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Government agencies are under growing pressure to do more with less. Leaders are expected to improve citizen services, strengthen transparency, respond to changing regulations, and prepare their organizations for AI. As a result, many agencies are exploring Snowflake to support government data modernization, analytics, and AI initiatives. Yet despite growing investment in technology, data often remains fragmented across disconnected systems, spreadsheets, departmental databases, and legacy platforms. 

That challenge is why events like Snowday Columbus 2026 are generating significant interest across the public sector. 

As data, analytics, and AI continue to reshape how government organizations operate, Snowday provides an opportunity for technology and business leaders to learn practical strategies for modernizing data environments, improving decision-making, and building a foundation for responsible AI adoption. 

For state and local government organizations, the conversation is no longer whether data modernization matters. The focus has shifted to how to make it happen effectively. 

Why Government Data Modernization Remains a Top Priority 

Government leaders rely on data to drive everything from budget planning and workforce management to public safety initiatives and constituent services. However, accessing accurate and timely information remains a challenge when data is stored across multiple systems. 

Common obstacles include: 

  • Disconnected operational systems 
  • Time-consuming manual reporting processes 
  • Limited visibility across departments 
  • Inconsistent metrics and reporting standards 
  • Challenges governing sensitive public sector data 
  • Difficulty scaling analytics and AI initiatives 

Without a unified approach to data management, agencies often spend more time collecting information than acting on it. 

Modern data platforms like Snowflake help address these challenges by bringing data together in a secure, governed environment designed to support analytics, reporting, and AI initiatives at scale. This aligns with the growing public sector focus on creating trusted, organization-wide data foundations that improve visibility and decision-making.  

How Snowflake Helps Government Agencies Modernize Data 

Many agencies have already modernized core operational systems. However, valuable information still resides across finance, HR, permitting, licensing, public safety, education, taxation, and citizen service platforms. 

The next step is creating a connected data ecosystem. 

A modern cloud data platform enables agencies to: 

  • Improve Cross-Department Collaboration: When departments operate from a shared data foundation, leaders gain a more complete view of operations, performance, and service outcomes. 
  • Accelerate Reporting: Teams can reduce the effort required to gather, reconcile, and validate information for executive reporting, compliance initiatives, and public transparency efforts. 
  • Strengthen Data Governance: Government organizations can implement consistent security controls, data access policies, and governance standards without sacrificing accessibility. 
  • Support Data-Driven Decision Making: Leaders gain confidence that decisions are based on reliable, timely information rather than fragmented reports created from multiple sources. 

Building an AI-Ready Data Foundation for Government 

AI remains one of the most discussed topics across government organizations today. From improving constituent experiences to identifying operational efficiencies, agencies are exploring where AI can create measurable value. However, successful AI initiatives require more than technology. 

They require: 

  • Trusted data 
  • Strong governance 
  • Secure architectures 
  • Clear business objectives 
  • Scalable analytics capabilities 

Organizations that invest in modernizing and governing their data today are better positioned to take advantage of future AI opportunities. 

Rather than approaching AI as a standalone initiative, leading agencies are treating it as the outcome of a broader transformation strategy that begins with data modernization. 

This theme continues to emerge across public sector conversations as organizations seek practical, sustainable approaches to adopting AI responsibly.  

Public Sector Analytics Trends Government Leaders Should Watch 

  1. Data Modernization Strategies: Learn how agencies are creating trusted, organization-wide data ecosystems that support reporting, analytics, and AI. 
  2. AI Readiness and Responsible Innovation: Explore practical approaches to adopting AI while maintaining governance, transparency, and public trust. 
  3. Advanced Analytics: Discover how agencies are transforming data into actionable intelligence that supports planning, operations, and service delivery. 
  4. Modern Data Architectures: Understand how leading organizations are connecting data across legacy systems, cloud platforms, and enterprise applications. 
  5. Public Sector Success Stories: Hear real-world examples of agencies using modern data platforms to improve efficiency, decision-making, and mission outcomes. 

Why More Government Organizations Are Choosing Snowflake 

Government agencies require a platform that balances innovation with security, governance, and scalability. 

Snowflake has become a popular choice because it helps organizations: 

  • Unify data across departments and systems 
  • Centralize data from multiple sources 
  • Improve access to trusted, governed information 
  • Support agency-wide analytics initiatives 
  • Enable secure, AI-ready environments 
  • Scale analytics without increasing infrastructure complexity 

For organizations looking to move beyond departmental reporting and build enterprise-wide visibility, platforms like Snowflake provide a foundation that aligns with long-term modernization goals. 

Turning Government Data Strategy into Action 

Technology alone doesn’t create transformation. Success comes from aligning data strategy, governance, business objectives, and organizational readiness. 

Government leaders attending Snowday Columbus 2026 have an opportunity to learn from peers, explore emerging technologies, and gain practical insights for modernizing data and analytics environments. Discussions are expected to focus on data modernization, AI readiness, governance, and real-world public sector use cases that demonstrate measurable outcomes. 

Whether your organization is beginning its modernization journey or expanding existing analytics and AI initiatives, one thing is clear: the agencies that establish trusted, connected data foundations today will be best positioned to deliver better services, improve operational performance, and embrace the future of AI with confidence. 

