) Behavioral analytics and segmentation(27:10) Data gaps in product analytics tools(31:42) Impact of the data team at ITV(Segment 3) (34:50) Business Opportunities (34:52) The future of AI at ITV and for Media & Entertainment(Segment 4) (39:53) TakeawaysLinksSanjeevan Bala’s LinkedInITV WebsiteThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website

On this episode of The Analytics Edge (sponsored by NetSpring), Asana's CIO, Saket Srivastava, delves into the profound impact of emerging technologies like self-service analytics and Generative AI on the future of work.Saket unveils Asana's research findings, revealing that a significant 50-55% of our time is allocated to operational tasks rather than yielding productive output. He articulates Asana's visionary approach towards enhancing work efficiency through AI integration.In addition, Saket explores imperative considerations for data leaders, emphasizing the importance of anticipating the consumerization of enterprise software and the requisite data strategies fueling product-led growth (PLG). Furthermore, he sheds light on the burgeoning challenge of shadow IT as business teams increasingly adopt self-service applications, alongside insights into the time-to-value dynamics of warehouse-native apps.Bio: Saket Srivastava is currently the CIO at Asana, boasting over 20 years of extensive expertise in data and analytics. With a deep proficiency in enterprise applications and infrastructure, Saket excels in forging executive partners to implement transformative solutions. His strategic focus encompasses driving efficiencies in ERP, CRM, HRIS, and IT Operations. Saket is a recognized thought leader, renowned for building and guiding large teams. His work expands internationally across the insurance, energy, healthcare, and banking industries. Key Quote:“Our product managers and our growth managers rely heavily on data to see how customers are using our platform. How frequently are they using? What capabilities are they using? Which capabilities are resonating more or less with them? There’s enough self-service that happens, but there are times they rely on data scientists to build models and experimentation. That informs our product roadmap.”- Saket SrivastavaEpisode Timestamps(Segment 1) (1:12)  Challenges(1:43) Saket’s career journey(2:45) Recommended tech investments(3:58) Leveraging data at Asana(6:40) Consumerization of enterprise software(9:33) Asana's vision for the Future of Work(14:35) Product analytics at Asana(16:48) Self-service & the challenges of shadow IT(22:30) Data warehouse strategy(Segment 2) (23:43)  Solutions(24:33) Warehouse-native apps(Segment 3) Business Opportunities(29:01) Generative AI at Asana(34:45) Priorities for the year ahead(Segment 4) (36:31) Takeaways

This episode of The Analytics Edge (sponsored by NetSpring), features Sanjay Agrawal, Co-Founder and CEO of Revefi. Revefi's data operations cloud offers a zero-touch data quality, spend, usage, and performance co-pilot for monitoring and optimizing cloud data warehouses. With Revefi, one customer reduced warehouse spend by 30% and their data team saw zero escalations from the business for data quality related issues, despite data adoption increasing 35%. Throughout the conversation Sanjay explores the continuing challenges in managing data quality, the emergence of zero-touch observability enabled by AI, and the need to control data warehouse costs despite the anticipated cost reductions with the cloud.Throughout the episode, Sanjay discusses the rapidly evolving field of data observability. He delves into the challenges and costs of data quality, emphasizing the importance of the right data at the right time and cost. Sanjay explores the concept of zero-touch data observability, likening it to level 4 automation in autonomous vehicles. He touches on the role of AI and ML in this context. The conversation also veers towards the new emerging dilemma where even though the cloud was supposed to reduce cost, businesses now find themselves seeking innovative ways to control costs within their cloud data warehouses..Bio:Sanjay Agrawal is a two-time co-founder of Revefi and ThoughtSpot. Sanjay has spent over 2 decades building foundational databases, technologies, SQL optimizers, and automating performances for entire warehouses. His latest endeavor, Revefi, offers a zero-touch, 360-degree data observability and monitoring solution for cloud data warehouses. At ThoughtSpot, he was instrumental in building a self-managing, distributed in-memory ACID compliant data warehouse capable of operating at 100 nanoseconds per input table.Key Quote:“Cloud data warehouses like Snowflake, RedShift, BigQuery, Databricks, and Azure have become the de facto place where businesses pull data out and use it for a business purpose. So the more compute you push on the cloud data warehouse, the closer it stays to the ecosystem and the easier it is for anyone to even consume such a system.”- Sanjay AgrawalEpisode Timestamps(Segment 1) Challenges(1:25) Motivations as a two-time founder(2:37) Defining data observability(5:32) Quantifying impact of poor data quality(8:47) Understanding the problem of bad data(13:08) Organizational responsibilities for data quality(15:30) Data quality and/or analytics (Segment 2) Solutions(18:17) Challenges to zero-touch data observability(21:15) Data observability in centralized warehouses(23:52) Managing cloud data warehouse costs(29:07) Leveraging AI/ML for data quality(32:06) Building a non-invasive observability platform(Segment 3) Business Opportunities(34:39) Product vision for data observability(Segment 4) (37:56) Takeaways LinksSanjay Agrawal’s LinkedInRevefi WebsiteThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website

