Hakkoda’s State of Data 2025 report shows a digital marketplace on the brink of massive transformation, as early AI adopters have already begun to shift away from experimental AI implementations and the idea of future-proofing one’s technology stack toward more practical, outcome-driven AI solutions and an emphasis on agile tooling.
Dubbing this shift, appropriately, the “tool” phase of AI deployment, the report goes on to highlight other major changes enterprise leaders can expect to get ahead of in the coming months.
In this blog, we’ll highlight four of the State of Data’s top findings and predictions to help you get a better grip on what the data landscape of 2025 has in store for businesses like yours.

1. “Future-Proofing” is a Thing of the Past
If 2024 made one thing clear, it’s that the rapid pace of technological advancement and tumultuous overnight shifts in the AI arms race have made the idea of “future-proofing” your data strategy a lot less compelling. This doesn’t mean that there aren’t strategic choices that companies can make to get out ahead of tomorrow’s biggest disruptions, but it does mean those choices are unlikely to be “one and done” investments. In a word, it’s time to get “future-ready.”
This emerging readiness paradigm shifts focus toward creating adaptable, scalable systems capable of rolling with the punches and evolving with changing technologies and business needs. This agile approach allows organizations to steer clear of vendor lock-ins, quickly integrate emerging technologies, and adjust their strategies in response to real-time data and market trends. Platforms like the Snowflake AI Data Cloud, capable of unifying data from multiple sources to power enterprise-wide analytics, will play an important role in these adaptable future data strategies.
Data teams, meanwhile, should begin to move away from ambitious five-year data plans, introducing in their stead shorter roadmaps of 6-18 months strategy roadmaps, with check-points sprinkled along the way to make sure the data solutions of today still make sense by the time you can build them.
2. Apache Iceberg is Ushering a New Era of Data Flexibility
If “future-proofing” is bound for the data strategy graveyard, Apache Iceberg is stepping into the void it left as a pivotal technology for the flexible data architectures of tomorrow. It is able to do so thanks to its high-performance, vendor-agnostic storage capabilities, which allow it to benefit from the unparalleled compute power of platforms like Snowflake while introducing new possibilities for cross- or multi-cloud tech stacks. To sweeten the deal, Iceberg’s tiered pricing model also gives enterprises that much more control over their technology budgets.
Apache Iceberg’s growing adoption across industries underscores its role in enabling flexible, future-ready data architectures, aligning with the shift away from monolithic, platform-exclusive data management practices.

3. Data Modernization Has Reached its Fever Pitch
At the same time that cross- and multi-cloud strategies have started to become more viable, the frenzy for organizations to migrate their data to the cloud is nearing its conclusion—with 100% of top performing enterprises surveyed for the report indicating that they’d be using a cloud data platform by the beginning of this year.
These organizations have put themselves in a great position to explore flexible, vendor-agnostic data strategies in the months ahead.
Things appear less rosy for less data-mature organizations—indicating a sink-or-swim moment to be had in the year ahead if they hope to keep up with their more data-savvy counterparts.
26% of the lowest performing organizations project continuing to deploy tooling they themselves identify as “legacy” through this year and beyond.
This illustrates a rigidity in their data stacks that will only grow more costly as technological change continues to accelerate.

4. Smarter, More Accountable AI Use Cases Have Arrived
As data stacks are taking on a more limber, fast-footed approach, major shifts are also taking place in enterprise AI strategies. The era of experimental AI projects that has characterized the last five years is already giving way to something new—an era of practical applications emphasizing real-world outcomes and overall return on investment (ROI). This transformation necessitates the adoption of a “Results as a Service” (RaaS) mindset, which layers new accountability on AI agents and automated processes and tasks them with delivering specific, measurable outcomes. As AI becomes more embedded in operational processes, companies are finding that the key to success lies in aligning AI solutions with their existing strategic objectives, ensuring that every implementation is both purposeful and profitable.
Tomorrow’s Data Journey Starts Today
As we look toward 2025 and beyond, we can count on data and AI capabilities and best practices to continue evolving at an unprecedented pace.
The shift from future-proofing to future-readiness, the rise of flexible architectures like Apache Iceberg, and the move toward results-driven AI are setting the stage for exciting things to come, and a future state whose foremost pillars are agility and accountability.
Enterprises that embrace these changes now and align their data, AI, and business goals will be that much better prepared to turn the next corner with confidence.
Ready to start building your pragmatic, future-ready data strategy? Read the complete State of Data 2025 report or talk to one of our experts today.