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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. 

Download Insights 

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Higher education institutions are under increasing pressure to improve student outcomes while navigating complex data environments. 

Many assume they must complete their Workday implementation before addressing data modernization, analytics, or student retention initiatives. 

Xavier University is proving otherwise. 

By modernizing its data environment in parallel with its Workday rollout, Xavier accelerated its student success strategy and established a scalable foundation for long-term impact. 

The Challenge: Limited Visibility Slowed Proactive Student Support 

Like many institutions, Xavier University was committed to improving retention and graduation outcomes but faced challenges due to fragmented data systems. 

Before this initiative: 

  • Student data lived across multiple systems and reports 
  • Advisors lacked visibility into academic, financial, and engagement indicators 
  • Faculty and staff had to piece together a student’s full experience 
  • Leadership lacked a unified, real-time view of persistence trends 
  • Evaluating outreach effectiveness required manual analysis 

As a result, student success teams relied heavily on manual reporting processes, limiting their ability to act quickly and proactively. 

A Strategic Start: Aligning Around Data-Driven Student Success 

Xavier partnered with Avaap’s Data, Analytics, and AI team to define a shared vision for data-driven student success. 

Over two months, the team established a strong foundation through: 

  • A current-state assessment of data and reporting processes 
  • More than 40 stakeholder interviews across eight departments 
  • Documentation of institutional goals and priorities 
  • Alignment on a long-term analytics strategy 

This work ensured the solution would address immediate needs while supporting future innovation. 

The Approach: Modernizing Data in Parallel with Workday 

Rather than taking a sequential approach, Xavier advanced data modernization alongside its Workday implementation. 

This allowed the university to: 

  • Deliver insights earlier in the transformation process 
  • Avoid delays associated with waiting for a fully mature ERP 
  • Establish a strong data foundation while systems were evolving 

As Xavier CIO Sheri Thomas shared: 

“By modernizing our data in parallel with our Workday implementation, Xavier was able to accelerate our student success strategy. Using Snowflake to unify Workday data with other critical sources enabled our first student persistence analytics capabilities, delivering immediate value and empowering leaders to support students proactively.” 

The Solution: A Unified Platform for Student Retention Analytics 

Working together, Avaap helped Xavier implement a modern data environment designed to support proactive student success. 

Centralized Data Platform 

  • A Student 360 data model brings together data from across the institution 
  • Expanding Workday integrations strengthen the connection between systems and analytics 

Automated Data Processing 

  • Alteryx automates data preparation 
  • Manual reporting processes are significantly reduced 

Interactive Analytics and Dashboards 

  • Tableau dashboards provide intuitive, interactive visualization 
  • Insights are accessible to both advisors and leadership 

Supporting Advisors with Timely, Actionable Insight 

The platform enables student success teams to evaluate patterns across key indicators: 

  • Academic performance 
  • Financial indicators 
  • Student engagement signals 
  • Enrollment and progression patterns 

With frequently updated data, advisors can: 

  • Identify students who may need additional support 
  • Search and filter students by ID, major, cohort, or indicators 
  • Understand enrollment patterns and term-to-term progress 

This reduces time spent gathering information and increases time spent supporting students. 

Enabling Strategic Decision-Making for Leadership 

In addition to supporting advisors, the platform provides leadership with a strategic view of student success. 

Leaders can: 

  • Monitor persistence and enrollment trends across cohorts 
  • Identify patterns across academic, financial, and engagement indicators 
  • Evaluate the effectiveness of student success initiatives 
  • Inform institutional planning and investment decisions 

The Results: Earlier Intervention and Stronger Outcomes 

Xavier now has a modern analytics platform that enables more proactive and informed decision-making. 

Key outcomes include: 

  • Earlier student support through improved visibility into student needs 
  • More effective advisor time with less effort spent on manual reporting 
  • Institution-wide visibility into persistence trends 
  • Reduced manual reporting through automation 
  • A foundation for future innovation across admissions, student success, and planning 

What’s Next: Expanding the Impact of Analytics 

The Undergraduate Student Persistence Analytics platform represents the first step in a broader vision for data-enabled decision-making. 

