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Tag: Digital Transformation

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. 

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.

Why Digital Transformation MUST start with Data Strategy

Posted on by David Rice

Forbes estimates that a massive 84% of companies fail at digital transformation.  Yet change is inevitable, and those who fail to innovate will get left behind.  Blockbusters, Polaroid and more recently Toys R Us are examples of those that have fallen to the sword of change.  This is why 90% of corporate strategies will list ‘information’ as a critical enterprise asset by 2022 according to Gartner.

So how can we move the needle to deliver more effective Digital Transformation initiatives?  As Warren Buffet once said, “It’s good to learn from your mistakes. It’s better to learn from other people’s mistakes”.  Take the time at the start of the project to research the common pitfalls of Digital Transformation and be deliberate in your planning to avoid them.

Let’s be clear, digital transformation is complex and involves many stakeholders with different objectives, vast webs of complex legacy systems and code and of course requires a shift in mindset and culture.  But here are a couple of things I’ve observed over the years that hinder Digital Transformation initiatives:

Lack of clarity and alignment on goals

It’s absolutely vital to understand the goals of the initiative and how they help the organisation to achieve it’s strategic objectives.  If you are unable to draw a dotted line from what individuals are doing on the ground to the objectives set in the board room, then stop and ask ‘why?’.  Otherwise it’s like running a marathon but not knowing where the finish line is.

Technology first approach

Technology is only a small part of Digital Transformation and with the cloud, it is often the easiest part.  Digital transformation is about intelligent use of ‘information’ to drive better ‘outcomes’.  Yet we often forget about the ‘Information’ and the ‘outcomes’, and just look at the technology.  Maybe this is because it’s the easy bit, the one that the technical team that’s been hired is more familiar with.  Focus on outcomes and ‘Information’.  That’s where data strategy comes in!

Data Strategy

I hear so much talk in the corridors of large corporates about the latest AI (Artificial Intelligence) or ML (Machine Learning) initiative or how the organisation is using RPA (Robotic Process Automation) or IoT (Internet of Things).  But I hear far less about Data Governance, Data Quality, Data Catalogues and Data Literacy. 

Yet IoT, Automation, AI & ML are underpinned by Data!  Without it they simply do not scale or deliver effective results. 

I often hear the term ‘Data Rich and Information Poor’ but it’s often akin to being ‘Rich’ with counterfeit money (i.e. The ‘data’ isn’t real or of a high quality) so the organisation isn’t really Data Rich at all.  The data is poorly categorised, inconsistent across systems, has no clear ownership and is often undocumented.  This results in mistrust of the technology (whether it is shiny and new or 30 years old), silos of information and poor decisions.

So start your Digital Transformation journey with a data strategy and map out the ‘Information’ required to deliver effective outcomes and how you plan to govern it.  Consider our four pillars, and define where you are right now in terms of people, processes, technology and data.  Ensure you have a team or partners that can bring people on the journey, create clarity around the outcomes and deliver solutions that truly scale.