Meet Avaap at Snowday Columbus 2026 

Avaap helps government organizations create secure, governed, and AI-ready data foundations that support measurable outcomes. From data strategy and analytics to Snowflake implementation and optimization, our team works alongside public sector leaders to turn data into a strategic asset.  

As a Snowflake Elite Partner, Avaap brings proven expertise helping government organizations accelerate data modernization, analytics, and AI initiatives. 

If you’re attending Snowday Columbus 2026, connect with our team to discuss your data modernization roadmap and explore practical approaches to analytics and AI adoption.

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As Workday Rising 2026 approaches, higher education institutions and government agencies are looking for new ways to maximize the value of their Workday investment. While AI and digital transformation will be major themes throughout the event, one trend deserves particular attention: the growing connection between Workday and Snowflake. 

For many organizations, Workday serves as the system of record for workforce, finance, and student data. Snowflake complements that role by providing a scalable platform for enterprise analytics, cross-system reporting, and AI-ready data environments. Together, they help organizations move beyond transactional processes and gain deeper insights from their data. 

Why Snowflake and Workday Are Becoming a Strategic Priority for Higher Education and Government 

Workday and Snowflake serve distinct but complementary purposes. 

Workday centralizes critical business and operational data. Snowflake enables organizations to bring together information from Workday and other enterprise systems to support analytics, reporting, planning, and AI initiatives at scale. 

For higher education institutions and government agencies, this integrated approach can help: 

  • Improve student success and retention initiatives 
  • Enhance workforce planning and talent analytics 
  • Strengthen financial planning and forecasting 
  • Create enterprise-wide reporting across multiple systems 
  • Support future AI and advanced analytics initiatives 

As organizations continue to modernize their technology ecosystems, the ability to connect operational data with enterprise analytics is becoming increasingly important. 

What Workday Data Cloud and the Snowflake Partnership Mean for Workday Customers 

Workday continues to expand its vision for a connected data ecosystem through Workday Data Cloud and strategic partnerships that improve access to HR, finance, and operational data for analytics and AI use cases. 

At the same time, Workday and Snowflake have also announced deeper collaboration focused on secure data sharing, enterprise analytics, and AI innovation. 

As these capabilities evolve, higher education and government leaders should pay attention to several key trends: 

  • More secure access to Workday data for analytics and reporting 
  • Reduced reliance on manual exports and disconnected reporting processes 
  • Stronger integration between Workday and broader enterprise data ecosystems 
  • Greater support for advanced analytics, machine learning, and AI initiatives 
  • Increased emphasis on data governance, security, and compliance 

These developments create new opportunities for organizations to generate insights from trusted data while maintaining appropriate governance controls. 

Extending the Value of Workday with Snowflake 

Many organizations are discovering that ERP modernization and data modernization deliver the greatest value when pursued together. 

At Xavier University, Workday implementation and data transformation initiatives were undertaken concurrently. Workday served as the operational system of record while Snowflake unified data from multiple institutional systems into a single analytics platform, helping provide trusted insights and support informed decision-making. 

This approach reflects a broader shift occurring across higher education and government. Rather than relying solely on operational reporting, organizations are building connected data environments that provide a more complete view of students, employees, finances, and organizational performance. 

Key Questions to Ask at Workday Rising 2026 

As you plan your agenda, consider exploring questions such as: 

  • How does our analytics strategy extend beyond standard Workday reporting? 
  • What role should Snowflake play in our long-term data architecture? 
  • How can we better connect Workday data with other enterprise systems? 
  • What data capabilities are needed to support future AI initiatives? 
  • How can we improve access to insights while maintaining governance and security? 

These conversations can help organizations move beyond ERP modernization and develop a more comprehensive data and analytics strategy. 

The Future of Workday Analytics, AI, and Data Strategy 

Workday Rising 2026 will feature discussions about AI, analytics, and the future of work. For higher education and government leaders, one of the most important themes to watch will be how organizations are combining Workday and Snowflake to support better reporting, stronger decision-making, and long-term AI readiness. 

Organizations that establish a connected and well-governed data strategy today will be better positioned to realize the full value of their Workday investment tomorrow. 

As a certified Workday partner and Snowflake Elite Partner, Avaap helps institutions and agencies bridge the gap between operational systems and enterprise analytics by combining Workday expertise with modern data, analytics, and AI capabilities built on Snowflake. 

Heading to Workday Rising 2026? Schedule time to connect with the Avaap team and discuss your Workday, data, analytics, and AI priorities. 

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As higher education leaders prepare to gather at EDUCAUSE 2026, colleges and universities are navigating a rapidly evolving technology landscape shaped by AI, advanced analytics, student success initiatives, and digital transformation efforts. Institutions are balancing enrollment pressures, budget constraints, growing expectations for personalized student experiences, and increasing demands for data-driven decision-making. At the same time, new innovations in AI, analytics, and cloud technology are creating opportunities to improve outcomes while doing more with existing resources. 

The annual EDUCAUSE conference has long served as a barometer for the future of higher education technology, and this year is expected to be no different. Based on conversations across the industry and the priorities we’re hearing from institutions, these are five higher education technology trends Avaap will be watching closely at EDUCAUSE 2026. 

1. AI in Higher Education Moves from Experimentation to Institutional Strategy 

The conversation around AI in higher education has matured significantly. While many institutions spent the past few years testing generative AI tools and developing initial policies, attention is now shifting toward enterprise-wide adoption strategies. 