Future opportunities include: 

  • Evaluating the effectiveness of student support initiatives 
  • Expanding insights for admissions and enrollment teams 
  • Supporting academic departments with program-level analytics 
  • Continuing to integrate new data sources through Workday and Snowflake 

Together, these efforts are helping Xavier create a more connected, proactive student success ecosystem. 

What Higher Ed Leaders Can Learn 

Xavier’s experience reinforces a key takeaway: 

Data modernization and Workday transformation do not need to happen sequentially. With the right approach, they can progress together to deliver faster, more meaningful results. 

Institutions can begin building retention analytics capabilities even without fully mature data environments when they focus on collaboration, practical progress, and scalable architecture. 

Join the Webinar: See the Approach in Action 

Want to learn how to apply this approach at your institution? 

In the upcoming webinar, Scaling Student Success: Advancing Snowflake and Workday in Parallel, you’ll hear directly from: 

  • Sheri Thomas, AVP and Chief Information Officer, Xavier University 
  • Kimberly Moore, VP of Student Affairs and Chief Success Officer, Xavier University 
  • Annaleah Morrow, Ph.D., VP of Customer Success, Avaap 

You’ll walk away with: 

  • Practical steps to advance student retention analytics during an active Workday transformation 
  • Guidance on structuring a collaborative data initiative 
  • Approaches for navigating data quality challenges 
  • Insights into how cloud platforms like Snowflake support scalable data foundations 

Save your spot to explore how Xavier is advancing student success with modern data. 

Register for the Webinar

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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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    As a Workday implementation partner, Avaap consultants spend their time helping organizations modernize systems and guide people through transformation. Just as importantly, our Transformation Solutions Practice, including Organizational Change Management (OCM), is central to how we help clients realize value from those investments.  

    Recently, Avaap had the opportunity to turn that expertise inward completing an internal Workday implementation. Led by our own consultants, this initiative allowed us to: 

    • Apply our expertise within Avaap, reflecting on what it truly means to lead change while simultaneously experiencing it ourselves.   
    • Intentionally design the change experience around the needs, perspectives, and day-to-day realities of our employees. 
    • Enable consultants to experience the new system through an end-user lens, deepening their understanding of the transformations we deliver for our clients. 

    In this blog, we reflect on our implementation through two distinct lenses:  

    1. The end-user’s perspective: Experiencing the transformation solely as a user 
    1. The lead’s perspective: Driving change on behalf of the organization 

    Together, these perspectives reveal lessons learned and insights gained from living the same change we so often help our clients navigate. 

    People at the Center of Our Internal Workday Implementation 

    One of the greatest strengths of Avaap’s internal Workday implementation was the intentional focus on the employee experience, recognizing our people as the end users of the new system. Throughout the implementation, we prioritized transparency, engagement, and feedback to ensure employees felt informed, supported, and heard.  

    This approach came to life in several ways: 

    • A strong change champion network established rhythm and structure through monthly meetings, clear and consistent Change Guides, and trusted peer-to-peer communication. 
    • Employees valued receiving updates from colleagues they trusted, reinforcing engagement and creating a powerful two-way feedback loop.  
    • Visible and active executive sponsorship reinforced alignment, accountability, and commitment to the change. 
    • Departmental meetings were intentionally leveraged to share updates, communicate key actions, and reinforce critical milestones and deadlines.  

    During go-live, hyper care made a meaningful and visible difference. Office hours, a dedicated Teams support channel, rapid issue resolution, and transparent public Q&A created real-time trust and confidence. Employees did not just receive answers — they learned from one another’s questions, strengthening system adoption and reinforcing confidence in the organization’s ability to support its people through change. 

    Lessons from a Compressed Workday Delivery Timeline 

    As change leads for this initiative, we experienced firsthand the realities of delivering against an ambitious timeline—challenges we often partner with our clients to navigate during a Workday transformation. 

