Data Innovation Day underscores a simple truth: modern innovation is impossible without high‑quality, accessible, and trustworthy data. Federal economic statistics alone guide decisions on jobs, demand shifts, and national well‑being—yet they face mounting challenges, from declining survey response rates to rising expectations for real‑time, granular insights. As Brookings notes, the entire federal data lifecycle is under pressure, requiring new approaches to collection, storage, sharing, analysis, and dissemination to maintain accuracy, usability, and privacy.
At the same time, the scale of global data creation continues to accelerate. By 2008, CPUs were already processing over 9.57 zettabytes of data, a figure that has only grown as AI, automation, and digital services expand. Governments and enterprises are responding by investing in open data ecosystems, such as the U.S. government’s 546,351 publicly available datasets on Data.gov as of May 2026, to fuel research, transparency, and economic activity. This surge in data availability and complexity is exactly why Data Innovation Day exists: to highlight the breakthroughs, the challenges, and the people shaping a data‑driven future. And the insights below do just that.
Integrity and Availability of Data is Mission Critical
On Data Innovation Day, we remind ourselves of the power of data and the evolution it has driven in our highly digitalized world. Data is king and has become a real competitive advantage for leading enterprises across sectors. However, this also means that this data is increasingly targeted by bad actors who are looking to cause widespread disruption, gain financially, or steal critical information.
As organizations increase their use of AI, their data needs to be effectively protected and secure. The integrity and availability of data is becoming mission critical. Shockingly only 58% of IT professionals have adopted Zero Trust principles, which are the foundation for data protection. Implementing Zero Trust Data Resilience with immutable backup storage, least-privilege access, and strict network segmentation means backups will remain secure no matter what other defenses fail.
Anthony Cusimano, Solutions Director, Object First
Using AI Data From Questions to Inform Answers
What we are seeing right now is that the most valuable data is no longer just behavioral or transactional. It is conversational. At Monic AI Systems, we track the actual questions buyers are asking across platforms like ChatGPT, Claude, Perplexity, and Google AI, and how businesses show up inside those answers. Underneath that, we track patterns across prompts, mentions, and sources to understand what is actually driving inclusion. That gives us a very different type of signal. Not just what people click, but what they ask, what they are comparing, and ultimately what gets selected. For companies thinking about data strategy, this is an important shift. Traditional analytics tell you what happened after someone visited your site. But AI driven discovery is happening before that, inside the answer layer. The opportunity is to understand: – what questions are being asked – how your brand is being positioned – where you are being excluded entirely We have seen that even small changes in how a business is described and where those signals exist across the web can significantly impact whether it is included in AI generated recommendations. This type of data also has implications for how companies think about targeting and messaging, because it reflects intent in a much more direct way than traditional keyword or audience models. From a data perspective, this creates a new category of insight. One that connects directly to visibility, trust, and selection. For organizations participating in conversations like this, the value is not just exposure. It is becoming part of the data ecosystem that AI systems use to form answers. This is where long term visibility is now being shaped.
Monica Tomasso, AI Visibility Expert, Founder, Monic AI Systems
Ingestion as the Moat
The interesting AI products of 2026 aren’t the ones with the cleverest models. They’re the ones that have done the unglamorous work of ingesting messy enterprise data into a clean, queryable form, then exposing it where humans need it most … in meetings
Josh Torrey, founder, CoAgentor
Turning Fragmented Creative Signals Into Usable Workflows
One thing I see clearly is that data innovation is becoming less about simply collecting more data and more about turning fragmented creative signals into usable workflows. In AI products, the real value is not only the model itself. It is the data layer around the model: which prompts users try, where generations fail, which outputs people save, which formats creators prefer, and how teams move from idea to finished asset.
For creative AI platforms, data helps close the gap between experimentation and production. A user may start with a vague prompt, but the platform can learn which model, aspect ratio, style, or template is likely to produce a better result for that use case. That kind of product intelligence is what makes AI tools feel less like a slot machine and more like a reliable creative system.
