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Tag: Artificial Intelligence

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. 

Your Enterprise Data Isn’t Aligned and It Shows 

Posted on by Luka Jasionyte

Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Raj Shah, Director of Strategic Accounts, to dive into his journey in data and analytics and explore the key challenges facing enterprise businesses today. Raj shares his insights on making sense of enterprise data, tackling data silos, the importance of strong governance, and how AI and high-quality data engineering are shaping the future. 

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

I was inspired by the opportunity to work across a variety of clients and industries, as data exists in every organisation. The business value that proper analytics can deliver feels almost magical, and although I didn’t enjoy programming, I found SQL surprisingly easy to write and understand.

What is the most exciting trend right now? 

AI, without a doubt. It has really focused everyone’s attention on getting their data right. It’s no longer something organisations can put off while continuing with manual or Excel-based analytics. AI depends on high-quality data, and ‘human-in-the-loop’ AI is the stepping stone for most organisations. Those that fail to embrace it risk being left behind.

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

Large enterprises often face several recurring data challenges that impact their ability to deliver reliable analytics. One major issue is the lack of a common data definition or language across the organisation. For example, a term like ‘margin’ can mean different things to different departments, leading to inconsistent reporting and decision-making. Another challenge is the reliance on flat files and manual data processing methods. These approaches are time-consuming, error-prone and make it difficult to scale analytics effectively. Excessive data transformation and manipulation often happens within front-end tools rather than being pushed down to the data warehouse. This creates inefficiencies, performance bottlenecks and governance risks.

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

For clients aiming to achieve successful outcomes in data and analytics, several priorities stand out. First, making data a true business asset is essential. This involves consolidating information from multiple systems into a clean, unified data warehouse that provides a single source of truth. Second, building data literacy across the organisation is critical. When teams understand and trust the data, they can make informed decisions and fully leverage analytics capabilities. Finally, reducing reliance on manual processes and Excel-based reporting is a key step towards scalability and efficiency. Moving to automated, integrated solutions not only saves time but also improves accuracy and enables advanced analytics. Together, these priorities create the foundation for delivering actionable insights and driving measurable business value.

Raj, why Snap Analytics? 

Clients choose Snap because we combine deep industry expertise with technical excellence. Our consultants have broad experience across the modern data stack, enabling us to design and deliver solutions that meet diverse business needs. We also bring specialised knowledge of complex systems such as SAP, supported by proven frameworks that reliably extract data and integrate it into modern cloud platforms. This ensures accuracy, scalability and speed. Snap focuses on delivering real business value. Every project is driven by outcomes that matter, whether that’s improving decision-making, reducing costs or accelerating innovation. Our approach uses lean teams, automation and reusable frameworks to achieve efficiency, standardisation and strong governance, giving clients confidence that their investment translates into measurable results.

AI for good – how data is helping to change the world

Posted on by David Rice

Artificial intelligence has been with us since the 1950s, but many people’s understanding of it still comes through sci-fi movies or shock newspaper headlines. Many worry that this technology is taking away our ability to think and act for ourselves, invading our privacy and taking our jobs. A recent poll by YouGov even found that 41 percent of the British public saw AI as a threat equivalent to nuclear weapons!

The reality is generally much more low key. Rather than creating new dystopias, AI has been most successful when applied to small, specific tasks, which are either too difficult or too time consuming for humans to carry out. As the potential of AI becomes more clear, ethical, or ‘Responsible AI’ has begun to be embraced by members of the tech community involved in solving some of humanity’s more intractable problems, potentially changing the world for good.

Agriculture

We’ve heard about the future of agriculture before thanks to the great GM revolution which promised to feed the planet with crops that were free from blight and disease. But it didn’t take long for GM foods to become as reviled as Victor Frankenstein’s final creation. Now AI is attempting to help farmers successfully produce more food. Sainsbury’s supermarket is testing sensors that will be able to provide data instantly to let farmers know the areas of their farm which most need water – perfect in drought prone countries or inaccessible locations. Meanwhile large scale farming is becoming more efficient thanks to AI powered drones. These drones are able to scan extremely large areas of farmland producing a large number of images, whilst also being able to use AI technology such as image recognition to be able to very quickly and accurately detect areas of farmland which are affected by disease in a way that even the most dedicated farmers could ever dream of.