    Here are a few of the lessons we look forward to applying in future client engagements: 

    • With a limited buffer for OCM activities, training development occurred in parallel with ongoing system configuration, extending preparation efforts, and requiring significant agility from the team.  
    • Delivery coincided with a demanding December client cycle and the holiday season, leaving little flexibility in employee schedules.  
    • To support adoption while honoring client commitments, sessions were recorded, materials were made readily accessible, and knowledge checks were implemented so employees could complete requirements between client engagements.  
    • This flexible, multi-modal approach enabled broad participation while preserving our commitment to client delivery and operational excellence.  

    As timelines compressed, change management often absorbed the shock. More importantly, the experience reinforced a critical insight we see repeatedly: while technology change is challenging, role clarity is often the harder—and more impactful—transformation. 

    Why the Internal Workday Experience Worked for Our Employees 

    From an end‑user perspective, the experience demonstrated how seamless change can feel when an organization intentionally supports its people, even amid the complexities and challenges faced by the project team. 

    “As a change management consultant, I understand the strategies that enable adoption, but experiencing a Workday implementation as an employee made the effort required feel far more tangible.” – Avaap Change Management Consultant 

    Several factors contributed to this success: 

    • Access to a centralized Learning Library. 
    • An active Teams channel. 
    • Clear FAQs made employees feel their needs were being anticipated rather than reacted to.  
    • A well‑designed matrix of communication channels, including a robust change champion network, ensured information surfaced when and where it was needed. 
    •  On-demand resources and flexible training sessions also allowed employees to engage in learning in meaningful ways, despite the demands of a busy time of year. 

    Leading Workday Change from the Inside 

    Leading change internally introduced a unique challenge: wearing two hats as both advisor and impacted stakeholder, while also supporting executive leaders as sponsors. Living this transition firsthand reinforced the responsibility that comes with advising on change 

    The experience underscored a lesson we share with clients every day: systems go live on a date, but clarity, adoption, and behavior change require sustained focus and intentional investment. 

    If you are preparing to kick off a Workday transformation, consider how Avaap can partner with you — bringing both extensive client experience and firsthand internal implementation lessons to help ensure a successful outcome. 

    Connect with us

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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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    Public-facing dashboards must meet strict accessibility requirements. That means the Web Content Accessibility Guidelines (WCAG) mapped remediation to alt text, color contrast, keyboard navigation, screen-reader order, and semantic structure. For government and public sector organizations, accessibility is not optional. It is a requirement tied directly to compliance, funding, and public trust. 

    The challenge is that each BI platform handles accessibility differently. Tableau, Power BI, and other BI tools each apply accessibility rules in their own way, creating inconsistency and risk across dashboard environments. 

    Why Manual Accessibility Remediation Falls Short 

    Manual BI dashboard accessibility remediation is often uneven, inconsistent, and slow. Without a standardized, platform-aware approach, accessibility fixes vary by developer, dashboard, and tool. 

    When a compliance complaint, audit finding, or federal funding review arises, agencies are frequently forced into rapid remediation. That response often happens without the internal expertise needed to act confidently and efficiently. 

    This gap quickly becomes more than a technical issue. It creates legal exposure, reputational damage, and remediation costs far greater than proactive investment. 

    As state and federal accessibility mandates tighten, organizations without a clear remediation strategy face increasing audit findings, legal action, and public scrutiny. 

    The Accessibility Skills Gap in BI and Analytics Teams 

    Accessibility expertise inside BI tools is rare. Most analytics teams are staffed with dashboard builders and developers, but very few accessibility specialists. 

    Teams may understand how to build dashboards that look clean and run fast, yet lack deep knowledge of how assistive technologies interpret visualizations, metadata, and interaction models. Without that expertise, even well-designed dashboards can fail accessibility reviews. 

    Avaap’s Solution: BI Dashboard Accessibility Remediation Services 

    Avaap’s BI dashboard accessibility remediation services pair platform-specific BI expertise with accessibility-aware dashboard engineering. 

    Our consultants assess accessibility gaps, remediate non-text content, address structural and visual barriers, and align metadata, so dashboards read clearly to assistive technology. The result is dashboards that meet WCAG expectations and work for every constituent. 