Rather than asking whether AI should be used, higher education leaders are increasingly focused on questions such as: 

  • How can AI improve the student experience? 
  • Where can AI reduce administrative burden? 
  • What governance structures are needed to ensure responsible use? 
  • How can institutions build trust among faculty, staff, and students? 

We are seeing growing interest in how AI can help institutions address workforce challenges, improve student support, strengthen decision-making, and streamline administrative operations.  The institutions making the most progress aren’t necessarily the ones deploying the most AI tools. They are the ones aligning AI initiatives with strategic goals, governance frameworks, and measurable outcomes. 

At EDUCAUSE 2026, we expect many conversations to focus on practical AI implementation, governance, and value realization rather than experimentation alone. 

2. Student Success Technology and Predictive Analytics Become More Proactive 

Student success remains central to institutional strategy, but the technologies supporting learners are becoming increasingly sophisticated. We also know that as staffing constraints continue across higher education, institutions are also looking for ways to scale student support without sacrificing personalization.  Colleges and universities are moving beyond retrospective reporting and toward predictive models that help identify potential risks before they impact retention and completion. By combining academic, enrollment, financial, and engagement data, institutions can gain earlier insight into student needs and intervene more effectively. 

Technology leaders are increasingly partnering with enrollment management, academic affairs, and student services teams to answer questions such as: 

  • Which students may need additional support? 
  • What factors influence persistence and completion? 
  • How can interventions be personalized at scale? 
  • What data can help improve student outcomes? 

The next generation of student success technology will be driven less by dashboards alone and more by actionable insights that help institutions make timely, informed decisions. Institutions that can connect data with proactive intervention strategies will be better positioned to improve student outcomes and advance institutional goals. 

3. Higher Education Data Strategy Powers Analytics and AI 

Many colleges and universities have invested heavily in analytics over the past decade. However, as AI and advanced reporting become more prevalent, institutions are discovering that success depends on the quality, accessibility, and governance of their underlying data. Higher education leaders increasingly recognize that fragmented data systems limit their ability to generate trusted insights, support AI initiatives, and make informed institutional decisions. As a result, there is growing emphasis on creating trusted data foundations that connect information across the institution. 

At EDUCAUSE 2026, we expect discussions around: 

  • Data governance and stewardship 
  • Enterprise data strategies 
  • Modern analytics platforms 
  • Institutional data literacy 
  • Cross-functional reporting and planning 

Strong data foundations are becoming a prerequisite for both effective analytics and responsible AI adoption. Institutions that invest in data quality, governance, and integration will be better positioned to support innovation, improve decision-making, and maximize the value of future technology investments. 

 4. Digital Transformation Focuses on Institutional Agility 

Higher education leaders continue to face significant workforce and budget pressures. Many institutions are being asked to deliver the same level of service, or more, with limited staffing and resources. 

This reality is accelerating interest in modernizing administrative operations across finance, human resources, payroll, grants management, procurement, and student services. 

Cloud platforms, automation tools, and streamlined business processes are helping institutions improve operational efficiency while creating better experiences for employees and students alike. 

However, modernization is no longer simply about replacing legacy systems. Institutions are increasingly focused on maximizing the value of their technology investments through: 

  • Process optimization 
  • Change management 
  • User adoption strategies 
  • Cross-department collaboration 
  • Continuous improvement 

The conversation is shifting from simply deploying technology to maximizing adoption, outcomes, and long-term value realization. Institutions are looking beyond implementation milestones and focusing on sustainable transformation that delivers measurable business and academic impact. 

5. Workforce Readiness, AI Literacy, and Digital Skills Remain Critical 

Technology alone cannot transform an institution. People remain the most important factor in successful digital transformation. 

As AI, analytics, and cloud technologies become more embedded in daily operations, colleges and universities are working to build new skills across their workforce. Technology leaders are recognizing that long-term success requires more than deploying new tools. It requires helping faculty, staff, and administrators understand how to use them effectively. 

Institutions are increasingly investing in: 

  • AI literacy programs 
  • Professional development 
  • Data literacy training 
  • Digital fluency across campus 

The goal is to create a culture where technology supports institutional priorities rather than becoming another layer of complexity. 

At EDUCAUSE 2026, we expect workforce readiness to remain a key topic as institutions explore how to prepare employees for an increasingly technology-enabled future and ensure their investments in technology translate into meaningful outcomes. 

Why These Higher Education Technology Trends Matter in 2026 

While each of these trends may seem distinct, they share a common theme: institutions are searching for ways to connect technology investments with measurable outcomes. Whether the focus is AI adoption, student success, data strategy, operational efficiency, or workforce development, higher education leaders are seeking practical approaches that support their mission while navigating ongoing financial, demographic, and operational pressures. 

The conversations at EDUCAUSE 2026 will likely highlight a common reality facing institutions: technology alone is not the goal. Success depends on aligning people, processes, data, and technology to advance institutional priorities and improve outcomes for students, faculty, and staff.  Many institutions are looking for ways to increase institutional capacity, improve decision-making, and better support students and employees without significantly increasing resources.   

As EDUCAUSE 2026 approaches, we look forward to engaging with higher education leaders about the opportunities and challenges shaping the future of higher education technology. 

Heading to EDUCAUSE 2026? We’d enjoy comparing notes on the challenges and opportunities higher education leaders are navigating today and discussing what’s next for our institutions.   

