What We Discovered at Data Innovation Summit 2026: AI Readiness, Migration & Modern Data Stacks

When we flew northbound to attend the Data Innovation Summit, DIS 2026, in Stockholm, we expected AI to dominate the conversation. And it did. But the most intriguing conversations were not about AI in isolation. Rather, they were about what needs to sit underneath AI: clean architecture, trusted metadata, governed pipelines, scalable modeling and data platforms that can evolve without creating more complexity.

The Data Innovation Summit returned to Stockholm for its 11th edition from May 6 to May 8 2026, with an event positioned around the rapidly changing world of data and AI. The event’s own framing focused on data, people, processes and technology, which matched what we heard on the show floor: organizations are no longer asking whether data and AI matter, they are asking how to make them operational, trusted and sustainable.

We were joined by our Finnish partner WiseDigi, represented by Ville Wasenius and Antti Kajala, alongside Matti Ristiluoma from WhereScape. Together, we spoke with hundreds of attendees across the event. Some were already familiar with WhereScape, some had heard of us through the Nordic data community and some were encountering us for the first time.

The overall theme was clear: modern data teams are trying to move faster … but they do not want to create another generation of fragile, undocumented, hard-to-change data infrastructure.

What Stood Out at DIS 2026?

The strongest pattern we saw was that AI, migration and modernization are now tightly connected. A few years ago, those topics often lived in separate conversations. One team discussed cloud migration. Another looked at data warehouse automation. Another experimented with AI. 

At DIS, those once disparate conversations had all started to converge.

Many attendees wanted to understand how to modernize their data platforms in a way that would support analytics today and AI tomorrow. That meant questions around Snowflake, Databricks, Microsoft Fabric, SQL Server, dbt, Informatica, Data Vault, metadata, governance and lineage all arrived in the same discussion.

That convergence matters. It tells us that organizations are moving beyond “which tool should we purchase?” and toward “how do we actually build a data environment that can evolve over time, with us?”

That is exactly where we believe data warehouse automation becomes most valuable: not simply in generating code faster, but in helping teams standardize, govern, document and evolve their data platforms with less manual rework.

Booth Conversations: More Focused Than Past Years

From a booth perspective, the event was productive. We scanned more than 300 people without duplicates, and the team estimated that additional attendees stopped by without being formally scanned. Wednesday and Thursday were the strongest days, while Friday was lighter, which may have been partly due to the event being extended to a third day this year.

One interesting observation from Matti and our partner WiseDigi was that the number of conversations may have been lower than previous years, but the quality was higher. In other words, fewer “brief passers-by” and more people with a real reason to stop, ask questions and explain what they were working on i.e. it was ‘quality over quantity’ for 2026.

It’s notable because DIS is a highly targeted data conference. The audience was largely made up of data practitioners, data leaders, architects, engineers and platform decision-makers. The team felt that conversations were more directly relevant to WhereScape than what we’ve seen at some broader events.

A rough estimate from the team was that around a quarter of booth visitors already knew of WhereScape to some degree. Some were existing customers, including organizations such as LKAB and Trafikverket. Others had heard of WhereScape through peers, partners or previous events.

That word-of-mouth awareness was highly encouraging for our team. In several cases, attendees came over because someone else had told them: “You should talk to WhereScape!”

The First Question Was Often: “What Exactly Does WhereScape Do?”

One of the most common questions was simple: what is WhereScape actually about?

That may sound basic but it is useful. And a good reality check, once you’re deep “in the weeds” with solving the details of so many customer data needs. In a crowded data stack, buyers often see many tools clustered together: ingestion tools, transformation frameworks, orchestration tools, data catalogs, Data Vault accelerators, cloud platforms, CI/CD tooling, governance software and AI assistants.

But … WhereScape does not fit neatly into only one of those categories.

That’s why the architecture-focused handouts we had on-site proved so useful at the booth. It gave our team a way to explain WhereScape visually: our products help teams design, build, deploy, document and operate data infrastructure through metadata-driven automation.

A simple way to think of it is like this …

• WhereScape 3D supports discovery, profiling, design and model-driven planning.
• WhereScape RED supports automated ELT, orchestration, workflow management and documentation.
• WhereScape Data Vault Express supports faster Data Vault 2.0 delivery through WhereScape 3D and RED patterns.
• WhereScape Enablement Packs help automation work across major source and target platforms.

That last point came up again and again. Our WhereScape Enablement Packs contain templates and configurations for WhereScape 3D, RED and Data Vault Express across many supported platforms, including source discovery and target-specific auto code generation. 

