The Modern Data Stack: Driving Value with Data

WebinarDecember 2022

As organizations of all sizes continuously look to drive value out of data, the modern data stack has emerged as a clear solution for getting insights into the hands of an organization’s people. With the rapid pace of innovation not slowing down, the tools within the modern data stack have enabled data teams to drive faster insights, collaborate at scale, and democratize data knowledge.

However, are tools just enough to drive business value with data?

Join Yali Sassoon, Chief Strategy Officer and Co-Founder at Snowplow Analytics, as he outlines the role of the modern data stack in any data-driven organization, the hallmarks of modern data tooling, how data teams can stay focused on value, and more.

Key takeaways:

The role of the modern data stack in driving outcomes with data
The hallmarks of modern data tooling, and how it enables data teams to focus on value
Why tooling and architecture only are not enough to drive value with data

Summary

The modern data stack, which revolves around cloud data warehouses or data lakes like Snowflake, Redshift, BigQuery, and Databricks, is a contemporary approach that organizations are adopting to expand their data capabilities. This strategy, which replaces traditional enterprise data warehousing and Hadoop-based systems, integrates various tools that simplify data ingestion, preparation, transformation, and analysis, resulting in an agile and cost-effective solution. Yali Sassoon, co-founder of Snowplow Analytics, highlights the necessity of starting with distinct use cases and achieving early victories by merging data from multiple sources to solve complex issues such as marketing attribution. The modern data stack's adoption is common across industries, from startups to large enterprises, because of its ability to promote intelligent data utilization. However, Yali advises organizations not to adopt new technologies without a distinct purpose, emphasizing the importance of understanding desired results and involving the appropriate cross-functional teams for success. Building a successful data platform requires a balance between investing in technology and aligning data initiatives with business objectives to ensure a substantial return on investment.

Key Takeaways:

The modern data stack enhances data capabilities through cloud-based solutions, replacing traditional data warehousing and Hadoop systems.
Successful adoption begins with distinct use cases and early victories, particularly through the integration of data from various sources.
Collaboration among cross-functional teams is vital for achieving business outcomes with a modern data stack.
Investments in technology should align with business objectives to ensure a substantial return on investment.
The modern data stack is widely adopted across industries, from startups to large enterprises.

Deep Dives

The Modern Data Stack

The modern data stack ...
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is a strategic approach that organizations choose to maximize their data potential. It typically centers around cloud data warehouses or data lakes, such as Snowflake, Redshift, BigQuery, and Databricks, which offer flexible scalability and lower costs compared to traditional systems. This stack is characterized by a comprehensive set of tools that support data replication, transformation, and analysis, effectively breaking down data silos within organizations. As Yali Sassoon explains, "The modern data stack allows you to pull data from different systems into a cloud data warehouse, enabling multi-source analysis that was previously unachievable." The integration of tools like Fivetran, Snowplow, and DBT within this stack empowers businesses to execute complex data use cases, such as marketing attribution, with newfound precision and insight.

Use Cases and Early Victories

Identifying and executing high-value use cases is vital to the success of a modern data stack implementation. Organizations should focus on use cases that leverage the stack's ability to integrate diverse data sources to deliver actionable insights. As highlighted during the webinar, marketing attribution emerges as a prime example of such a use case, allowing companies to understand the true impact of their marketing efforts across multiple platforms. By merging data from sources like Facebook, Google, and in-house analytics, businesses can uncover previously hidden patterns and optimize their strategies for maximum impact. Yali noted, "Organizations have driven step changes in marketing effectiveness by identifying high-value campaigns that were previously overlooked."

Adoption Across Industries

The modern data stack is being adopted by a wide variety of organizations across different industries, from new startups to established enterprises. Its flexibility and cost-effectiveness make it an attractive option for companies looking to expand their data capabilities without the substantial investments once required by on-premise data solutions. The proliferation of cloud data warehousing technologies has democratized access to advanced data tools, enabling businesses of all sizes to utilize data-driven decision-making. Yali shared that "Snowflake's user numbers continue to grow, as more organizations commit to using data in intelligent ways."

Building a Successful Data Platform

A successful modern data stack implementation requires more than just the right technology; it demands a strategic approach aligned with business objectives. Organizations must prioritize use cases that deliver tangible business value and ensure collaboration among data engineers, analysts, and business stakeholders. Yali emphasized the importance of starting small, advising, "Buy just the tools you need for your initial use case and demonstrate its value before expanding the stack." This approach not only minimizes unnecessary expenses but also encourages stakeholder buy-in by showcasing early successes. Ultimately, the key to success lies in maintaining a clear focus on desired outcomes, managing change effectively, and ensuring that data initiatives are closely integrated with business objectives.