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Modernizing data and analytics is a priority for state and local agencies. As organizations look to reduce operational burden, improve security, and scale analytics, Tableau Cloud migration is becoming a key initiative. 

Many agencies and even entire states are evaluating how to migrate Server to Tableau Cloud while maintaining access to critical data and minimizing disruption. 

This guide provides a clear, practical overview of what the migration process looks like, along with the benefits and considerations for public sector teams. 

What is Tableau Cloud Migration and What Does it Involve? 

Tableau Cloud migration is the process of moving analytics assets from an on-premises Tableau Server environment to a fully managed cloud platform. 

This includes: 

  • Dashboards and workbooks 
  • Data sources and connections 
  • Users and permissions 
  • Governance and security configurations 

The goal is not just to move content, but to modernize how analytics are managed, accessed, and scaled across the organization. 

Why State and Local Agencies Are Moving to Tableau Cloud 

For public sector organizations, maintaining legacy analytics infrastructure can limit scalability and efficiency. 

Tableau Cloud offers a modern alternative by enabling agencies to: 

  • Scale analytics without infrastructure constraints 
  • Reduce the burden of system maintenance and upgrades 
  • Enable secure data sharing across departments 
  • Provide broader access to data for decision makers 

By shifting to a cloud-based analytics platform, agencies can focus less on managing systems and more on delivering insights that support mission outcomes. 

Common Challenges in Tableau Cloud Migration 

While the benefits are clear, migration from Tableau Server to Tableau Cloud requires careful planning. 

Common challenges include: 

  • Identifying and organizing existing dashboards, users, and data sources that NEED to be migrated 
  • Cleaning up Tableau content that is no longer necessary 
  • Configuring source data connections in the most effective and secure manner 
  • Managing the migration process without disrupting users 
  • Proper user communication messaging and timing 
  • Ensuring embedded external content is properly repointed after migration 
  • Validating that dashboards function properly after migration 

For larger or more complex environments, these steps can introduce risk if not approached in a structured way. 

A Practical Approach to Tableau Cloud Migration 

A successful Tableau Cloud migration strategy follows a structured process designed to reduce risk and maintain continuity. 

1. Assess your current environment 

Understand your existing analytics landscape, including users, dashboards, and data dependencies. Identify stale or unused content to ensure a clean start in your cloud environment. 

2. Plan the migration 

Develop a clear roadmap that defines timelines, testing strategy, communications, and cutover planning. 

3. Execute the migration 

Move users, content, and data sources in a controlled and structured way that minimizes disruption. 

4. Test and validate 

Ensure dashboards, permissions, and data connections to perform as expected. 

5. Support post go-live adoption 

Monitor performance and provide support to ensure a smooth transition for users. Maintain an open line of communication. 

This approach helps maintain access to critical data while minimizing disruption during the migration process. 

How to Minimize Risk During Cloud Migration 

For public sector teams, minimizing disruption and maintaining trust is critical. 

Best practices include: 

  • Communicating clearly with stakeholders throughout the process 
  • Establishing realistic timelines and expectations 
  • Testing thoroughly before go-live 
  • Planning for a controlled transition from server to cloud 

A structured and transparent approach ensures that users experience little to no disruption. 

Prepare for Your Tableau Cloud Migration 

If Tableau Cloud is on your roadmap, understanding what a successful migration looks like is an important next step. 

In our upcoming webinar, Avaap and Tableau will walk through: 

  • The end-to-end Tableau Cloud migration process (including timeline expectations) 
  • What to expect during each phase of migration 
  • Best practices to reduce risk and disruption 
  • Lessons learned from working with public sector organizations 
Register for the webinar 

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Snowflake’s fully elastic, consumption-based platform enables organizations to rapidly scale data, analytics, and AI. However, this same flexibility can lead to unexpected costs if governance is not intentionally designed into the platform. 

Many organizations encounter cost challenges not because Snowflake is inherently expensive, but because cost governance is introduced too late. Warehouses may run longer than necessary; workloads may compete for shared resources, and visibility into consumption may be limited across teams. As a result, organizations struggle to forecast spend, enforce budgets, and explain costs. 

Improving Snowflake cost predictability requires a deliberate approach that combines cost observability, governance, and architecture. 

Why Snowflake Costs Become Unpredictable 

Snowflake meters compute, storage, and cloud services independently, and compute resources can scale instantly. This design provides flexibility, but it also shifts responsibility for cost management to how the platform is used and structured. 

In Snowflake environments, cost variability is often driven by: 

  • Warehouses running longer than needed 
  • Shared environments supporting multiple workloads 
  • Limited visibility into usage patterns 
  • Lack of clear accountability across teams 
  • Consumption of AI and ML features without structured monitoring 

Without intentional governance, these patterns can make costs difficult to manage and predict over time. 

What Is Snowflake Cost Predictability? 

Snowflake cost predictability is the ability to make platform consumption transparent, controlled, and aligned to business needs. 

This requires: 

  • Cost observability: Visibility into usage across warehouses, roles, and workloads 
  • Governance by design: Controls embedded directly into the platform architecture 
  • Cost controls: Guardrails that prevent unintentional overspend 
  • Clear accountability: Mapping usage to teams, roles, and business functions 

Most importantly, these capabilities must be built into the platform from the start rather than added after costs become difficult to manage. 