The next phase of data innovation will be about responsible personalization. Users do not just want more automation. They want systems that understand their workflow, save time, reduce failed attempts, and still leave them in creative control. For startups, that means the companies that win will not be the ones with the most models listed on a page, but the ones that use data to make those models easier, faster, and more useful in real work.
Kruno Sulic, Founder, Cliprise
Modern Data Storage is The Backbone for High-volume Analytics
Data Quality, Integration, and Process Design
I’m Marco Kohns, co-founder of ERP Pilot. For Data Innovation Day, my view is that the most valuable data innovation in mid-market companies happens when operational data becomes usable for day-to-day decisions, not just reporting. In ERP, that means cleaner finance, inventory, and purchasing data feeding one system so teams can spot issues earlier and automate routine work with more confidence.
We see this especially in ERP selection and modernization. When companies choose systems that fit their processes and data structure, the payoff is tangible: the right ERP can reduce financial close time by 50-70% and lower admin overhead by 10-20%. On the AI side, even relatively basic ERP automation can cut manual accounts payable processing by 40-60%.
That’s why I think Data Innovation Day should focus less on flashy dashboards and more on data quality, integration, and process design. If the underlying enterprise data is fragmented, innovation stalls. If it’s structured well, companies can move faster on forecasting, automation, and cross-functional decision-making.
Marco Kohns, co-founder, ERP Pilot
Data Context Memory
My favorite topic! Most companies already have more data than they know what to do with.
What they’re missing is memory.
A customer explains the same issue three different times across three different channels. A sales team revisits decisions that were already made two weeks ago. A leadership team wonders why AI outputs feel inconsistent even though they invested heavily in the models.
Usually, the problem isn’t intelligence.
It’s missing context.
We’ve spent years treating conversations like temporary events instead of durable business assets. The call ends. The meeting ends. The chat scrolls away. Then everyone acts surprised when knowledge keeps resetting inside the organization.
That’s the part I think people are starting to realize.
The next phase of data innovation is less about collecting more information and more about preserving the right information long enough for it to compound.
Building the Strategy and Platforms to Turn Data into Action
Rigorous Data Testing
The biggest misconception in tech today is that better algorithms can beat dirty data. But they can’t. There have been many instances of even well-funded AI projects failing, despite a solid concept behind them. All that is needed to make a model fall apart is dirty or improperly labeled data, or no data whatsoever, a problem that can sneak up during any AI project.
At Unidata, we are working with a lot of different organizations whose attempts to introduce artificial intelligence have failed due to poor understanding of how data affects machine learning outcomes. The common pattern of failure that emerges here is that 80% of the budget is spent on model training, while only 20% is allocated to acquiring quality data for AI development. In reality, successful implementation requires quite the opposite.
On Data Innovation Day, I encourage you to discuss not what model is more powerful, but whether the data underpinning such models has undergone a rigorous testing process. I believe the future of artificial intelligence will be determined by companies that approach building data infrastructure just as other firms did before them with hardware.
Kirill Meshyk, Head of AI Data Collection, Unidata
The Next Decade Will Be Measured By Trust
For the last twenty years, data innovation has been measured by scale: bigger datasets, more sources, faster pipelines. But the next decade will be measured by trust. AI is now embedded in systems where mistakes carry real consequences: medical devices, autonomous vehicles, factory robotics, and defense systems. In those environments, you can’t rely on internet-scraped data, and you can’t rely on outputs hallucinated by another model. You need data with a known ground truth, grounded in physics, with every label explainable and provable. The industry is shifting from asking, ‘How much data do we have?’ to asking, ‘How do we know this data is correct?’ That shift may be the most important change happening in AI today and much of the industry still hasn’t caught up.
Brian Geisel, Founder & CEO, Symage
Turning Legacy Complexity Into AI-ready Infrastructure
Data Innovation Day highlights a critical truth: the most valuable data isn’t the newest, but the decades of transactional intelligence locked inside IBM Mainframes, AS/400s, and legacy ERPs. These systems hold the record of truth, yet remain inaccessible to modern AI due to proprietary protocols and complex COBOL and RPG logic.