Trend 1: AI Is Everywhere, But the Bottleneck Is Still Data Readiness

AI was the dominant event theme. The official DIS positioning itself centered heavily on applied Data, Analytics and AI + the agenda included dedicated areas for machine learning, agents, generative AI, data engineering, DataOps, data platforms and architecture.

But at the booth level, our level … AI conversations were often practical, rather than abstract.

People were not only asking “how do we use AI?” They were asking questions more like …

• How can AI speed up modeling work?
• How can AI assist with the “brain work” of data architecture?
• How can AI help us understand sources faster than before?
• How do we prepare governed data for AI use cases?
• How do we reduce manual work without losing control?
• How do we stop AI from generating unreliable or unreviewed outputs?

That distinction matters. AI is not just another destination for data. It raises the standard for the data foundation underneath it.

Our view is that AI-ready data requires consistent models, lineage, documentation, validation, governance and repeatable delivery patterns. Without those, teams risk feeding AI systems with data they do not fully trust or understand.

That is why we think AI-readiness belongs close to the data warehouse and data platform conversation. WhereScape’s own AI-ready data approach focuses on validation, governance, profiling, lineage and metadata visibility, because AI models are only as useful as the data foundation behind them.

The biggest AI takeaway from DIS was this: the winners will not simply be the teams that add AI the fastest. They will actually be the teams that give AI the strongest governed data foundation to work from.

Trend 2: Migration Is No Longer a Single-Platform Conversation

Migration was one of the biggest topics at DIS.

But these were not simple “we are moving from X to Y” conversations. Attendees asked about many different migration paths, including cloud modernization, Databricks, SQL Server, Databricks and moving away from legacy integration tools such as Informatica.

What intrigued us was that there was no single dominant target platform this year. Instead, the Nordic data community appears to be working across a highly mixed platform landscape. Some organizations were interested in Snowflake. Some were interested in Databricks. Some were still heavily invested in SQL Server (on-prem). Some were exploring SQL modernization, without wanting to replace everything.

This reflects what we see more broadly: the modern data stack is not becoming simpler. It is becoming more distributed.

That is why platform flexibility matters. WhereScape supports multiple target platforms, including Snowflake, Databricks, Microsoft Fabric, SQL Server, Oracle and others, with automation helping teams move from design to deployment faster.

The broader lesson from DIS is that migration is not just about changing platforms. It is about reducing the cost of future change.

Trend 3: SQL Server Modernization Still Has Strong Demand

One of the clearest booth themes was continued interest in SQL Server modernization.

This is important because “modernization” is often treated as shorthand for “move to the cloud.” But many organizations still have deep investments in SQL Server. They may want better governance, faster delivery, improved documentation, reduced SSIS dependency or a more future-ready architecture without immediately abandoning what already works.

That was reflected in conversations at DIS. Some attendees were not asking how to leave SQL Server. Instead, they were asking how to improve it.

This aligns with our own experience. For many SQL Server teams, the pressure is not just platform age. It is accumulated manual work: hand-coded ETL, fragile stored procedures, complex job chains, limited documentation and hard-to-change logic.

WhereScape’s SQL Server automation story is centered on combining SQL Server’s established performance with data automation to deliver analytics faster and with less manual effort. 

The takeaway is not that every SQL Server estate needs to be replaced. The takeaway is that many SQL Server estates need to become more understandable, more automated and ultimately easier to evolve.

Trend 4: dbt Comparisons Are Becoming More Common

Another hot topic was dbt.

Attendees asked how WhereScape compares with dbt, how certain dbt workflows would be handled in WhereScape and whether WhereScape would replace or complement tools in the dbt ecosystem.

That is a useful question because dbt has become a major reference point for analytics engineering. It gives teams a code-based way to manage transformations, testing, documentation and modular SQL development. But many organizations are now comparing that with more end-to-end automation approaches.

Our answer depends on the customer’s environment, there is no “one size fits all” answer to be had.

It’s important to understand that WhereScape is not just a transformation framework. It supports design, modeling, code generation, orchestration, documentation, lineage, deployment and operational management. 

In real-world terms, the discussion is not always “dbt or WhereScape.” Sometimes the right answer is:

• Use dbt where code-first transformation workflows are already strong.
• Use WhereScape where teams need metadata-driven automation, modeling and governed delivery across the full data warehouse lifecycle.
• Use WhereScape to standardize patterns, accelerate refactoring and keep design, code + documentation connected.