Cost Observability: Measuring Before Managing 

The first step in governing Snowflake costs is visibility. Organizations cannot manage what they cannot see. 

Snowflake provides usage and telemetry data that can be used to track consumption, including: 

  • Credit usage by warehouse, role, and query 
  • Storage usage and growth 
  • Query runtime and activity patterns 
  • AI and advanced feature consumption 

By centralizing and structuring this data, organizations can: 

  • Identify which workloads drive costs 
  • Understand how usage changes over time 
  • Detect anomalies and unexpected spikes 
  • Provide stakeholders with clear cost insights 

Well-designed dashboards turn usage data into actionable insight and enable proactive cost management. 

Managing AI and Advanced Workload Costs 

AI and ML capabilities introduce additional cost considerations because they are event-driven and can be embedded directly within queries or applications. 

To improve cost predictability, organizations should: 

  • Separate AI usage from standard compute reporting 
  • Track usage by role, user, and application 
  • Monitor invocation frequency and cost behavior 
  • Identify repeated or unnecessary processing 
  • Apply alerts for unusual usage patterns 

Tagging AI-related resources with metadata such as cost center, environment, and application enables precise cost attribution and supports governance. 

Governance by Design: Building Predictability into the Platform 

The most effective Snowflake environments embed governance directly into the platform architecture. 

A governance-by-design approach includes: 

  • Standardized warehouse provisioning with consistent configurations 
  • Role-based access controls to prevent misuse of compute resources 
  • Mandatory tagging for cost tracking and accountability 
  • Validation during deployment to enforce standards before changes go live 

By embedding governance into how the platform is built and managed, organizations reduce manual oversight and improve consistency across environments. 

Common Snowflake Cost Anti-Patterns 

Certain patterns consistently lead to unpredictable costs in Snowflake environments: 

  • Using a single shared warehouse for multiple workloads 
  • Running oversized warehouses without usage-based sizing 
  • Lack of tagging and cost attribution 
  • Missing resource monitors or budget thresholds 
  • Embedding AI workloads into general analytics queries 
  • Relying on manual governance processes 

Addressing these patterns improves both cost control and overall platform stability. 

A More Effective Approach to Cost Control 

Improving Snowflake cost predictability requires a coordinated approach across: 

  • Observability to understand how the platform is used 
  • Native controls to enforce budgets and limits 
  • Governance embedded in architecture and processes 
  • Workload isolation to improve accountability and transparency 

When these elements are aligned, organizations can manage costs proactively rather than reacting after spend has already occurred. 

Take Control of Snowflake Costs 

Snowflake costs can be made predictable when governance, observability, and architecture work together. 

By treating cost as an architectural consideration, organizations can build a platform where consumption is transparent, controlled, and aligned to business priorities. 

Download the whitepaper to learn how to design for Snowflake cost predictability from day one. 

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As organizations consolidate data and machine learning (ML) workloads into Snowflake, a common question arises: 

“Should we rebuild our ML pipelines to take full advantage of Snowflake’s native capabilities?” 

On paper, yes, Snowflake now offers feature stores, experiment tracking, model registry, and improved observability. But in reality, most teams aren’t starting from scratch. They’re sitting on pipelines that already work. 

The challenge isn’t building something new.  It’s understanding and safely evolving what already exists. 

The Reality of Machine Learning Pipelines in Snowflake 

A familiar pattern often emerges: 

  • Pickled XGBoost models in stages  
  • Snowflake Tasks for orchestration  
  • Custom tables tracking metrics  

These systems often work but lack standardized practices for experiment tracking, model governance, and feature reuse. Teams face a tradeoff: 

1. Rebuild everything to align with native capabilities  

    or 

    2. Maintain legacy pipelines and accrue technical debt  

    Neither option is particularly appealing. 

    Why Translating ML Pipelines in Snowflake is the Hardest Part 

    Snowflake provides the building blocks. The challenge is translating existing pipelines into that ecosystem.

    Questions quickly emerge: 

    • Where does feature logic actually live, and how do we extract it into a feature store? 
    • How do we convert loosely tracked metrics into structured experiments?  
    • What becomes of staged, pickled models in a governed model registry?  
    • Which parts of the pipeline can change safely without breaking downstream systems? 

    Understanding the current system well enough to evolve it safely is often the real bottleneck. 

    Using Cortex Code to Modernize Snowflake ML Pipelines

    This is where Snowflake Cortex Code shines. It’s more than a coding assistant. 

    Rather than starting from scratch, Cortex Code can: 

    • Parse Snowpark logic to identify feature engineering  
    • Suggest mappings to a feature store  
    • Translate metric tables into experiment tracking  
    • Highlight redundant steps and dependencies  

    Crucially, it acts as a thought partner, helping teams reason through architecture, make incremental changes safely, and balance modern best practices with existing constraints. 

    Modernizing ML Pipelines Without Disrupting Production 

    Pipelines feed dashboards, models, and operational decisions. Breaking them has real consequences. 

    A safer approach is incremental: 

    1. Understand the pipeline end-to-end  
    1. Map components to native equivalents  
    1. Introduce modern ML patterns alongside legacy ones  
    1. Phase out old components once parity is validated  

    Hybrid architectures may run temporarily. It’s messy, but it’s safe. 