At OpenLegacy, we bridge that gap by turning legacy complexity into AI-ready infrastructure. The platform enables LLMs to query live mainframe data via RAG APIs, grounding AI outputs in current transactional facts rather than stale training data. It automatically maps complex legacy structures into JSON and GraphQL for immediate ingestion into vector databases and analytics pipelines – and does it with the sub-second latency that real-time AI agents require.
AI is only as powerful as the data it can reach. Legacy enterprises have more of that data than anyone – they just haven’t been able to use it. Close the connectivity gap, and their history becomes their edge.
Ron Rabinowitz, CEO, OpenLegacy
A Shift in How Engineering Leaders Think About Portability
Especially with what’s been happening over the past few months, Data Innovation Day this year is a good moment to point out that the most important data innovation over the next few years will be a shift in how engineering leaders think about portability. Open source data tools used to come with architectural neutrality built in by default. With the recent wave of consolidation across streaming and database infrastructure, that’s no longer a safe assumption. Teams coming out ahead will treat neutrality as a deliberate design decision they revisit on every contract.
The cost of getting this wrong is what I’d call architectural debt. Engineering teams already think about technical debt as shortcuts in code that need to be revisited. Architectural debt accumulates a layer below that, in the integration points and convenience layers that quietly tie a stack to a single vendor over three to five years. This kind of debt rarely surfaces in code review or on a product roadmap, and it shows up the moment a team tries to migrate or renegotiate. By that point the cost to unwind those dependencies can be staggering. Engineering organizations that take architectural debt seriously now will preserve real options later.
Anil Inamdar, the Head of Data Services, NetApp Instaclustr
Making What Already Works More Visible, Resilient and Accessible
Data Innovation Day is an opportunity to rethink what progress really looks like. For years, enterprise modernization was framed around moving off core platforms, with roadmaps built on eventual replacement. In reality, most enterprises kept the systems that perform best at scale and built around them. Expansion, not abandonment, became the norm, bringing new complexity with it.
The challenge today isn’t access to data; it’s understanding data in context. Understanding system signals across logs, transactions and hybrid environments still depends on a shrinking pool of experts. That gap slows decision-making at a time when speed and clarity matter most, driving a shift in how organizations approach data innovation.
By embedding AI into operational systems, organizations can translate complex system behavior into clear, explainable insight. Teams can move from signals to answers faster without adding complexity or risking disruption.
Data innovation isn’t about changing everything. It’s about making what already works more visible, resilient, and accessible. The organizations that lead will be those that start with understanding and build from there.
Michael Curry, President of Data Modernization Business Unit, Rocket Software
Governance Keeping Pace With AI
Governance leaders are shifting their focus from ‘How do we slow this down?’ to ‘How do we move faster without losing control?’ Because when governance doesn’t keep pace with AI’s speed and scale, the risk is both operational and existential.
Businesses don’t just risk AI projects going live without proper guardrails—and the compliance and trust issues that follow. They also risk stalling innovation and losing ground to competitors. This reality is reshaping the mindset around AI governance, where speed is no longer a nice-to-have but a fundamental requirement.
Accessing, Governing, and Activating Unstructured Data
In 2026, unstructured data has emerged as the backbone of AI innovation, redefining how enterprises harness intelligence across their organizations. As AI continues to advance, the availability of high-quality structured data is reaching its limits, creating what many analysts call a “data ceiling”. However, with an estimated 80–90% of enterprise data existing in unstructured forms, from documents and emails to images, videos, and design files, the potential to unlock its value has never been greater. This vast, often underutilized data holds the key to deeper insights, smarter automation, and more contextually aware AI systems.
The next wave of AI progress will depend on how effectively organizations can access, govern, and activate their unstructured data. Doing so will require a strategic shift; one that prioritizes data quality, context, and security in equal measure. Unstructured data is the next iteration of data for AI. In 2026, having a comprehensive strategy for enterprise unstructured data is no longer considered “being a step ahead”, but vital for AI success.
Nick Burling, Chief Product Officer, Nasuni
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