Snowflake Summit 2023 outcomes and paving the way for a new era of AI-powered data management

DAS42 POV Series

Whether you’re a tech enthusiast or a business leader looking to stay ahead of the curve, pay attention to the striking key themes from the Snowflake Summit 2023, held in June. Throughout the year, DAS42 will explore these top themes shaping the future of technology-driven organizations. We’ll give our perspective on the how, when, and why of adopting these industry advancements.

The industry is evolving, and Snowflake is making strides in machine learning and generative artificial intelligence (AI), cost optimization, native apps, container services, and data privacy and security. Leveraging these powerful tools is a key component to breaking down business silos. Follow this “DAS42 Point of View” series as we explore how these trends are revolutionizing how businesses operate to achieve efficiency, scalability, and robust data protection in today’s dynamic digital world.

The announcements at Snowflake focused on bringing AI, large language models (LLM), and other applications to your data versus the far less secure and more complex model of moving your data outside of your cloud. As Nick Amabile, DAS42 CEO, shared at Summit 2023, “Enterprises need to shift their thinking from ‘bringing data to their apps’ to ‘bringing apps to their data.’” This idea allows organizations to build, train, and deploy AI-powered applications within Snowflake’s Data Cloud, where their proprietary enterprise data already resides. And, as Nick also shared, with all of this happening in your cloud environment, the operation is “secure, governed, and centralized.”

“Hey, Snowflake, How Much Profit Will My Company Make in 2024?” 

Snowflake Summit 2023 is a testament to Snowflake’s commitment to pushing the boundaries of AI-powered data management, enabling organizations to embrace the transformative potential of AI while upholding the critical principles of security and data privacy. By leveraging large LLMs and generative AI to democratize data, answer complex business questions, and drive actionable insights, Snowflake recognizes a future for this innovation.

Day one of the summit began with a keynote announcement from tech partner NVIDIA’s CEO Jensen Huang and Snowflake’s CEO Frank Slootman on bringing NVIDIA’s industry-leading GPU and AI modeling technology to the Snowflake Data Cloud. This technology will enable businesses to quickly build custom AI-powered applications using generative AI trained on their proprietary data, all within the security of the Snowflake Data Cloud. To be able to create a LLM that’s specific to your business and ecosystem to best inform transformative AI outputs is game-changing!

Ignoring what goes on under the hood for the moment, what does this mean for the business user?

You can now speak directly to your data to get the answers to drive insights and informed decisions. For data and analytics teams, this takes self-service a step further and helps lead higher business ownership and efficiencies – a complete 180-degree shift from the traditional history of communicating with computers. Through natural language processing (NLP), you can now speak in your native language to query your data and drive action, versus speaking in a programming language like SQL or Python. NLP acts as the “Rosetta Stone” to translate end-user requests into a language that Snowflake can comprehend. Generative AI can then answer business queries and provide visualizations to facilitate consumption, accessibility, and utility of the data, all enhancing the user experience and taking data democratization for organizations to an entirely different level. 

Imagine, for a moment, the power of being able to directly ask questions and receive answers from data without having to translate your words through the help of programmers. Now, imagine how queries and insights could be more powerful by leveraging your LLM built off the data from a business and its ecosystem. This process generates enriched data sets that include first-, second-, and third-party data and leverages machine learning (ML) to be more accurate, focused, comprehensive, and intelligent over time.     

As a finance end user, you can ask questions directly (in natural language), such as “What was our revenue and margin by product last quarter?” or predictive queries like “Based on historical trends on churn, revenue, and profitability and current backlog and pipeline, what does the next quarter’s forecast look like?”. Leveraging second- and third-party data through Snowflake’s Data Collaboration, you can even layer macroeconomic data into a forecasting model to see the impact of interest rates, currency exchange rates, and inflation on organization projections. 

As a sales or revenue-focused end user in a subscription-based business, you could ask descriptive questions, such as “How many subscribers did we onboard last month by each channel and product?” and predictive queries, like “Based on trend analysis as it relates to churn and retention and new subscribers added per period, what does my next quarter look like from a number of total subscribers and associated revenue?”. Again, leveraging second- and third-party data through Snowflake’s Data Collaboration allows you to layer on market data around cohorts, behavior, and demographics to inform the model and insights to educate a forecasting model and garner a better understanding of potential outcomes. 

These two use-case examples show how this empowers business users, arming them with a treasure trove of accessible data at their fingertips to drive business-critical decisions and strategy. But there’s a cautionary tale here. A business cannot reap the fantastic benefits of AI/ML without first investing properly in the foundational elements that make this all possible. 

Organizations need to ensure the appropriate bedrock is in place to reach the level of data maturity to support and execute the level of AI/ML properly. These include:

• Data Strategy: a well-conceptualized, universally aligned, and actionable data strategy to define objectives, rules of engagement, investment, owners, and vision for data and its valuable role in the organization.

• Modern Data Platform: a platform built atop best-of-breed tools (like Snowflake) to ensure efficient processing, workflows, and holistic datasets to leverage. DAS42’s Full Stack Philosophy takes an end-to-end approach to platform architecture, strategy, and implementation, allowing an organization to think more holistically about its platform, strategy, and roadmap. 

• Data Literacy: a level of data literacy maturity to ensure the team understands what data is as a product and how to leverage data to drive informed decisions.

• Data Quality: a set of standard definitions, metrics, and KPIs, as well as identified sources of data to ensure accurate, trusted data. 

• Data Governance: a toolset and process that establishes and maintains checkpoints and guardrails to ensure that data remains holistic, accurate, controlled, and, ultimately, trusted by business users. 

• Expert Advisory and Consulting: a plan to ensure industry standards and best practices are followed by leveraging a data and analytics Professional Services organization like DAS42 to help facilitate adoption through organizational change management, deliver business-focused solutions that drive ROI, and accelerate time to value.

The new era of Snowflake enables organizations to harness the power of AI without compromising on crucial aspects, such as security and privacy. Businesses now have more opportunities to unlock the full potential of their data by leveraging advanced AI capabilities within the Snowflake platform.

By prioritizing security and data privacy, Snowflake ensures organizations can confidently explore and implement AI-driven insights and innovations – empowering businesses to drive meaningful outcomes, make data-informed decisions, and gain a competitive edge while maintaining the highest data protection standards.

Snowflake demonstrates its commitment to helping customers optimize and control their costs on the platform by offering features like AI/ML, budgets, and warehouse utilization. These tools enable customers to effectively manage their spending, align their usage with budgetary constraints, and make data-driven decisions to improve cost efficiency and performance within the Snowflake Data Cloud.

Ready to get started or unsure where to begin? Take advantage of a free 30-minute consultation to learn how we can help your organization no matter where you are in the data maturity lifecycle. You can also take our data maturity quiz to learn how we can help your organization no matter where you are in the data maturity lifecycle.

Tom Stentiford is the Chief Delivery Officer at DAS42, managing the client experience and ensuring project execution and delivery. DAS42 is a data consultancy with a modern point of view.