    Challenges in Modernizing ML Pipelines in Snowflake

    Modernization isn’t frictionless. Key limitations include: 

    • Deeper Snowflake coupling: Leveraging native ML features often means leaning heavily into containers and GPU-backed compute. This increases costs relative to warehouse-based processing.  
    • Risk of over-engineering: Cortex Code is powerful, but it can suggest real-time or complex patterns where batch is sufficient. Smaller pipelines may not need every best practice.  
    • Incremental boundaries: Some pipelines are too inconsistent or tightly coupled for a safe incremental approach; partial redesign may still be required.  

    A Repeatable Approach to Snowflake ML Pipeline Modernization 

    Across clients, we see the same need: 

    • Existing production model pipelines  
    • Desire to adopt Snowflake-native ML capabilities  
    • Hesitation to rebuild from scratch  
    • Need for architectural guidance  

    At Avaap, this has become a repeatable approach: use Cortex Code to accelerate understanding and mapping, modernize incrementally, and maintain compatibility. Teams modernize faster, without disrupting production, and gain clarity on what the system is actually doing. 

    What This Enables for ML Pipelines in Snowflake

    AI-assisted development is often framed as productivity gains. In ML modernization, the bigger impact may be architectural: 

    The bottleneck isn’t writing code, but understanding existing systems well enough to change them safely. 

    Tools like Cortex Code accelerate that understanding, bridging the gap between legacy pipelines and modern ML platforms. Not by replacing engineers, but by helping them make better, faster architectural decisions. 

    Key Takeaways for Modernizing ML Pipelines in Snowflake

    Snowflake’s ML ecosystem is powerful, but adopting it doesn’t require starting over. 

    The path forward is evolution, not replacement. It’s often the safer, faster approach. It comes with tradeoffs, friction, and careful decisions. But with the right approach and the right tooling, it’s manageable and repeatable.

    At Avaap, we help teams navigate these transitions, modernizing pipelines safely while leveraging Cortex Code to accelerate both understanding and implementation. 

    Because the teams that move fastest aren’t the ones rebuilding everything. 

    They’re the ones who know how to evolve what they already have. 

    Connect with Avaap’s Data, Analytics, and AI team to explore how you can modernize your Snowflake ML pipelines without disrupting what already works.

    Connect with us

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    Snowflake Summit 2026 brings together a wide range of sessions across AI, data engineering, and analytics, showcasing how the platform is being applied and extended in real-world environments. 

    For Avaap, this year’s Summit is about how organizations are putting Snowflake to work: applying AI in real workflows, enabling teams with the right tools, and doing it in a way that’s governed and sustainable. As a consulting partner focused on helping higher education, government, and nonprofit organizations turn data investments into measurable outcomes, these are the conversations we pay closest attention to.

    Below are a few sessions our team is especially looking forward to—both for what they demonstrate and the practical discussions they tend to spark.

    1. Public Sector AI in Action at Snowflake Summit: Ohio Department of Taxation

    Session: Ohio Tax: Call Center Analytics, Call Transcription and Agentic Workloads (AI230) 
    Speaker:     Budwhite Zhang, CDO and Deputy CIO, State of Ohio Department of Taxation 
    Date & Time:      Monday, June 1 | 4:00–4:45 PM PDT 

    This session offers a clear example of AI being applied within a regulated public sector environment. The Ohio Department of Taxation uses Snowflake Cortex to analyze call center transcripts, improve service consistency, and better understand taxpayer needs. 

    What stands out is how directly this connects AI capabilities to an operational workflow — turning unstructured conversations into structured, usable insight. It’s the type of implementation that often becomes a foundation for additional use cases, from service optimization to fraud detection. 

    Why we’re interested: 

    • A real-world application of AI in a government setting  
    • Practical use of unstructured data to drive outcomes  
    • A repeatable pattern that can scale across use cases  

    2. Extending Snowflake with AI Apps and Analytics Workflows

    Session: The Builder’s Guide to Sigma AI Apps: Workflows, Cortex Agents and AI Functions (BI214) 
    Speaker:     Senior Solution Engineer, Sigma 
    Date & Time:     Wednesday, June 3 | 1:00–2:30 PM PDT 

    One of the themes we expect to see across Summit is how Snowflake’s capabilities are extended through its partner ecosystem. This session is a strong example of that in practice. 

    Using Sigma alongside Snowflake Cortex, attendees will build a “Sales Pipeline Analyzer” that processes unstructured sales transcripts, extracts insights like deal health and risk, and writes structured outputs back into Snowflake. From there, workflows trigger notifications and dynamically update dashboards. 

    It’s a hands-on look at how partner technologies and Snowflake work together to move beyond analysis and into action — enabling AI applications that interact with data, systems, and users in real time. 

    Why we’re interested: 

    • Demonstrates how Snowflake is extended through partner tools like Sigma  
    • Shows how AI workflows move from insight to action  
    • Reflects the types of solutions organizations are building today, not just exploring  

    3. AI Governance and Trusted Data Foundations at Snowflake Summit

    Session: What’s New: Innovations in Data and AI Governance (WN210B)
    Speaker:     Snowflake 
    Date & Time:     Tuesday, June 2 | 3:30–4:15 PM PDT 

    As organizations expand their use of AI, governance becomes more tightly connected to day-to-day development and operations. This session focuses on how Snowflake is evolving its governance capabilities to support that shift. 

