What a week! We’re just getting back from Snowflake Summit 2024 and are still basking in the afterglow of another incredible industry conference. Alation showed up in full force as a double-black diamond sponsor, with a big booth, several customer speaking sessions, after-parties, and a space on the terrace so fun it scooped the award for most engaging activation space!

Whether you were at Summit or couldn’t make it this year, this blog will give you a full recap of all the best bits you missed. Let’s dive in!

Keynote highlights: Iceberg, Internal Marketplace, and Cortex AI

By now we know: you can’t build trusted AI without trusted data. In his keynote address CEO Sridhar Ramaswamy presented a slew of updates aimed at enhancing both AI capabilities and the underlying data foundations that support them. 

AI and data management: a dual focus

While AI takes the spotlight, Snowflake understands the importance of robust data management. Ramaswamy, who hails from AI search vendor Neeva, highlighted this balance. Snowflake’s announcements included general availability of external tables on Apache Iceberg, the introduction of an Internal Marketplace, the launch of Universal Search, and a preview of AI-powered object descriptions.

Key announcements enhance data foundations for AI

The long-awaited general availability of Iceberg external tables allows customers to store their data in AWS, Azure, and Google Cloud. This move, paired with the unveiling of the Polaris data catalog, enables querying across various engines. (As a technical tool, Polaris facilitates communication between compute engines and the storage layer, nicely complementing Alation’s platform for business users.)

Snowflake’s Internal Marketplace empowers companies to create data marketplaces unique to their enterprise. Experts can publish and curate data products, from datasets to machine learning models, facilitating internal data sharing and business use.

Universal Search, an AI-powered search engine, integrates data from internal tables, Iceberg tables, third-party providers, and the Internal Marketplace. This comprehensive search capability simplifies data discovery, making it easier for users to find relevant information without needing to know its exact location.

Snowflake announced that Data Quality Monitoring will soon be generally available, helping data governors and stewards enhance the monitoring of data sensitivity and quality. The Alation Data Quality Processor for Snowflake leverages this quality information through Snowflake Horizon and centralizes it in the Data Health tab of the Alation Data Intelligence Platform. This provides holistic data access and quality insights to ensure AI-ready data from Snowflake and other data and BI assets across the enterprise.

AI-powered innovations

AI-powered object descriptions, leveraging LLMs, automate documentation, making data more accessible and easier to understand. This feature, along with enhancements to Snowflake Cortex AI, underscores Snowflake's commitment to integrating AI into their ecosystem.

Cortex AI now includes new services like Cortex Analyst and Cortex Search. These tools allow businesses to query their analytical data and build high-quality chatbots quickly. Cortex Guard, another addition, focuses on filtering harmful content, enhancing data security.

Snowflake’s Data Cloud Summit 2024 showcased a harmonious blend of AI innovation and solid data management foundations. These enhancements not only meet the current demands of AI-driven enterprises but also pave the way for future advancements in data intelligence.

USAA: data governance for customer experience

In the customer session, How USAA Delivers an Exceptional Customer Experience with Well-Governed Data, Chief Data Architect Adam Udell revealed how the century-old company has achieved a unified data environment in pursuit of an exceptional customer experience. In his talk, Udell shared how USAA’s history dates back to 1922 when six Army officers who were unable to secure auto insurance due to the high-risk nature of their military lifestyle founded the company to serve military members and their families. Today, USAA supports about 13.5 million members with a workforce of 37,000 employees.

All that data and history resulted in some serious legacy challenges, as well as cultural resistance to change. To modernize their data environment (and deliver better customer service) USAA prioritized three goals:

• Achieving a unified data environment

• Leveraging data insights to improve business operations and member experiences

• Transitioning their data environment to enhance the lives of their members

To get there, Udell focused on key initiatives like improving the data environment, moving analytics workloads out of data centers to platforms like Snowflake, and creating a comprehensive data ecosystem. He also emphasized the importance of not working in silos, ensuring all components work together with the right policies, standards, and processes in a transparent data governance framework. 

Data modernization for insurance: Three leadership strategies

Udell recommended three strategies for others seeking to achieve similar goals:

• Apply Retail Principles to Data Management: Data management supply chains have much to learn from retailers. Just as retail uses a structured approach to move products from producers to consumers efficiently, a similar model can be applied to data systems. This involves managing data lifecycle and inventory (in a data catalog), and creating valuable data products.

