Sarah Nagy, Co-founder and CEO at Seek, discusses using large language models to simplify data querying for non-experts, reduce data team workload, and provide a natural language interface to databases. The podcast explores empowering users with efficient data query solutions, revolutionizing data querying with Seac and CED, a multi-model approach in AI systems, navigating data queries and user experience, and the tools and challenges in data analysis with a focus on empowering data professionals with Seek.
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Quick takeaways
Large language models enable non-experts to query data easily, reducing strain on data teams.
Natural language interface to databases empowers users, revolutionizing data querying processes.
Deep dives
Revolutionizing Data Querying with Natural Language Processing
One of the most promising applications of large language models is enabling non-experts to effortlessly query their data, reducing the strain on data teams. SEEK, led by Sarah Nagy, is leveraging natural language processing to transform how teams interact with data. The platform offers a natural language interface to databases, empowering business users to independently access and query data. Traditional tools like Tableau and Looker have attempted to address this issue, but organizations still struggle due to the dependency on data teams for insights.
Challenges in Data Inquiry and Data Team Efficiencies
Data teams often face challenges when bombarded with requests, leading to delays in answering queries and balancing workloads. The inability for non-experts to pose new questions without human intervention hampers scalability within organizations. Existing solutions like BI tools have limitations in addressing this issue, resulting in a reliance on hard-coded data views. SEEK seeks to address these challenges by providing a natural language interface that allows for a seamless querying experience.
Enhancing Collaboration and Efficiency in Data Analysis
Inefficient processes in data analysis, where multiple team members unknowingly work on duplicative tasks, highlight the need for improved collaboration and streamlined workflows. SEEK aims to prevent redundant efforts by facilitating access to previously answered questions and encouraging disciplined use of the platform. The efficiency of data querying and analysis is further enhanced through features like saving chat interactions for future reference.
The Future of Data Querying and Automation
As SEEK endeavors to revolutionize data querying through natural language processing, the future landscape of data analysis may witness a shift towards more intuitive and accessible tools. By enabling non-technical users to ask complex questions and receive accurate insights, SEEK could potentially redefine the skill sets required for data analysis professionals. The platform's ability to automate SQL queries and facilitate data exploration paves the way for a more seamless and efficient data analysis process.
One of the most promising applications of large language models is giving non-experts the ability to easily query their own data. A potential positive side effect is reducing ad-hoc data analysis requests that often strain data teams.
Sarah Nagy is the Co-founder and CEO at Seek which is using natural language processing to change how teams work with data. She joins the podcast to talk about the platform and providing a natural language interface to databases.
Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .