The Analytics Edge

Data and AI in Media & Entertainment with Sanjeevan Bala, Group Chief Data & AI Officer at ITV

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 Bala EPISODE 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) Takeaways LINKS Sanjeevan Bala’s LinkedIn [https://www.linkedin.com/in/sanjeevanbala/?originalSubdomain=uk] ITV Website [https://www.itvplc.com/] Thomas Dong’s LinkedIn [https://www.linkedin.com/in/tomdong/] Vijay Ganesan’s LinkedIn [https://www.linkedin.com/in/vijayganesan/] NetSpring Website [https://www.netspring.io/]

Self-Service Analytics and the Future of Work with Saket Srivastava, CIO at Asana

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 Srivastava EPISODE 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

Data Operations for Cloud Data Warehouses with Sanjay Agrawal, Co-Founder and CEO at Revefi

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 Agrawal EPISODE 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   LINKS Sanjay Agrawal’s LinkedIn [https://www.linkedin.com/in/sanjayagrawaliitk/] Revefi Website [https://www.revefi.com/] Thomas Dong’s LinkedIn [https://www.linkedin.com/in/tomdong/] Vijay Ganesan’s LinkedIn [https://www.linkedin.com/in/vijayganesan/] NetSpring Website [https://www.netspring.io/]

Customer Analytics, C360 & Warehouse-Native with John Humphrey, former Head of Data Platform Product and CDO at Intuit Mailchimp

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 Humphrey EPISODE 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) Takeaways Links John Humphrey’s LinkedIn [https://www.linkedin.com/in/johnmhumphrey/] Thomas Dong’s LinkedIn [https://www.linkedin.com/in/tomdong/] NetSpring Website [https://www.netspring.io/]

Understanding Your Customer with Marketing Analytics with Jason Davis, Founder and CEO at Simon Data

This episode of The Analytics Edge, sponsored by NetSpring, features Jason Davis, the Founder and CEO at Simon Data. Simon Data is a next-generation customer data platform (CDP) built on Snowflake. It offers the fastest and simplest way to provide a true 360 degree customer view for your marketing team. Jason describes the modern martech stack and how CDPs are moving to the data warehouse, the differences between packaged and composable CDPs, and how marketing analytics helps marketers reach customers more quickly and more effectively via personalization. With a PhD in machine learning, data mining, and statistics, Jason has helped Simon Data provide businesses the spending trends, behavioral trends, and direct customer feedback required for a comprehensive understanding of their customers. Jason was also the founder of Adtuitive, an adtech platform that was acquired by Etsy to power Etsy’s own internal ad systems.  In this episode, Jason discusses strategies for centralizing data, the role of the data warehouse, and how GenAI and LLMs are impacting marketing. BIO: Jason Davis is the Founder and CEO at Simon Data. With a PhD in machine learning, data mining, and statistics, Jason was also the founder of the retail adtech platform, Adtuitive, which was acquired by Etsy in 2009 to power Etsy’s own internal ad systems.  KEY QUOTE: “True centralization aggregates data from all channels, not just what someone clicked on the website – offline, IoT, support... Let's have shipping and returns data and everything else that is required to properly instrument a real business put in a single place, which is governed, secure, complete, and accurate.”- Jason Davis EPISODE TIMESTAMPS (1:22) Founding stories: Adtuitive & Simon Data (5:58) The modern marketing data stack (10:20) Data centralization strategies (11:57) Security, privacy, and compliance (13:39) GA4 and alternative strategies (15:33) Composable CDPs vs packaged CDPs (18:10) Breaking down data silos (20:14) Data infrastructure and cloud migration  (21:09) Why Simon chose Snowflake? (22:21) Real-time personalization (25:17) The importance of Reverse ETL (30:50) Customer 360: Where are we now? (34:35) The impact of GenAI/LLMs on marketing (35:20) Takeaways LINKS Jason Davis’ LinkedIn [https://www.linkedin.com/in/drjasondavis/] Simon Data Website [https://www.simondata.com/] Thomas Dong’s LinkedIn [https://www.linkedin.com/in/tomdong/] Vijay Ganesan’s LinkedIn [https://www.linkedin.com/in/vijayganesan/] NetSpring Website [https://www.netspring.io/]