• Leadership for Change: Successful data management initiatives require strong leadership support and change management strategies. It is crucial to adapt communication for different audiences and emphasize the importance of flexibility and interoperability in the data ecosystem. Showcasing progress and engaging the internal community through events like an annual tech conference helps drive alignment and adoption across the organization.

• Choosing and Using Alation: USAA chose Alation for its user-friendly experience with data across different roles including data science and data engineering. Alation's native integrations and open APIs allowed it to fit seamlessly into USAA's broader data ecosystem. Furthermore, Udell pointed to the fact that Alation provides visibility into data usage and adherence to governance processes, helping optimize data migration and utilization decisions.

Fiserv accelerates data modernization with Alation and Snowflake

In another fascinating customer session, Intelligent Cloud Migration and Optimization with Alation, Jim Haas, Sr Director, Chief Data Architect and Allen Goldschmidt, Director of Data Management at Fiserv shared their transformative journey in modernizing their analytics landscape. As a leading B2B company in the FinTech sector, Fiserv handles vast amounts of data across various domains, including banking, merchant services, and credit card issuance. Their session highlighted the challenges and solutions they encountered while integrating Snowflake and Alation to create a more efficient and insightful data environment.

How Fiserv used Alation to modernize their Data Cloud

• Modernizing Analytics Landscape: Fiserv embarked on a mission to modernize their analytics landscape with the goals of gaining efficiencies, enabling new products, and improving data sharing with customers. By leveraging Snowflake's data-sharing capabilities alongside Alation’s cataloging features, they built a federated yet connected analytics environment in the cloud. This approach allowed them to securely share data, describe and communicate metadata, and drive better business intelligence across their organization.

• Integrating Alation for Enhanced Data Management: Alation played a crucial role in Fiserv's data modernization strategy by providing robust data cataloging and metadata management. This integration helped Fiserv address significant pain points, such as managing institutional knowledge and understanding the vast array of data assets. Alation's features enabled them to catalog data effectively, provide lineage insights, and utilize AI for automating and enhancing data documentation processes. As Haas said, "Using tools such as Alation is important for us because we have a lot of data that needs to be described and understood by masses, and that tool helps us do that."

• Federated Data Stewardship Approach: Fiserv adopted a federated data stewardship model, recognizing that a central team could not manage the entire company's data needs. This model involved a small central team working with business units to educate and empower them in managing their own data. By starting with pilot projects in friendly business units and gradually expanding, Fiserv ensured a smooth and scalable implementation of their data catalog and governance processes.

Fiserv's session provided an inspiring look into how a major FinTech company can overcome significant data management challenges. Through the strategic use of Snowflake and Alation, Fiserv has successfully modernized its analytics landscape, enhancing both internal and customer-facing capabilities. Alation has become an integral part of what it means to trust and manage data intelligently at Fiserv. One executive said, “Data that’s not cataloged is useless to me.” The team’s experience underscores the importance of adopting modern data management tools and approaches to stay competitive and innovative in today's data-driven world.

Talking about data modernization, there was a buzz at Summit around migrating legacy processes and data assets to Snowflake while ensuring that migrated workloads were optimized and ready to scale. Supporting this, Alation announced the Peak Performance Snowflake Native App (available now within the Snowflake marketplace in partnership with Spreems), which can help identify, prioritize, and de-risk the modernization journey while ensuring that migrated workloads can be closely monitored for efficiency in the AI Data Cloud. 

Alkermes conquers complexity with Alation

Bo Yang is the Director of Commercial Data Strategy and Governance at Alkermes, a global biopharmaceutical company with a portfolio of drugs that treat mental health. Back in 2020, they sought to launch Lybalvi, the first oral medication for the treatment of both schizophrenia and bipolar disorder. To achieve this, Yang’s team set out to rebuild the data architecture and strategy across the company. In the session, Data Governance Best Practices in Life Sciences, Yang shared the story of that journey from chaos to clarity. 

“We have thousands of tables, billions of records, and hundreds of thousands of columns in our commercial data warehouse,” Yang shares. “Data dictionaries and business rules about data were just all over the place. Some of them are in Excel files, or Powerpoints, or Word documents, or just living in people's heads. All this data is constantly updated. Keeping up with it just becomes mission impossible.”

