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Month: February 2026

Snap Analytics and Dataiku Partner to Accelerate the Adoption of Enterprise AI 

Posted on by Luka Jasionyte

London and Boston, 23rd February 2026 – Snap Analytics and Dataiku today announced a strategic partnership designed to help organisations turn artificial intelligence into a reliable, everyday business capability. The partnership brings together Dataiku’s enterprise AI platform with Snap’s delivery and operating model, giving companies a clear path from experimentation to production-grade AI. 

Across every industry, interest in generative AI has surged. Leaders want systems that can predict outcomes, automate decisions and support teams in real time. Yet most organisations remain stuck in pilots. Their data is fragmented. Their models are difficult to govern. Their analytics tools were built for reporting, not for running AI at scale. As a result, promising use cases fail to move into the core of the business. 

The Snap and Dataiku partnership is designed to close that gap. Dataiku provides a platform that allows organisations to build, deploy and govern AI models in one place. Snap provides the teams that connect data, design workflows, apply governance and ensure that AI continues to deliver value after it goes live. Together, they create a simple outcome: AI that works in the real world. 

“Everyone is talking about AI, but very few organisations are using it to run their business,” said David Rice, Chief Executive Officer of Snap Analytics. “Dataiku gives organisations a powerful platform to build and manage AI. Snap makes it operational. Together we help clients move from experimentation to AI that delivers real results.” 

As organisations adopt more advanced forms of AI, governance has become a growing concern. Boards and regulators expect transparency, traceability and control. At the same time, business leaders want faster decisions and better insight. The partnership addresses both. By combining Dataiku’s control layer for models with Snap’s delivery approach, organisations can scale AI without losing trust or oversight. 

“Companies want AI they can trust and scale,” said Simon P., Partner Manager at Dataiku. “Snap brings the delivery model that makes Dataiku stick. This partnership gives customers a practical way to move from building models to running them as part of their everyday operations.” 

The rise of generative AI has also exposed the limits of older analytics platforms. Tools that were built for manual workflows and batch reporting struggle to support the speed and complexity of modern AI. Many organisations are now looking to move away from fragile, legacy approaches and towards platforms that can manage data, models, and decisions together. 

“Gen AI has raised expectations across the business,” said Calvin Fuss, Head of AI Practice at Snap Analytics. “Leaders want systems that can think, predict, and act. Dataiku provides the control and orchestration layer for AI. Snap provides the people and execution. Together we give organisations something far more powerful than traditional analytics tools.” 

What sets the partnership apart is how AI is delivered. Snap embeds AI engineers and data specialists inside client teams, working alongside the business to build, deploy and maintain AI in production. This approach turns Dataiku from a software platform into a lasting enterprise capability, ensuring that AI continues to evolve as business needs change. 

Snap Analytics and Dataiku believe this model represents the future of enterprise AI. Not as disconnected tools or short-lived pilots, but as a governed, embedded capability that supports everyday decision making and long-term growth. 

How AI Is Rewriting the Enterprise Data Playbook

Posted on by Luka Jasionyte

Luka Jasionyte, in the marketing team at Snap Analytics, catches up with Tom Bruce, Group Managing Director & Co-founder, to get the lowdown on his journey through data and analytics, and to share insights on what he thinks are the most important trends, AI initiatives and challenges facing enterprise businesses today.  

What inspired you to get into data and analytics, Tom? 

My love for sports statistics and data, along with their transformative impact on sport itself, inspired me. Books like Moneyball showed me how data and analytics can help organisations, making me realise the considerable influence I could have on companies with a relatively small amount of resource. 

What is the most exciting trend right now? 

It’s the obvious answer but clearly AI is already transformational and seeing the applications and more importantly the evolutions that will happen within this space is going to be exciting. With the rate of development and change increasing the impact on productivity, the way we work and the benefits this brings to business makes it a thrilling development to be a part of. We’ve been working on some exciting and transformational AI initiatives with clients, and everyone in this space shares the buzz about the potential and opportunities available now and into the future. 

What are some common data-related challenges that large enterprises typically face? 

I’ve been seeing two big challenges that large enterprises face when it comes to data, and these aren’t exactly new issues, but they’re becoming even more crucial now with the push towards AI-based solutions. 

First off, there’s data quality and governance. It’s all about having the right data, properly managed and owned in a way that’s governed and easily accessible to everyone who needs it. The challenge here is ensuring that the data is accurate, consistent, and reliable. Without good data quality and governance, any AI solution built on top of it is likely to be flawed or ineffective. 

