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Tag: Cloud Cost Management

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. 

AI Isn’t the Hero, Your Data Is.

Posted on by Luka Jasionyte

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. 

The Big 3 Data Problems Holding Enterprises Back

Posted on by Luka Jasionyte

Luka Jasionyte, in the marketing team at Snap Analytics, catches up with Deepam Biswas, our Head of Technology and Delivery in the India office, to get the low down on his journey through data and analytics, and to share insights on what he thinks are the most important trends, Generative AI in business, and challenges facing enterprise businesses today. 

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

Data has always fascinated me, not just as numbers, but as the foundation of every decision, strategy, and transformation. What truly drew me into the field was the recognition that data is only as valuable as its accuracy, structure, and data governance. Without trust in the data, businesses can’t make informed decisions, drive efficiency, or unlock meaningful insights. My curiosity for uncovering patterns, validating assumptions, and refining raw information into something truly usable has been a constant throughout my journey. 

What is the most exciting trend right now? 

In my opinion, the most exciting trend in data and analytics right now is the integration of Generative AI in business. With Generative AI, we can leverage natural language to both analyse data and generate actionable, prescriptive strategies. This means that we can ask complex questions in plain language and receive insightful, data-driven responses that guide decision-making.

Additionally, modern BI tools have evolved to automatically identify patterns, anomalies, and correlations in data that might be missed by human analysts. These tools present insights in an easily digestible format, making it simpler for businesses to understand and act upon the data. Another fascinating development is edge computing, which allows for the processing of sensor data almost in near real-time. This capability enhances efficiencies in business processes such as warehouse management and production management by providing timely insights that can significantly improve operations. Overall, these advancements are pushing the boundaries of what we can achieve with data and analytics, making it an incredibly exciting field to be a part of. 

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

One of the most significant challenges I’ve seen in large enterprises is dealing with data silos. Data is often scattered across various systems like CRM, OMS, Billing & Invoicing, and legacy systems, making it incredibly difficult to get a comprehensive 360-degree view for data analytics. This fragmentation can lead to inefficiencies and missed opportunities for insights. Another major issue is the persistent skill shortage in the data field. There’s a high demand for skilled professionals such as data engineers, data scientists, data analysts, and data governance specialists, but the supply just can’t keep up. This gap can hinder the ability of enterprises to fully leverage their data. Additionally, many large enterprises still rely heavily on batch processing of data, which results in considerable latency in generating insights. This often forces upper management to make business decisions based on outdated data and gut feeling, rather than real-time analytics.

Near real-time data analytics is still complex and costly, but it’s crucial for making timely and informed decisions. Generative AI in business is helping bridge this gap by automating data analysis and providing real-time insights without requiring deep technical expertise, enabling enterprises to make smarter, faster decisions. These challenges can significantly impact the efficiency and effectiveness of data-driven strategies in large enterprises. 

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

When it comes to driving successful outcomes in data and analytics, clients have some top priorities that are pretty clear. First and foremost, they want to know how this data is going to make them more money or save them money. It’s all about ROI and tangible business values. It’s no longer enough to just get reports or dashboards that tell them what happened. Clients want to understand why it happened and, more importantly, what they should do next. They seek prescriptive analytics that guide decision-making. And let’s not forget about protecting sensitive data. With the growing number of data privacy regulations like GDPR and CCPA, adhering to these rules is non-negotiable. It’s all about making sure their data is secure while still being able to leverage it for business success. 

Deepam, why Snap Analytics? 

Snap Analytics has extensive experience with diverse and complex client projects across industries such as FMCG, Finance, and Supply Chain. This means clients benefit from the exposure to a wide range of real-world business problems and data challenges, and we provide effective solutions to tackle them. Our core mission is to help clients ‘make sense of data’, by connecting their data, technology, and teams to drive more effective decision-making. This approach ensures we deliver tangible business value, not just technical solutions. 

Also, we proudly work with the most progressive technologies and leading cloud vendors like Snowflake, Databricks, and Matillion, providing clients with hands-on experience with in-demand tools and platforms. I would say that one of our key differentiators is our specialisation in SAP Data. We excel in connecting complex SAP landscapes to modern cloud data platforms, providing seamless and efficient data integration.