Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Mark Todkill, Head of Delivery, to dive into his journey in data and analytics and explore the key challenges facing enterprise businesses today. Mark 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?
I’ve always been drawn to problem-solving, finding patterns, breaking down complex challenges, and using maths and logic to uncover solutions. That curiosity has been a constant thread throughout my life, though I never set out thinking data and analytics would become my career. But after graduating, I landed in a role that aligned perfectly with my strengths, and that’s where everything started to click.
Over time, my passion for data grew through hands-on experience. The field moves fast, with new problems to tackle and fresh innovations emerging constantly. What I love most is how it brings together two sides of problem-solving. On one hand, there is the technical challenge of making sense of vast amounts of enterprise data, structuring it, and ensuring it is usable. On the other, there is the business side, which focuses on turning that data into meaningful insights that drive smarter decisions.
It is a space that keeps me engaged, constantly learning, and always excited for the next challenge. That balance between technical precision and strategic thinking is what makes it such a compelling field to be in.
What is the most exciting trend right now?
One of the most transformative trends in data and analytics right now is the fusion of AI and high-quality data engineering. AI continues to revolutionise industries, from automation and predictive analytics to personalised customer experiences and real-time decision-making. However, its true power lies not just in the algorithms but in the enterprise data foundation it relies on.
As AI adoption accelerates, businesses are increasingly recognising that structured, well-governed data is the cornerstone of effective AI-driven solutions. Without clean, reliable, and strategically modelled data, AI can produce inaccurate insights, biased recommendations, or inefficient automation. The success of AI depends on robust data pipelines, governance frameworks, and scalable architectures that ensure the right data is available at the right time.
What are some common data-related challenges that large enterprises typically face?
There are three key challenges I tend to see when it comes to enterprise data.
The first is making sense of the data. Large enterprises generate huge volumes of information across different platforms and departments, but without a clear, unified view, it is difficult to connect the dots. That means businesses might be sitting on valuable insights but are unable to leverage them effectively.
The second challenge is data silos and a lack of strategy. Different departments often have their own databases and tools, creating a fragmented data landscape. Without proper integration, businesses end up with incomplete insights and decisions based on limited information. On top of that, many companies invest in analytics without a clear roadmap, meaning data initiatives do not always align with business goals or deliver meaningful results.
The third challenge is weak data governance. If there is no clear ownership and accountability, businesses face data quality issues, compliance risks, and accessibility problems. Governance is essential for keeping data reliable, secure, and usable across the organisation. Without it, even the best analytics tools cannot provide accurate insights.
Businesses that take a proactive approach to these challenges will gain deeper insights, make smarter decisions, and fully unlock the value of their data.
What are the top client priorities for those looking to drive successful outcomes in data and analytics?
The main priority for large enterprises is ensuring that data is structured in a way that makes sense to business teams. Data must not only be available but also organised, accessible, and aligned with business objectives so that decision-makers can extract meaningful insights. Companies are increasingly recognising the importance of empowering teams with well-governed, well-structured data models that enable faster and more informed decision-making. Without a clear structure and governance framework, even the most advanced analytics tools will not deliver real business value.
Another critical factor is showing value quickly. Early wins are essential for proving the impact of data initiatives and securing buy-in across the organisation. Businesses need to see tangible results fast, whether through automation, streamlined reporting, or AI-powered insights. Strong collaboration between data teams and business teams plays a key role. When data professionals work closely with stakeholders, they can better understand challenges, refine solutions, and make enterprise data more impactful.
Lastly, cost management remains a top priority. While cloud technologies provide unmatched scalability, they also introduce the risk of rising costs if not properly managed. Enterprises must strike a balance between leveraging cloud flexibility and maintaining cost control by optimising storage, processing, and data usage.
Mark, why Snap Analytics?
What sets Snap apart is our ability to deliver high-quality solutions that drive real business impact. While technical expertise is at the core of what we do, our approach is always business-first, ensuring that every solution we develop has a tangible, measurable outcome for our customers. Data and analytics should not exist in isolation, they should directly support business strategy, streamline operations, and enable smarter decision-making. That is exactly what we focus on.
But more than that, we foster a collaborative, open partnership with our customers. Every project is built on trust, transparency, and shared expertise, making Snap a long-term, strategic partner rather than just a service provider.
