1. Everyone Is Talking About Data Quality
It came up in the keynotes, in dedicated sessions, in conversations over coffee. Whether it’s analytics, AI, or business operations, the message was clear: solutions are only as good as the data that feeds them.
That might sound obvious, but it’s a challenge every company still struggles with. Data is created everywhere, flows through multiple systems, and ends up being used in ways no one initially planned for. It’s messy, it’s alive, and it never really stops moving.
We joined two (fully packed) sessions specifically focused on strategies to improve data quality. They were insightful, but they also confirmed something we already believe: there are no “easy fixes.” And that’s logical. The value of most companies today lies in their people first, and their data second, and improving it isn’t a one-time job. It’s more like cleaning a house. You’re never completely done, and one person ignoring the mess can undo the work of many others.
Maybe the goal isn’t to live in a spotless house. Who could, really? Managing data quality is a bit like managing a home: you learn what’s worth tidying first, what keeps getting messy, and how to get everyone to do their part, both in fixing the issues and avoiding new ones. When you start doing that, the tools come in as practical helpers, not magical solutions. That’s exactly how we see DATAshaper: not as a silver bullet, but as a way to make managing and improving data quality easier, clearer, and more collaborative.
2. SQL Server Keeps Evolving and That’s a Good Thing
A big part of DATAshaper’s logic runs on SQL Server, so we were happy to see how much attention it still gets from Microsoft. The upcoming SQL Server 2025 release shows that the technology isn’t just alive. it’s evolving to fit the modern data landscape.
New features like native JSON support make it easier to handle flexible or semi-structured data, something that’s increasingly important in migrations where different systems use different formats. REST API integration will allow us to connect to external validation or enrichment services much more easily. And with fuzzy matching and native embeddings, we see real opportunities to improve one of the biggest data quality challenges out there: detecting and resolving duplicates.
In short, SQL Server is preparing for a world where structured and unstructured data live side by side. For us, that’s great news. It means our foundation remains strong while giving us room to innovate.
3. LLMs Are Here to Stay but They Need Solid Ground.
It’s impossible to talk about data these days without mentioning AI and large language models. There’s a lot of excitement, and rightly so. But what stood out in several sessions was a shared sense of realism: these models only make sense when built on top of reliable data, stable processes, and clear governance.
That’s something we fully agree with. At DATAshaper, we’re already exploring how to safely integrate LLMs into our platform. Not as replacements for human expertise, but as assistants that make migrations faster and smarter. It’s part of our VLAIO-funded R&D project on automating data migrations, and events like dataMinds Connect help us keep that work aligned with industry best practices.
Wrapping Up
All in all, dataMinds connect was a great reminder that even as tools and technologies evolve, the real challenge remains the same: helping businesses work better with their data. For us, that’s what DATAshaper has always been about: bridging the gap between people, systems, and the data that connects them.
Until next year, dataMinds.