Healthcare

AI’s role in assisting and sometimes replacing doctors is one of the more sensitive areas. In 2017 the UK’s data protection watchdog ruled that the NHS had illegally handed over the data of 1.6 million British patients to Google. The case showed that safeguards are needed whenever personal data is being used. However, when accessed responsibly, there is no denying that the results can be impressive. A two year partnership between Google’s DeepMind and London’s Moorfield hospital used data from thousands of retinal scans to train AI algorithms to detect signs of eye disease. It worked more quickly and efficiently than any human, cutting down the work done by a highly trained and expensive specialist from hours to just seconds. The next step is to use the same AI to analyse radiotherapy scans for cancer.

Endangered species

The appearance of AI driven drones in our skies bring with them fears of inescapable state surveillance. But studies by scientists tracking populations of endangered animals have found a new use for the technology – detecting and tracking species in the most remote locations. Using satellite data together with thermal and infrared imaging, drones are able to spot animals with between 43% and 96% more accuracy than human-made observation. At the moment, the limited range of drones means that success in tracking wide ranging species like polar bears is proving harder to achieve.

With the world now facing unprecedented challenges caused by climate change, epidemics and an ageing population, the importance of AI’s role in tackling these problems has never been greater. The battle to convince the public that it is in their best interest, however, is only just beginning.

Why AI is a lot like teenage sex – and how you can get better at it

Posted on by David Rice

In 2013 Dan Ariely, professor of behavioural economics at Duke University, got the analytics world all aquiver when he stated: “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” His risqué comments appeared in the midst of a data revolution, the experience of which was less than satisfactory for many companies.

Six years on and we’re doing it better. However, AI is in danger of being in the same position as Big Data was six years ago. Some data strategists would have you believe that AI and its bedfellow machine learning are better than sex. But as with anything in the data revolution, don’t expect to achieve total fulfilment overnight.

Cost vs kudos

A report from the McKinsey Global Institute predicts that early adopters of this technology could grow the value of their business by 120%. It adds that those who fail to jump on board the AI gravy train could lose a fifth of their cash flow. No surprise that companies are throwing money at the problem – but not necessarily always for the right reasons.

Kudos is not enough of a reward for a business to pump considerable sums of money into an AI project. A survey by the US analytics company Figure Eight showed that the majority of companies were spending at the very least $50,000, rising to over $5 million for those serious about making it a central part of their business.

AI has now been around for two decades. Advances in tech plus heavy investment from the likes of Google mean that the cost of leveraging AI tools will continue to fall, levelling the playing field and allowing smaller businesses to utilise AI. If you’ve made it your business to amass a wealth of clean, properly managed data you are already well positioned to launch an effective AI project.

Let’s not get ahead of ourselves

With any data project, there are things you need to think about before you get started. If you’re looking to get started with AI specifically, first consider whether the problem you would like to address is best served by the technology. Don’t expect AI to act as a sort of panacea; you need to be deploying it in the right way and for the right reasons. If you’re unsure, talk to an expert first (yes, we can help with that) to assess what sort of data analysis would be best for your particular problem.

Maybe there is an area of your business you are certain would benefit from an AI solution – if only you could convince the CFO to invest. If they’re keeping a tight hold of the purse strings ask yourself: does this align with the greater corporate strategy? If no, you’d probably be better focusing your efforts somewhere that does.

Finally, when you have identified the right AI project and hired yourself a crack analytics specialist (hello), don’t assume that the thing will just run itself. AI is smart but it still needs help. That means putting together the right team – and not just a couple of people borrowed from the IT department. Successful AI needs buy-in from people who understand the business need and who are working with the numbers on a daily basis.

Get it right and you’ll transform your AI experience from a meaningless one night stand to a satisfying relationship that grows into something really special.