The fact that this question came up repeatedly tells us that the market is becoming more mature. Buyers are no longer evaluating tools as isolated products. They are asking how the full lifecycle all fits together.

Trend 5: The Competitive Landscape Is Crowded

DIS also showed just how crowded the data automation and modern data stack market has become.

The team noted a strong competitor presence; this matters because customers are surrounded by competing narratives e.g.

• Ingest data faster.
• Transform data in code.
• Modernize into Snowflake.
• Automate on Databricks.
• Replace legacy ETL.
• Add AI to your modeling workflow.
• Govern everything through a catalog.
• Move toward Data Vault.
• Consolidate your stack.
• Keep your stack modular.

And so on. What all these different narratives mean is that the challenge for buyers is not a lack of options. In some ways,it is the opposite: too many tools, too many claims and too many partial answers.

That is why we know our message absolutely needs to stay practical. WhereScape is strongest when we explain the full lifecycle clearly: discover, model, automate, deploy, orchestrate, document and govern.

We do not need to claim that every customer should use only one tool; that’s not the reality of the market. Instead, we need to show where WhereScape removes the repetitive manual work that sits between these tools, platforms and methodologies.

Trend 6: Governance and Lineage Are Becoming AI Conversations

Governance and lineage were not always named directly in every discussion but they sat firmly underneath many of the questions.

When people talked about AI-readiness, they were really asking whether their data could be trusted. When they talked about migration, they were asking whether they could understand dependencies before making changes. When they talked about dbt, they were asking how to keep documentation, models and transformations aligned. When they talked about public sector environments, they were often thinking about compliance, traceability and accountability.

That is where data governance and lineage become part of delivery, not a separate afterthought. Our governance and lineage approach is designed around producing documentation, lineage, impact analysis and audit trails as teams design and build.

This is increasingly important because AI makes unclear data more dangerous. If a report is wrong, someone may challenge it. If an AI system produces a confident answer from poorly governed data, the error can scale quickly.

In that sense, AI does not reduce the need for governance. It makes governance more urgent.

What We Learned From the Nordic Data Market

We’ve operated in the Nordic data market for a long time and it is clear that it has a distinctive character. This was reflected in conversations at DIS, which were pragmatic, technical and often centered on real implementation challenges – rather than abstract hype.

We saw strong interest from public sector and enterprise organizations. We also saw that many attendees were working in hybrid environments, with some existing on-premises infrastructure, some cloud initiatives and some evaluation of newer platforms.

A few observations stood out:

• AI interest is high but teams want controlled, well thought-out use of AI rather than superficial add-ons.
• Migration conversations are active, but there is no single destination platform.
• SQL Server modernization remains highly relevant.
• dbt has become a common comparison point in data transformation discussions.
• Snowflake and Databricks continue to appear in serious modernization conversations.
• Documentation, lineage and governance are becoming more strategic because of AI-readiness.
• Partner-led conversations are especially valuable in Nordic markets where local trust matters.

WiseDigi’s presence helped here. As a highly-valued regional partner, their perspective made our booth conversations more grounded. They were able to connect WhereScape’s automation story to local market realities and real customer environments.

Our Core Takeaways From DIS 2026

Here are the main lessons we took from the event.

• AI readiness is now a data architecture conversation, not just an AI tooling conversation.
• Migration is becoming more complex because organizations are modernizing across mixed platform estates.
• SQL Server remains really important, especially for teams that want modernization without a full replacement project.
• dbt has become a common comparison point, so data automation vendors need to explain lifecycle coverage clearly.
• Governance, lineage and documentation are becoming more urgent as AI adoption increases.
• Buyers want practical demos and architecture patterns, not only high-level messaging.
• Partner expertise remains critical, especially when translating product capability into local market relevance.

Final Thoughts

DIS 2026 further reinforced something we already believed: the next phase of data modernization will not be won by speed alone.

Speed matters. Automation matters. AI matters. But if speed creates more undocumented pipelines, more fragmented tooling and more unclear ownership, it simply creates a faster version of the same old problem.

The organizations we spoke with in Stockholm are seeking out a better balance. They want to modernize, without losing control. They want to use AI, without sacrificing trust. They want to migrate, without rebuilding everything by hand. They want to compare tools honestly and understand where each one fits together.

That is where our team at WhereScape will continue to focus: helping data teams move faster while keeping metadata, models, documentation, lineage and deployment connected.

DIS was a useful reminder that the market is not short of tools. What data teams need now is a clearer, more automated path from architecture to trusted delivery.

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