    Avaap works alongside stakeholders to define an actionable remediation approach. We can own delivery end-to-end or coach internal teams through remediation. Our engagement offers two reusable, long-term assets: 

    • An Accessibility Remediation Tracker 
    • A Reusable Accessibility Playbook 

    These deliverables ensure accessibility improvements extend beyond a single project. 

    Key Features of Avaap’s BI Accessibility Services

    • Accessibility Assessment and WCAG Gap Analysis: A WCAG-mapped review of dashboards and agency checklist responses is delivered as a prioritized Accessibility Remediation Tracker, giving teams a clear path forward. 

    • Accessibility-Aware Data and Dashboard Engineering: Standardized field names, metadata, and calculated fields ensure alt text, tooltips, and semantics are readable and logical for assistive technologies. 

    • Reusable Accessibility Playbook and Templates: A WCAG-compliant template library and an accessibility playbook your team can apply to every future dashboard build. 

    • Advisory and Enablement: Working sessions focused on accessible design patterns, color standards, metadata discipline, and keyboard-first navigation for dashboard builders and reviewers. 

    Measurable Outcomes for Public-Sector Analytics Teams 

    1. Lower Compliance Risk 

      WCAG 2.0 and 2.1 Level AA conformance reduces exposure to complaints, legal action, and audit findings. 
    1. Faster Path to Conformance 

      A platform-agnostic remediation approach compresses timelines, even across large dashboard estates. 
    1. Inclusive by Default Dashboards 

      Dashboards that work for all users, including those relying on assistive technology. 
    1. Standards That Scale 

      Playbooks and templates transform one remediation effort into a sustainable accessibility standard. 
    1. Platform Flexibility 

      Consistent delivery across Tableau, Power BI, and other BI platforms within your environment. 

    Why Choose Avaap for BI Dashboard Accessibility Remediation 

    Avaap’s Data, Analytics, and AI brings deep expertise across Tableau, Power BI, and modern BI platforms, with a strong track record supporting state and local government clients. 

    We understand how public-sector data environments are built, governed, and audited, and how to make analytics accessible without disrupting operational workflows. 

    With Avaap, accessibility moves beyond one-off compliance work. It becomes part of how the analytics function operates, ensuring public-facing dashboards remain compliant, usable, and inclusive over time. 

    To learn more about Avaap’s BI dashboard accessibility remediation services, get in touch with our Data, Analytics, & AI team. 

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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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    At Avaap, we know that organizational change efforts succeed when people—not just processes or technology—move forward with clarity, confidence, and commitment. Yet in large transformation programs, the gap between the client organization and the system implementor (SI) can easily become a barrier: misaligned expectations, unclear ownership, and competing priorities often slow momentum at the very moment when teams need to move in sync. 

    Our team has spent years operating at the intersection of these two worlds. Recently, Avaap was presented with a unique opportunity to partner as organizational change management (OCM) leadership on both the client side and the system implementor side, and that dual perspective ended up being one of our biggest strengths throughout the project. It allowed us to build trust quickly, anticipate friction before it surfaced, and create a unified experience for stakeholders who were navigating the change. 

    Why Dual-Perspective Organizational Change Management Leadership is Impactful 

    Working across both sides of the partnership gave us a unique advantage. We were able to better understand the realities of the client’s internal culture, decision-making rhythms, and organizational constraints – while simultaneously adhering to the SI’s delivery model, project cadence, and technical dependencies. This combination enabled us to efficiently and effectively translate, align, and accelerate. 

    1. A Dedicated Client-Side OCM Lead Creates Early Clarity and Sustained Momentum 

    One of the most impactful elements of this partnership model was having a client-side resource with deep OCM expertise embedded from the start. On this project, that role became a critical success factor. 

    Here are several ways our dedicated client-side OCM lead made a measurable difference throughout the transformation initiative: 

    • Anticipated challenges before they became blockers. Because the client-side OCM lead effectively functioned as an employee of the institution, they were trusted with candid details about the organizational culture and history, enabling them to identify risks early – whether related to stakeholder readiness, communication gaps, or process impacts.

