Six Takeaways from Big Data London 2025

At this year’s Big Data London, industry leaders from companies like ThoughtSpot and Broadridge shared how data and AI strategies are evolving as organisations push for real business impact. The event felt timely with many firms moving beyond infrastructure and starting to focus on measurable outcomes.

Even so, the maturity gap in AI adoption remains clear. Governance, trust, and skilled delivery teams continue to separate early adopters from those still testing the waters.

Here are six takeaways that stood out to us and what they mean for businesses building the next generation of data and AI ecosystems.

1. From Data Platforms to AI Ecosystems

Many speakers echoed the same message: the industry has moved past building data infrastructure for its own sake. The focus now is on creating connected AI ecosystems that turn data pipelines into real business outcomes.

2. Agentic AI Is Becoming Practical (But We’re Still Early)

Talks from ThoughtSpot and showed that Agentic AI isn’t just theory anymore. Teams are embedding AI agents directly into analytics and workflows, automating insight discovery and decision-making.

That said, AI isn’t as far forward as everyone likes to think it is. Roger Burkhardt from Broadridge gave a great reminder of the gap between hype and execution. Real-world adoption still depends on governance, model trust, and the ability to turn prototypes into production systems.

3. Data Quality Remains the Biggest Blocker

Even with all the progress in tooling, poor data hygiene remains the main barrier to adoption. Several enterprise speakers shared that less than 40% of their data is “AI ready.” Investment is shifting from models back to foundations.

We see this daily. AI initiatives only succeed when the data underneath them is structured, complete, and trustworthy. Many of our clients are refocusing on the basics; strengthening lineage, validation, and enrichment before they scale.

4. The Nearshore Advantage Is Real

A common thread across vendors and enterprise leaders was the struggle to find skilled talent locally. We had multiple conversations around how nearshore data and AI teams are helping companies scale faster and more cost effectively, something we’re seeing firsthand at Remobi.

We've found this model works best when nearshore engineers are fully embedded within client teams, sharing delivery rhythms, tools, and priorities to move faster together.

5. Collaboration Between Data and Business Is Finally Happening

A positive shift this year is that data teams are embedding closer to commercial functions. Instead of dashboards, the conversation is now about decision velocity and value creation.

This gives data capability a permanent seat at the business table, breaking down silos and helping decision-makers act with confidence on trusted, timely information.

6. Most “AI products” are Really GPT in New Clothes

It was clear that many of the tools showcased are built directly on GPT or similar foundation models, simply packaged differently. There’s huge innovation in UX and integration layers, but it would be interesting to see a genuine new player enter the foundation model space.

The Remobi Way

If Big Data London 2025 highlighted anything, it’s that success in AI now depends on execution, integration, and trust.

Remobi helps organisations achieve that by combining deep engineering expertise with nearshore delivery, giving clients access to world-class data and AI talent aligned with their business goals.

To explore how nearshore delivery can accelerate your AI transformation, visit remobi.co or connect with Ethan Cohen on LinkedIn.