This episode of The Analytics Edge is brought to you by NetSpring and showcases John Humphrey, former Head of Data Platform Product and Chief Data Officer at Intuit MailChimp. John joins us to discuss data strategies for Customer Analytics, from continuing investments in Customer 360 to the emergence of warehouse-native data platforms. He delves into the technology gaps that forced a detour from our earlier vision of Customer Analytics. As products became more digital, the data platforms at the time were unable to handle all the event data being produced – leading to the emergence of specialized first-generation product analytics platforms, data silos, and fragmented analytics platforms for product analytics. John introduces 4 levels of Customer Analytics maturity to help data leaders rationalize earlier investments, and how the cloud data warehouse is enabling warehouse-native strategies and applications to break down those earlier silos, and finally now, deliver on the promise of C360 and Customer Analytics.Bio:John Humphrey is the former Head of Data Platform Product and Chief Data Officer at Intuit MailChimp, with over 2 decades of experience and expertise in data science and data engineering. He was the first data analyst at Goodreads (later acquired by Amazon), helped take LegalZoom public, and has had multiple stints in data leadership roles at Meta and Intuit along the way. John earned a masters in Systems Engineering from the University of Virginia and holds a bachelors in Management Science from Virginia Tech.Key Quote:“TBD”- John HumphreyEpisode Timestamps(Segment 1)(1:16) Earlier roles in product analytics(3:30) Shortcomings of 1st-gen product analytics(9:08) Shortcomings of BI for product analytics(11:26) Funnels in BI vs. product analytics(13:04) Customer 360 and the data warehouse today(15:23) Data streams for complete C360(17:42) Customer 360 versus a CDP(20:48) Aligning C360 & CDP strategies with the warehouse(Segment 2)(23:53) Benefits of cloud data warehouses(26:11) Analytics tools for all types of customer data.(29:38) Maturity model for Customer Analytics (Segment 3)(34:02) AI-powered Customer Analytics (Segment 4) (37:50) TakeawaysLinksJohn Humphrey’s LinkedInThomas Dong’s LinkedInNetSpring Website

Jason Davis, Founder and CEO at Simon Data, discusses the modern martech stack and the role of customer data platforms in marketing analytics. He explores the advantages of using the data warehouse as the center of marketing and customer data, as well as the impact of generative AI and LLMs on marketing analytics.