    • Escalated issues with precision and credibility. Grounded in the organizational context and overarching change strategy, the client-side resource escalated issues appropriately and on time, and their perspective carried significant weight with project leadership due to their strong institutional relationships. This kept the core activities conducted by the OCM workstream moving without unnecessary delays. 

    • Drove productive collaboration across teams. Working sessions were more focused and efficient. The client-side lead could bridge perspectives, clarify expectations, and ensure that decisions were made with the right information and the right people in the room. 

    • Reduced ambiguity and accelerated decision-making. Their presence created alignment across stakeholders, helping teams move forward with confidence rather than revisiting decisions or reinterpreting requirements. 

    2. The SI-Side OCM Lead Drives Delivery and Change Adoption 

    While the client-side OCM lead helped us understand the intricacies of the internal culture and facilitated early alignment, the OCM role on the SI side was equally essential in ensuring seamless execution and adoption. The SI-side OCM lead functioned as a bridge between technical delivery and the client’s change management needs, translating system requirements into actionable communication and training strategies that resonated with end users. 

    • Ensured alignment between project milestones and change activities. By proactively mapping change activities to each phase of the system implementation, the SI-side OCM lead helped teams anticipate resource needs, manage expectations, and avoid last-minute surprises. 
    • Facilitated cross-functional collaboration. The SI OCM lead fostered collaboration across technical, functional, and client-facing teams, ensuring that messaging, training, and support were consistent and appropriately validated. 
    • Monitored and responded to adoption risks. Using data-driven insights and feedback loops, the SI OCM lead identified areas of resistance, tracked progress, and adjusted engagement strategies to maximize user adoption and minimize disruption. 
    • Provided expertise that streamlined training efforts. Leveraging prior experience, the SI OCM lead was able to accelerate training development—an area that is often challenging in Workday implementations without support from an experienced Workday resource.  
    • Supported continuous improvement. By capturing lessons learned and sharing best practices, the SI OCM lead contributed to ongoing optimization of both project outcomes and organizational readiness for future transformations. 

    Together, the client-side and SI-side OCM leads complemented each other’s strengths, resulting in a holistic approach that delivered both technical success and sustainable change adoption through dual‑perspective organizational change management leadership. 

    Establishing Trusted Partnerships and Unified Change Management 

    Respectful Partnership Strengthens Brand Credibility 

    Operating on both the client and SI sides requires a careful balance of advocacy, neutrality, and professionalism. Our approach centered on: 

    • Respecting each organization’s role and expertise. We reinforced shared accountability and mutual respect for each other’s strengths – the client as the expert in their culture and operations, and the SI as the expert in best practices for digital system transformation. 
    • Representing each brand with integrity. Our resource that was embedded with the client acted as an extension of their team—upholding their values, communication style, and expectations, while adhering to Avaap’s policies and procedures. 

    This balanced approach enhanced credibility for everyone involved. Stakeholders noticed a unified front, not competing agendas. 

    A Unified OCM Strategy That Works Across Both Organizations 

    Because we understand the dynamics of both sides, we were able to design and implement an OCM strategy that was: 

    • Practical for the client to execute (aligned with their culture, capacity, and leadership style) 
    • Compatible with the SI’s delivery model (integrated with milestones, testing cycles, and deployment timelines) 

    This alignment reduced rework, minimized confusion, and ensured that change activities supported—rather than lagged—the technical implementation. 

    The Result: A More Cohesive, Confident, and Change-Ready Organization 

    By bringing together the strengths of both the client and the system implementor, dual‑perspective organizational change management leadership helped create an environment where:  

    • Teams collaborated more effectively 
    • Decisions were made faster 
    • Training and readiness efforts stayed on track 
    • Stakeholders felt supported and informed 
    • The organization moved through change with clarity and purpose 

    This dual-perspective OCM model doesn’t just support transformation—it elevates it. 

    Looking for the right support to ensure your large-scale system transformation is a success?  Explore Avaap’s Organizational Change Management services to learn more about our capabilities.

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