“So how do you make sure you have an essential repository to keep up with all this information?” he continues. “And how do you make it easy for people to find and understand the data?” In the two years leading up to the launch of Lybalvi, Yang built that central repository with Alation. Here are his three learnings for others facing a similar challenge:

Take a pain-point-focused approach to tool selection

How could Yang find a tool that would address the needs of data users and the business? “We tried to identify the key pain points and prioritized the resolution of these pain points based on business value and impact,” Yang says. “It all boils down to having the right people with the right skill set and mindset and the right tool to equip them to create these core data capabilities. These include master data management (MDM), data, operation, and optimization, as well as acquisition and governance.”

First on the roadmap was rebuilding their MDM and commercial data warehouse using semantic layers, so users could find and use the data more efficiently and effectively. With this warehouse in place, the team then moved to centralizing data acquisition and improving data quality. These changes made data easier to find and use. “So, as we got close to the launch of Lybalvi, we actually saw the data utilization reach a new high,” Yang recounts. “And together with that, the complexity of data and our data ecosystem reached a new high as well.” More data usage – and complexity – made the need for data governance more pronounced. “We realized we had to stand up and formalize our data governance program,” he concludes.

Invest in data governance and training for the business

To formalize data governance, “We knew a tool like a data catalog would be very, very helpful,” says Yang. At this point, with MDM and a data warehouse established, he had the proper foundation for a powerful catalog. “After our first phase implementation, we rolled out our first ever commercial data catalog which has really helped us continue our journey in enhancing our data governance and data literacy.”

How did Yang ensure the rollout was successful? It comes down to people. “Change management is so important when you're rolling out a tool like this,” he shares. “You want to make sure you provide sufficient training. You want to make sure that the data catalog becomes part of your everyday process. Because at the end of the day, you want people to understand the value of a tool like this and start using it to realize those benefits.”

Those benefits must extend to the business. “It’s key that you involve the business from the very beginning,” Yang emphasizes. “This is not a project just for your IT folks and your data people. It's really for the end users. So to be able to identify the core, functional stakeholders and those power users from the get-go and having them be part of the journey is very important. You also want to work with your data SMEs to make sure the business content is curated as well. Change needs to happen from the bottom up and top down.”

This is not to say that there’s one perfect approach. Yang underscores this when he says, “Don't let perfection be the enemy of good.” While planning is important, “The execution of the plan is not always going to be perfect. So it's always important to be agile because your strategy can change.”

Pick a data platform embraced by the community

After a rigorous POC with input from a broad range of stakeholders, Yang found the best tool for Alkermes in Alation. “We basically had a very unanimous decision when we selected Alation,” he says. “At the end of the day, you want to choose something that fits your business needs and your business model the best…. We found that Alation not only helps the IT organization and data team but also the broader user community.”

Having that central data repository in Alation empowers people to self-serve and learn with ease. “We find people are more knowledgeable about data because all this information is now at their fingertips. They don't have to phone a friend like they used to. They can easily go into Alation, and look up information like a Google search engine. Alation really allows people to find, understand, and trust the data.”

For Yang, the value offered by the Alation platform makes it much more than a catalog. “It's evolving into a comprehensive data intelligence platform,” he says. “Our data and IT teams can capture important aspects of data in one central place: Things as simple as table and column definitions, to more complex business rules, and even data caveats. We’re also able to scan our data processing pipeline and service some of the useful data lineage information which helps us make impact analysis a lot easier.”

All these changes have one ultimate purpose: improving patient outcomes. “Everything that we do, from our drug discovery to data management and governance is all about making sure that we can help our patients gain access to our medication to improve the health of their life,” Yang concludes. 

Thomson Reuters automates access for faster insights

Thomson Reuters needs data analytics to deliver timely information fast. But manual access requests and static controls caused delays and made data processes slow and complex. The data team needed a way to automate access for more people and speed up time to insight. In the talk How Automating Data Access Accelerates Insights at Thomson Reuters, Umayal E., Director of Data Platform, shared how the information conglomerate uses Immuta, Alation, and Sailpoint to scale their enterprise data management and accelerate access securely. Here are 3 key takeaways from the talk:

1. Transformation through integration and automation

Thomson Reuters has significantly enhanced their data management capabilities by integrating Snowflake with the Immuta data security platform, Alation, and SailPoint identity and access management. This integration has facilitated a streamlined, automated workflow for data access requests, shifting from labor-intensive, error-prone manual processes to a sophisticated, attribute-based access control system. The result is a drastic reduction in turnaround times for data access from days and weeks to mere minutes and hours, increasing operational efficiency and user satisfaction.