Then there’s data modelling. This is about setting up the right foundations with an optimised and easily understandable data model. It’s key to making the most out of AI solutions. A solid foundation ensures that everything else works smoothly and efficiently, allowing AI to deliver its full potential. If the data model is too rigid or poorly designed, it can limit the effectiveness of AI and make it harder to adapt to new requirements or changes in the business environment. 

So, while these challenges aren’t new, they’re definitely more important now than ever before. 

What are the top client priorities for those looking to drive successful outcomes in data and analytics? 

We’ve often seen that a lot of investment has gone into cloud solutions, but there hasn’t always been control over the costs associated with these investments. Cost optimisation for cloud solutions is something that is increasingly coming up on our clients’ agendas. It’s becoming clear that while the cloud offers immense flexibility and scalability, without proper cost management, it can quickly become a financial burden. Clients are now more focused on finding ways to optimise their cloud spending to ensure they are getting the best value for their investment. 

Similarly, there have been numerous Gen AI exploration projects as organisations are just getting started with AI. We’ve noticed that there often isn’t a suitable business case for these initiatives, and most aren’t taken through to production. It’s crucial to properly identify the value of AI initiatives and ensure that more focus is given to clearly scoped AI projects with tangible business benefits. By doing so, organisations can avoid wasting resources on projects that don’t deliver real value and instead concentrate on initiatives that have a clear and measurable impact on their business.  

The key priorities for clients looking to drive successful outcomes in data and analytics are cost optimisation for cloud solutions and a focused approach to AI initiatives with well-defined business cases. 

Tom, why Snap Analytics? 

We’re experts in delivering the data foundations essential for successful AI projects. Our team is dedicated to ensuring our customers get the best service, reflected in our high retention rates and excellent feedback.  What sets us apart is our focus on our customers. We understand every business is unique, and we tailor our solutions to meet each client’s specific needs. Whether it’s optimising data quality, implementing robust governance frameworks, or developing cutting-edge AI models, we’ve got you covered. 

Our clients know they can rely on us to deliver results. We’ve built a reputation for being the team businesses turn to for new and innovative solutions. We’re constantly pushing the boundaries of what’s possible and are excited to help our clients achieve their goals. We’re here to provide the expertise, support, and innovation you need to thrive in today’s data-driven world. 

Data Doesn’t Have to Be Difficult

Posted on by Luka Jasionyte

Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Tim Andrews, Head of Data Visualisation, to dive into his passion for data, spotlight emerging trends, tackle enterprise challenges, and uncover what truly drives client success. 

What inspired you to get into data and analytics, Tim? 

Solving problems has always motivated me. From a young age, I felt comfortable working with data sets and searching for insights that could make a difference. Turning raw information into something meaningful and actionable has always fascinated me. Helping to move the needle on complex business challenges is still exciting today. What I enjoy most is being part of something innovative, especially when we achieve what others thought was impossible. 

What is the most exciting trend right now?   

There is still a way to go, but I think the combination of AI, cloud data warehouses and modern analytics applications will mean much quicker time to insights and a more conversational style to solving business problems. This shift is making analytics more accessible, allowing decision-makers to interact with data in ways that feel natural and intuitive. It is not just about speed, but about enabling better collaboration and reducing the complexity that often slows progress. As these technologies mature, they will transform how organisations approach strategy and execution, creating a stronger link between data and real business outcomes. 

What are some common data-related challenges that large enterprises typically face?   

Large enterprises often face several recurring challenges with data. Processes frequently generate poor-quality data, making it difficult to use for analytics later on. There is often a lack of clear data ownership within the business, which leads to gaps in accountability and governance. Many organisations also struggle with insufficient skills to solve complex data problems effectively. Business subject matter experts rarely have enough time to contribute to data and analytics projects, which slows progress. Finally, there is a common lack of understanding about the importance of strong foundational data and business context when tackling advanced analytics or AI initiatives. Without these basics, even the most advanced tools cannot deliver the expected results. 

What are the top client priorities for those looking to drive successful outcomes in data and analytics?   

Clients who want to achieve successful outcomes in data and analytics tend to focus on a few key priorities. First, identifying the right projects is essential, particularly those with clear business value and strong potential for user adoption. Second, working with partners who can deliver high-quality, robust solutions that meet the brief on both outcomes and cost is critical. Finally, making it easier for the business to solve complex problems and maximise the use of existing technology ensures that investments translate into measurable results. 

Why Snap Analytics? 

I tell my delivery team that our goal is simple: to become our clients’ favourite partner. We achieve this by being innovative, accountable, transparent and trustworthy in everything we do. I believe we are living up to that ambition and continue to strive for excellence every day.