    Updates to Horizon Catalog, improvements to semantic alignment, and expanded interoperability through open standards all point toward a more unified approach to managing data and AI assets. Capabilities like automated classification, lineage, and data quality monitoring are increasingly central to how organizations maintain trust as they scale. 

    Why we’re interested: 

    • Connects governance directly to how AI is built and deployed  
    • Highlights tools that support consistency across complex environments  
    • Reinforces the importance of strong data foundations  

    Snowflake Summit Takeaways for Higher Education and Government Leaders

    Across these sessions, a consistent pattern emerges: organizations are moving from isolated AI experiments to integrated, operational solutions that are governed, scalable, and tied to real outcomes. 

    That’s where Avaap focuses. As a consulting partner for higher education and government organizations, we help teams apply these patterns in ways that align with their data, systems, and institutional priorities. If you’ll be attending Snowflake Summit, we invite you to schedule time to connect with the Avaap Data, Analytics, and AI team using our meeting request form and continue the conversation onsite.

    Schedule Time to Connect

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    Many organizations adopt Snowflake to modernize data, improve analytics, and prepare for AI. Their goals are fairly consistent: to achieve operational excellence by utilizing the organization’s data and generate positive results across its various functions.

    Across industries, leaders are asking how to move from data consolidation to clear, actionable insight. They are looking for analytics and AI initiatives that are governed, practical, and built to last. Achieving this depends not only on the platform itself, but on how data is modeled, integrated, governed, and aligned to real business priorities. 

    As a Snowflake Premier Partner, Avaap works with higher education, government, and nonprofit organizations to help close this gap. By combining deep Snowflake expertise with industry experience and proven delivery approaches, Avaap helps organizations transform Snowflake investments into measurable outcomes that support confident decision-making. Continue reading to learn how Avaap brings this approach to life through its Snowflake capabilities. 

    Avaap’s Core Snowflake Capabilities 

    Avaap’s Snowflake capabilities are designed to turn strategy into execution through secure data foundations, integration, analytics, and AI that organizations can trust and scale. 

    1. Secure and Governed Data Foundations 

    A strong Snowflake environment starts with the right architecture and governance. Avaap helps organizations design and implement Snowflake environments that support scalability, security, and long-term growth. 

    This includes: 

    • Snowflake environment setup and architecture aligned to organizational needs 
    • Role based access controls and data security best practices 
    • Data governance frameworks designed for regulated environments 
    • Logging, monitoring, and performance visibility 

    The result is a Snowflake foundation leaders can trust to support enterprise analytics and AI initiatives. 

    2. Workday and Snowflake Integration 

    A key differentiator of Avaap’s Snowflake capabilities is our combined expertise in Workday and Snowflake. 

    As a trusted Workday Services Partner, Avaap helps organizations extend Workday data into Snowflake to support enterprise analytics, reporting, and AI use cases. This integration enables deeper insight across finance, HR, and student data. 

    This integration enables: 

    • Enterprise reporting and analytics on Workday data 
    • Predictive and trend analysis using Snowflake 
    • Governed access to operational data for broader stakeholders 
    • A foundation for AI driven insights built on trusted data 

    3. Analytics and AI Built for Practical Use 

    Interest in AI continues to grow, but success depends on strong data foundations and clear use cases. Avaap helps organizations apply analytics and AI on Snowflake in ways that are practical, responsible, and aligned to business needs. 

    Rather than starting with experimentation, Avaap focuses on: 

    • Analytics use cases that support decision making 
    • AI initiatives grounded in governed, high-quality data 
    • Patterns that support adoption, trust, and long-term value 

    This approach helps organizations move from AI experimentation to execution with confidence. 

    How These Capabilities Come to Life at Snowflake Summit 

    At Snowflake Summit, data leaders come together to share how they are moving from data strategy to execution. Many of the same challenges and priorities discussed throughout this post are the focus of conversations happening across sessions, the expo floor, and partner meetings. 

    At Snowflake Summit 2026, Avaap is connecting with organizations to discuss: 

    • Building secure, governed Snowflake foundations that leaders trust 
    • Extending operational systems like Workday into enterprise analytics 
    • Applying analytics and AI in ways that support real decision making 

    For teams attending Snowflake Summit, these discussions highlight how Snowflake capabilities translate into practical outcomes across higher education and the public sector. 

    Unlock the Full Value of Snowflake with Avaap 

    Snowflake provides a powerful foundation for data and AI. Avaap helps organizations unlock their full potential through deep Snowflake expertise, industry experience, and proven delivery. 

    If you’re attending Snowflake Summit, we invite you to connect with Avaap onsite to continue the conversation.  

    Not attending Snowflake Summit? Avaap works with organizations year‑round to help transform Snowflake investments into measurable results. 

    Connect with us

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    Workday is a powerful system of record for people, money, and planning. But as organizations mature in their Workday journey, a common question emerges: how do you move beyond operational reporting to enterprise-wide insight without compromising performance, security, or governance? 

    For many Workday customers, the answer is Snowflake. 

    Snowflake serves as the analytics and AI layer downstream of Workday, allowing Workday data to scale beyond transactional reporting and become enterprise data.

    This architecture preserves Workday’s role as the system of record while enabling the depth, history, and flexibility required for enterprise analytics and AI readiness. 

    Together, Workday and Snowflake create a modern data foundation, one that supports executive decision-making without placing additional strain on operational systems. 

