Cloud data platforms have moved from “nice to have” to business-critical. Across the UK, organisations are increasingly moving to the cloud to unlock scalability, improve performance, reduce infrastructure costs, and give teams faster access to analytics.
In fact, over 90% of UK organisations now use at least one cloud service, with hybrid and multi-cloud environments becoming the norm rather than the exception.
Legacy data platforms are struggling to keep up with modern data volumes, real-time reporting demands, and the pace of digital change. As a result, cloud data migration is no longer just an IT initiative, it’s a business priority.
However, moving to a cloud data platform is about more than shifting data from one environment to another. Without the right preparation, organisations risk replicating legacy issues in a new platform.
This article is designed to help IT decision-makers and data leaders prepare for moving to a cloud data solution by understanding the most important considerations upfront and building a clear, effective cloud adoption strategy.
What you will learn in this article:
A cloud data solution is a modern, cloud-based environment that enables organisations to store, integrate, analyse and govern data at scale. Rather than relying on on-premise infrastructure, it uses cloud-native services to deliver flexibility, resilience and performance.
Typically, a cloud data solution includes a cloud data warehouse (such as Snowflake, AWS, Google BigQuery and DataBricks), data integration pipelines that ingest data from multiple systems, and BI and analytics tools that enable reporting and insight. Supporting layers for governance, security and monitoring ensure the platform remains reliable and trusted as it grows.
It’s important to distinguish between simply moving data to the cloud and modernising your entire data platform. A basic cloud data migration may replicate existing challenges in a new environment, whereas modernisation rethinks architecture, processes and governance to unlock real value.
Moving to a cloud data platform is a strategic decision, not just a technical one. The considerations below will help ensure your cloud data migration delivers meaningful business outcomes.
Before selecting tools or designing architecture, it’s essential to be clear on why you’re moving to the cloud. Some organisations focus on cost reduction, others on scalability, performance, faster analytics, or AI readiness. All are valid, but only if they’re clearly defined.
Agreeing on what “success” looks like upfront helps ensure your cloud data platform migration delivers value from day one. It also prevents decisions being driven by technology alone rather than business outcomes. These goals should form the foundation of your cloud adoption strategy.
Expert tip from our Technical Director, Lee Connor:
“We see a lot of organisations jump straight into tools and platforms before they’ve clearly defined what success looks like. The most successful cloud migrations start with outcomes not technology. When goals are clear, everything from platform choice to architecture decisions becomes much simpler.”
Before you start migrating data, you need a realistic view of its current condition. That means understanding issues around data quality, duplication, inconsistent definitions, and legacy structures that may no longer meet business needs.
Migrating poor-quality data into a cloud data solution doesn’t solve the problem, it often amplifies it. Cloud migration is an opportunity to clean, rationalise and prioritise your data, ensuring that what moves forward is accurate, relevant and valuable.
Expert tip from our Data Engineer, Alex Hocquigny:
“Cloud migration doesn’t magically fix data quality issues. In fact, it often exposes them faster. I’d always advise to treat any migration as a chance to clean, rationalise and prioritise your data, otherwise you’re just moving the same problems into a more visible environment.”
Most organisations underestimate how complex their data landscape has become. Over time, systems multiply: ERP, CRM, finance platforms, marketing tools, operational systems, all generating data in different formats and at different speeds.
Understanding how many data sources you have, whether they require real-time or batch integration, and how they depend on each other is critical. A robust integration layer ensures your cloud data platform remains scalable, reliable and adaptable as the business evolves.
Expert tip from our Senior Pre-Sales Consultant, Noel Watson:
“Integration complexity is almost always underestimated. Organisations tend to focus on their main systems and forget about the long tail of spreadsheets, third-party tools and operational feeds. Mapping this properly upfront avoids nasty surprises later.
Governance, security and compliance must be considered early in any cloud data migration. Before moving data, organisations need to understand requirements around data ownership, access controls, retention policies, regulatory compliance and security risks.
