Data Operations for Cloud Data Warehouses with Sanjay Agrawal, Co-Founder and CEO at Revefi
12/12/23 • 43 min
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
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
Previous Episode
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
Next Episode
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
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