2. Data governance supports AI and data culture

Leadership sought to build a data-driven culture supported by strong data governance foundations. By establishing a centralized enterprise data platform, they have created a single source of truth for the organization, enabling consistent and secure data access. This platform not only supports various internal business processes but also integrates with their enterprise AI platform, underpinning their vision to be a leading content-driven, AI-powered company. The move to an operating company model and the focus on data governance have enhanced new levels of data transparency, discoverability, and trust within the organization.

3. Empower innovation while ensuring compliance

Balancing speed and security was a significant challenge for Thomson Reuters. The adoption of Immuta's platform allowed them to automate and scale their data governance processes effectively. This approach has empowered their data scientists and AI practitioners by providing timely access to critical data, thereby accelerating the development of AI models and use cases. At the same time, the robust security measures and compliance controls have ensured that they stay ahead of data protection requirements, reinforcing the trustworthiness of their platform and data.

Thomson Reuters' journey underscores the importance of integrating advanced data management tools to foster a data-driven culture, enhance operational efficiency, and enable innovation. By automating data access controls and improving governance, they have not only accelerated their data initiatives but also ensured robust compliance and data security, setting a benchmark for organizations aiming to leverage data assets more effectively.

Data Cloud Now spotlight: How data intelligence fuels AI

Over on the trade show floor, Snowflake anchor Ryan C. Green welcomed Alation CEO Satyen Sangani along with Marc Rind, CTO of Data at Fiserv, for a joint interview. The trio discussed how a data intelligence platform fosters an understanding of data to fuel analytics, governance, and AI – within and without the business. 

“An understanding of the data is critical,” Rind pointed out. “Sharing that data across our entire enterprise would not be possible unless we understood where that data came from and the purpose of why it's there in the first place. Alation helps us understand and catalog all of that data so whether that's across business units or even out to our own customers, they can understand and trust that that data that they're using is the data that they expect it to be."

In a sea of questionable data, data analysts and scientists often struggle to find that which they can trust. This is where a tool like Alation becomes critical. “What Alation does is when we partner with a customer like Fiserv, we're really giving them the insight and information to be able to know which datasets to be able to manage,” Sangani elaborated.

That clarity becomes exponentially more important when AI is involved. "AI models come and go, and they are improving every couple of days,” remarked Rind. “So it matters about the data that it's using… AI becomes the new UI. It's critical for all of that to tie together with that trust factor, and having a partner like Alation to help us with the cataloging and the understanding and the trust of that data is a key part to all of it."

Scalable AI (and governance!) with Alation and Snowflake

Over at the Alation booth, our team of product experts was joined by Snowflake’s Data Governance Specialist Ravi Kumar for two 90-minute hands-on lab sessions. 

The 100+ attendees were treated to a robust trial complete with a well-stocked cloud environment courtesy of Snowflake Partner Connect. Participants learned about the data governance lifecycle and how the Alation framework supports scalable governance. They also got a first-hand look at how policies in Alation map back to Snowflake, with dynamic data masking and powerful, intelligent search and lineage to explore it all. The lab concluded with a crash course in Compose (Alation’s intelligent SQL editor) and an introduction to our new Data Quality Processor integration with Snowflake. 

Finally, Takashi Suzuki, General Manager of the Data Platform Department at NTT Docomo led a riveting session on leveraging customer data for a potent loyalty program. As Japan’s largest telecommunications company, NTT Docomo manages a vast trove of data. In Democratizing Data Across NTT Docomo with Streamlit, the NTT team shared how they use Alation as the entry point for more than 2,5000 data users, who rely on the platform to quickly find, use, and understand trusted data. As a direct result of this data democratization, NTT reports a 10X productivity improvement through the collaboration and sharing made possible by Alation. 

When it comes to a potent partnership, these sessions represent just the tip of the iceberg of what’s possible when you pair Alation with your Snowflake Data Cloud. Indeed, the entire conference, from the keynote to the customer stories and booth activations, stood out as a testament to the power of teamwork and collaboration where data and AI meet technology. 

Curious to learn more about our partnership with Snowflake? Explore our partner page.