This episode features an interview with Anahita Tafvizi, VP and Head of Data Science & Business Operations at Instacart. Instacart is the leading grocery technology company in North AmericaAs a senior executive at Instacart, Anahita drives key operations and strategic decisions across all company product pillars and ensures data investments are aligned with the long-term business strategy. She leads a team of over 150+ Data Science and Strategy individuals across all company product lines including consumers, shoppers, advertisers, and retailer products. Previously, Anahita was the Director of Finance for Google Commerce, Retail & Travel, as well as the Head of Finance for YouTube Ads and Head of Analytics & Data Science for eBay Ads. She is passionate about building high-performance data and strategy organizations with a focus on agility and impact. Anahita earned a Ph.D in Physics from Harvard University.In this episode, Anahita talks about structuring her data science team to reveal opportunities for new efficiencies that guide Instacart’s 4-sided marketplace, her approach to hiring the leadership team and overseeing 150+ employees, and reveals recent data science initiatives fueling product-led growth.Bio:Anahita Tafvizi is currently the Vice President and Head of Data Science & Business Operations at Instacart. As a senior executive of the company, she drives key operations and strategic decisions across all Instacart product pillars and ensures data investments are aligned with the company’s long-term business strategy. She is passionate about building high-performance data and strategy organizations with a focus on agility and impact. Key Quotes:“How can we make the experience of buying groceries on Instacart not just more convenient but also more efficient and delightful? To inspire product strategy, we spend a lot of time trying to understand patterns of shopping so we can build personalized experiences.” - Anahita TafviziEpisode Timestamps(01:17) Anahita’s path to data science(03:04) Instacart’s 4-sided marketplace(04:56) Structure of the data science team(07:10) How business teams can unlock new insights(09:51) Benefits and drawbacks of virtual teams(12:45) Data needs of product-led growth(14:30) Key data science techniques, tools, and skills(16:30) Recent data science initiatives fueling PLG(19:16) Instacart's data maturity(20:20) Data access for business context(22:00) Approach to hiring data science leaders(23:30) Career growth paths in data science(26:37) Increasing internal talent bench(27:59) Driving efficiency in an economic downturn(32:08) Key insights on grocery delivery services(34:35) TakeawaysLinksAnahita Tafvizi's LinkedInInstacart WebsiteThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website

This episode of The Analytics Edge, sponsored by NetSpring, features an interview with Soumyadeb Mitra, Founder and CEO at RudderStack, the leading warehouse Customer Data Platform that’s purpose-built for data teams. RudderStack is an open-source, enterprise-ready platform for collecting, storing, and routing customer event data to your data warehouse and dozens of other tools. After founding the company in 2019, Soumyadeb led RudderStack to 100+ employees and a $56 million Series B funding round in 2022. Prior to RudderStack, he co-founded Mariana, a VC-funded B2B martech startup, which was later acquired by 8x8 in 2018. Soumyadeb earned his PhD in Computer Science from the University of Illinois Urbana-Champaign.In this episode, Soumyadeb talks about the founding stories behind RudderStack, the evolution of Customer Data Platforms, and the significant impact that a warehouse-centric CDP approach has on business.Key Quotes“We want to look at product funnels and customer journeys, but then combine that with Salesforce data, right? I mean, I want to look at funnels separately for enterprise customers and customers who closed versus customers where we are competing with a specific vendor and so on. And this is a very standard thing I would imagine. I mean, we see that across all our companies, but it was surprisingly hard to do with a lot of these cloud product analytics tools, right? They're amazing tools, but then they're only designed to ingest a specific kind of data. And if you want to combine other data sources, it becomes really fragile and complicated to set up those data pipelines, right? So yeah, I think Warehouse-native enables that and kind of unlocks that set of use cases. Plus there are all these challenges around data privacy, which again, it's not so much for a company like us, but at scale, it becomes a problem, right? I mean, you're centralizing your data in a data warehouse. Why do you need to ship everything to another vendor to do specific parts of your analytics? It just does not make good sense..” - Soumyadeb MitraEpisode Timestamps(01:11) Founding story behind RudderStack(02:50) The evolution of CDP(06:50) Business challenges CDPs are trying to solve(08:06) Packaged vs. composable debate(10:55) Benefits of warehouse-native CDP(17:14) Analytics on customer data(18:47) Data activation and reverse ETL(21:10) Real-time personalization(26:05) Achieving customer 360 view(28:08)  Business impact with a warehouse-centric CDP approach(30:17) The future of CDPs(34:48) TakeawaysLinksSoumyadeb Mitra's LinkedInRudderStack WebsiteThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website

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.-----------Key QuotesAmplitude 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-----------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-----------LinksNikita Shamgunov's LinkedInNeon WebsiteThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website

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-----------LinksAwinash Sinha's LinkedInBask Iyer’s LinkedInZoom WebsiteThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website

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.-----------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-----------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-----------LinksAmit Prakash’s LinkedInThoughtSpot WebsiteThoughtSpot Generative AI MeetupThomas Dong’s LinkedInVijay Ganesan’s LinkedInNetSpring Website