Cloud platforms offer powerful governance capabilities, but only if they’re implemented correctly from the outset. The rise of AI adoption also sees an increase in AI-driven data governance. With both of these things in mind, retrofitting governance after migration can be a complex task and, not to mention, significantly harder and more expensive.
Expert tip from our MD, Mike Cawthorn:
“Governance works best when it’s designed in from the start. Retrofitting security and access controls after migration is significantly harder and more expensive. Early governance decisions set the tone for trust, compliance and scalability.”
Not all cloud data platforms are the same, and the right choice depends entirely on your business requirements. Performance, scalability, cost models, integration capabilities and analytics use cases should all be considered.
Platforms such as Microsoft Fabric, Snowflake and DataBricks are often chosen for their elasticity, ease of use and separation of compute and storage. However, the most important factor is alignment with your business goals, not following industry trends.
A strong example is Catalyst BI’s work with Fenwick, where selecting the right cloud data platform led to a 40% reduction in operational costs and up to 10× faster analytics performance.
Expert tip from our Sales Director, Tracy Parish:
“There’s no universally ‘best’ cloud data platform, only the best one for your needs. We always encourage clients to align platform choice with business goals, data volumes, and usage patterns rather than following market hype.”
There are multiple ways to approach data warehouse migration. Some organisations choose a phased approach, gradually moving workloads to the cloud. Others opt for a big-bang migration, switching systems over in one coordinated move.
Key considerations include tolerance for disruption, system dependencies, and whether parallel running of legacy and cloud platforms is required. Choosing the right approach helps minimise risk and maintain business continuity.
Catalyst BI supports data warehouse modernisation using proven frameworks and tools such as DataOps.live, which brings CI/CD and automation into data platform delivery, reducing errors and shortening timelines.
Expert tip from our Data Engineer, Steve Bates:
“The safest migrations are the ones that balance speed with control. Whether it’s phased or big-bang, the key is having clear checkpoints and fallback options so the business is never left without trusted data.”
Cloud migration impacts people as much as technology. Analysts, engineers and business users all rely on data in different ways, and poorly managed change can erode trust.
Considering user impact early allows organisations to plan for training, communication, self-service BI adoption and ongoing support. Maintaining confidence in data during the transition is essential for adoption and long-term success.
Expert tip from our Head of Data, James Green:
“Technology change is easy compared to behaviour change. If users don’t trust the new platform, they’ll revert to spreadsheets. Early engagement, training and quick wins are critical for adoption.”
While cloud data platforms offer cost efficiency, with many organisations reporting 30–40% infrastructure savings, poor design or lack of planning can lead to unexpected spend. Understanding cost drivers such as compute, storage and integration is essential for building a sustainable solution.
Designing for performance from the start and planning for ongoing optimisation ensures your cloud data platform remains efficient as usage grows. Cloud environments benefit from continuous tuning rather than one-off optimisation.
Expert tip from our Account Director, Dan Packer
“Cloud cost control isn’t about spending less; it’s about spending smart. Designing for performance and monitoring usage from day one avoids cost surprises and ensures long-term sustainability.”
Every successful cloud data migration starts with a clear cloud adoption strategy. This includes an upfront assessment, a defined roadmap, and agreement on priorities, risks and success measures.
A structured strategy reduces uncertainty, aligns stakeholders and ensures the migration delivers value rather than disruption.
At Catalyst BI, we run Data Strategy Workshops to help organisations align their business objectives with a robust data strategy, ensuring every insight leads to measurable value and not through technology bias.
Moving to a cloud data platform is one of the most impactful decisions an organisation can make, but only when it’s approached strategically. With the right planning, cloud data solutions deliver faster analytics, lower costs, better scalability and improved decision-making.
By understanding your goals, data, users and architecture before migrating, you avoid costly mistakes and set your organisation up for long-term success.
If you’re planning a move to the cloud or want to assess your readiness, Catalyst BI can help.