All Posts

Most enterprises don’t have a data shortage. They have a movement shortage. Dashboards refresh. Reports circulate. Insights land in slide decks. Yet the same decisions get made the same way—at the same pace—often with the same debates. That gap between “we know” and “we do” is the last mile problem. It’s where analytics investments quietly lose their return. It's where you need organisational intelligence.

Data quality has moved from the back office to the boardroom. Clean, trusted data now drives speed, customer experience, risk reduction and AI readiness.

Most AI projects fail not because the models are wrong but because the data underneath them is not trusted. We explore why data integrity is the real foundation for every AI outcome.

Most organisations don’t have a data problem — they have an ownership problem. Your data won’t improve until your accountability model does. Discover why data quality fails without clear ownership and how treating data as a product creates trust, speed, and real business value.

Most AI initiatives never move past experiment stage. The problem isn’t the algorithms — it’s organisational readiness, data quality, and execution. Here's how to turn pilots into production-grade value.

Most data transformations fail not because of technology, but because organisations lack clarity, ownership, and measurable business outcomes. Every organisation seems to think they're on a “data transformation journey” but few can honestly say they know where they’re going or if they’re even moving. At Spark, we’ve sat in boardrooms where millions of euro have been spent on data platforms, new tools, and entire teams of analysts… yet the same problems persist: ✅ Inconsistent

As European organisations race to deploy AI, many face a common roadblock: how to access usable data without breaching privacy laws or exposing sensitive information. Synthetic data offers a breakthrough, enabling compliant, scalable AI across finance, healthcare, manufacturing, travel, and beyond.

Generative AI is more than just a buzzword—it’s a powerful tool for traditional enterprises looking to unlock value from their data. In this post, we explore how legacy organisations can take a data-first approach to AI, driving real impact through cleaner pipelines, smarter automation, and scalable governance.

Why building from scratch might be the most expensive, slowest, and riskiest move you can make in 2025. Walk into any large company...

To become truly AI-native, enterprises must start with a strong data strategy. Discover how data architecture, governance, and culture lay the foundation for scalable, trustworthy AI—turning isolated experiments into business-transforming solutions.

Hiring challenges in data roles are delaying digital transformation across many organisations.

You can’t build AI on shaky ground. Clean, connected, and well-governed data is the starting point for any meaningful AI journey....

How embedding expert talent can accelerate delivery, reduce risk, and create long-term value. Delivering meaningful results in data and...

Why scalable, well-structured data architecture is the foundation for successful AI adoption. From Data Chaos to Clarity: Building an...

Harnessing Financial Expertise to Drive Data-Driven Success. In today's rapidly evolving business landscape, data has become a...

Discover how to unlock sustainable value from PE investment by focusing on what’s often overlooked—your data. When Speed Collides with...

Learn how to monetise your data assets and turn untapped enterprise value into real business growth Why Data Monetisation Matters At...