    How Snowflake Extends Workday Analytics Beyond Operational Reporting 

    Workday provides a unified operational foundation across HR, Finance, and Planning. Once that foundation is in place, organizations begin looking for broader visibility—insight that spans domains, extends across years of data, and supports strategic planning. 

    Snowflake is where that expansion happens. 

    By extending Workday data into Snowflake, organizations unlock enterprise-scale analytics while maintaining performance and governance. Workday continues to power day-to-day operations. Snowflake becomes the platform for insight, intelligence, and long-term analysis. 

    From Workday System of Record to Enterprise Analytics Platform 

    Workday and Snowflake are purpose-built for different, but complementary, roles. Workday excels at operational execution and transactional reporting. Snowflake is designed for analytics at scale, cross-domain insight, and advanced workloads. Together, they enable a clear separation of responsibilities that protect core systems while expanding analytic capability. 

    For Workday customers, this approach supports: 

    • Enterprise-wide analytics without impacting Workday performance 
    • Cross-domain insight by combining Workday data with other enterprise sources 
    • A governed foundation for advanced analytics and AI initiatives 

    The result is a scalable, future-ready architecture without disruption. 

    Enabling Enterprise and Executive Analytics with Workday and Snowflake 

    Using Workday and Snowflake together allows organizations to answer strategic questions that operational reporting alone cannot support. 

    With Snowflake downstream of Workday, leaders gain access to historical, longitudinal, and cross-functional insights that support planning, performance management, and enterprise decision-making—while Workday remains focused on what it does best. 

    Why Workday Organizations Are Adopting Snowflake for AI Readiness 

    Workday customers are moving now as several forces converge: 

    • Executives expect insights that cut across people, money, and operations 
    • AI initiatives require scale, openness, and historical depth 
    • Years of Workday data are compounding in strategic value 

    Snowflake meets these demands while allowing Workday to remain in the trusted system of record. 

    Download the Full Whitepaper 

    This blog provides a high-level view of why Snowflake has become the preferred analytics and AI architecture for Workday customers. 

    The full whitepaper goes deeper into: 

    • Architecture patterns and governance considerations 
    • Real-world executive use cases 
    Download the full whitepaper

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    In today’s data-driven world, organizations need more than fragmented insights—they need a complete, connected view of their data to make smarter, faster decisions. That’s where Avaap’s Panoramic AI, powered by Alteryx, comes in. 

    Panoramic AI consolidates data from multiple sources into a single, comprehensive platform, providing a 360-degree view of engagement and outcomes. Whether you serve citizens, students, or customers, Panoramic AI helps you transform raw information into actionable insights that drive impact. 

    Key Benefits of Avaap’s Panoramic AI 

    Organizations often struggle with siloed systems and inconsistent data. Panoramic AI solves that challenge by delivering: 

    • Fuel AI with Trusted Data 
      Provide accurate, comprehensive data for better insights and predictions. 
    • One-Stop Solution 
      Access advanced analytics and AI with continuously refreshed data from multiple systems. 
    • Cross-System Linkage 
      Connect disparate datasets—even without common keys—for unified analysis and AI with a single query. 
    • Rapid Insights 
      Quickly answer critical questions and gain strategic insights to improve decision-making and service delivery. 

    Core Capabilities of Panoramic AI

    Panoramic AI is designed for ease of use, featuring an intuitive, low-code/no-code interface that enables even non-technical users to participate in solution development and analysis. 

    1. Data Management

    • Collect and consolidate disparate data 
    • Profile and cleanse data 
    • Blend and link data from multiple sources 
    • Transform and curate datasets 

    2. Analytics & AI: 

    • Feature engineering for predictive models with solution accelerators 
    • Predictive variable association analysis to identify strong predictors 
    • Accelerated predictive/AI model development 
    • Data visualization and reporting integrated with your preferred tools 

    Why Avaap Partners with Alteryx for Data Integration 

    Avaap partners with Alteryx to simplify data collection, cleansing, linkage, analytics, and AI at scale. Benefits include: 

    • Reduced complexity 
    • Faster delivery 
    • Enhanced collaboration 
    • Increased transparency and data literacy 
    • Flexibility and scalability 
    • Automation for efficiency 

    Who Can Benefit from Panoramic AI? 

    Panoramic AI is built for government, higher education, and non-public sector organizations: 

    1. Government (Citizen/Employee/Vendor/Provider 360): 

    • Detecting fraud, waste, and abuse 
    • Increasing cross-program enrollment 
    • Improving program effectiveness 
    • Enhance employee satisfaction and retention 

    2. Higher Education (Student/Employee 360): 

    • Optimizing enrollment strategies 
    • Improving student success and retention 
    • Boosting alumni engagement and donations 
    • Enhancing employee satisfaction and retention 

    Ready to Elevate Your Data Strategy? 

    Data is one of your organization’s most valuable assets—but only if you can harness it effectively. Avaap’s Panoramic AI, powered by Alteryx, helps you break down silos, unify information, and turn insights into impact. Whether you’re improving citizen services, driving student success, or enhancing customer experiences, Panoramic AI enables faster, smarter decisions with intuitive tools that empower collaboration across your team. 

    If you’re ready to elevate your data strategy and deliver measurable outcomes, Avaap is here to help.  

    Connect with Us