How to modernise your data platform: benefits, challenges and solutions

What is data platform modernisation?

This upgrade spans everything from data storage and processing to data strategy, analytics and governance, enabling businesses to scale efficiently, access real-time insights, and support innovation.

By modernising their data platforms, organisations can reduce operational costs, strengthen security, accelerate decision-making, and lay the foundation for advanced AI and analytics – key advantages for driving business growth in the age of big data.

What are the key drivers for data platform modernisation?

Several pressing factors are driving organisations to modernise their data platforms. Explosive growth in data volumes is pushing legacy applications to their limits, often resulting in performance bottlenecks and escalating maintenance costs.

At the same time, widespread cloud adoption and digital transformation initiatives are compelling businesses to shift toward more flexible, scalable architectures.

The increasing demand for real-time data accessibility – whether for customer experiences, operational agility, or timely decision-making – is also a major motivator, making modernisation not just a technical upgrade, but a strategic imperative that helps businesses future-proof.

How do I know if my data platform needs modernisation?

Recognising when your data platform needs modernisation starts with identifying performance and efficiency red flags.

Frequent downtime, sluggish reporting, and limited scalability often indicate that legacy systems are struggling to keep pace with business demands.

Integration issues with modern analytics, AI tools, or cloud services can further signal that your platform is falling behind.

If you’re also facing rising costs for storage and processing, it’s a clear sign that your current setup may no longer be sustainable – or competitive.

What are the main approaches to data platform modernisation?

There are several strategic approaches to modernising a data platform, each tailored to address specific technical and business needs.

Organisations often adopt one or more of the following:

• Migrating to cloud-native data warehouses such as Snowflake, BigQuery, or Azure Synapse to gain scalability, flexibility, and cost efficiency.
• Building data lakes or lakehouses to unify structured and unstructured data, enabling broader analytics and machine learning capabilities.
• Modernising ETL/ELT pipelines with tools that support automation, real-time processing, and seamless integration across systems.
• Adopting real-time streaming architectures like Apache Kafka or AWS Kinesis to power up-to-the-minute insights and responsiveness.

What are the benefits of cloud-based data platforms?

Cloud-based data platforms offer a wide range of advantages that make them ideal for modernising your data infrastructure.

Key benefits include:

• Pay-as-you-go pricing – allows businesses to lower costs by paying only for the resources they use, ensuring efficient budget management.
• Faster scaling and better flexibility – enables seamless handling of growing data volumes and user demands without the need for significant infrastructure changes.
• Built-in security and compliance – provides robust protection against security vulnerabilities and adherence to industry standards, reducing the burden of regulatory compliance.
• High availability and disaster recovery – ensures reliable performance with minimal downtime, safeguarding operations against unexpected disruptions.
• Access to native analytics and AI services – unlocks powerful tools for real-time insights, predictive modeling, and automation, enhancing decision-making capabilities for business users across all departments.
• Improved decision-making – enables access to real-time data and analytics, allowing leaders to quickly adapt to market shifts and operational challenges.
• Reliable database and efficient data processing – optimises databases for speed and reliability, improving processing of large datasets and reducing latency for faster insights.
• Increased customer satisfaction – provides real-time access to customer data, enabling businesses to offer personalised experiences and improve overall service quality.

How do you plan a data platform modernisation project?

To successfully plan and execute a data platform modernisation project, follow this step-by-step checklist:

Assess your current environment

Evaluate your existing data architecture, performance bottlenecks, integration challenges, and total cost of ownership. Identify pain points and gaps in functionality that are hindering innovation.

Define clear business goals

Align modernisation efforts with strategic objectives such as real-time analytics, AI enablement, cost reduction, or improved agility. Well-defined goals ensure all technical decisions support broader business strategies.

Select the right technologies

Choose cloud platforms and data tools that fit your goals and use cases. Ensure the technology stack can scale with your business and integrate effectively with existing systems.

Design a scalable architecture

Create a flexible, future-ready data architecture that supports growth and evolving business needs. A well-designed architecture will allow for easy integration of new tools and services as your business expands.

Implement in phases

Break down the project into manageable, low-risk iterations to deliver value quickly and minimise disruptions. This phased approach ensures agility and allows for adjustments based on feedback and changing requirements.

Invest in education and training

Ensure your teams are equipped to operate and leverage the new platform effectively. Training helps maximise adoption and empowers your workforce to use the platform’s capabilities fully.

What challenges are common during data platform modernisation?

Data platform modernisation comes with its share of challenges that organisations need to anticipate and manage carefully.

Data migration complexity

Migrating large volumes of data, especially from legacy formats or systems with minimal documentation, can be time-consuming and error-prone.

To mitigate it, use automated migration tools, perform a detailed inventory of existing data, and conduct pilot migrations to identify potential issues early.

System integration issues

Integrating new systems with existing tools and workflows often requires significant effort and customisation.

To mitigate it, develop a detailed integration plan, conduct thorough testing before full deployment, and use middleware or API gateways to bridge legacy applications with modern solutions.

Ensuring data quality and consistency

Ensuring data accuracy and consistency during migration is critical to avoid disruptions in reporting and analytics.

To mitigate it, implement data quality checks, standardise data formats, and establish data governance practices that include continuous monitoring and validation of migrated data.

Managing change across teams

From training staff to shifting mindsets, resistance to change can slow progress if not handled proactively.

To mitigate it, engage stakeholders early, provide targeted training, and create clear communication strategies to ensure all teams understand the benefits and their role in the transition.

Controlling project scope and costs

Modernisation efforts can easily expand beyond initial plans, leading to scope creep and budget overruns.

To mitigate it, define clear project objectives and deliverables, use an agile project management approach to keep the project on track, and set realistic timelines and budgets, with frequent check-ins to assess progress.

FAQ

Should we move our data platform to the cloud during modernisation?

Yes, cloud platforms offer elasticity for scaling without large upfront costs, lower total cost of ownership with pay-as-you-go pricing, and reduced on-premise hardware needs. They also provide enhanced security with built-in encryption and compliance tools, and make it easier to integrate AI/ML and analytics services for advanced insights and machine learning capabilities.

What is the role of DataOps in modernising a data platform?

By incorporating DataOps practices, organisations can ensure continuous integration, delivery, and testing of data flows, which accelerates the modernisation effort. This approach helps teams collaborate more effectively, reduces manual errors, and enhances data quality by automating repetitive tasks and introducing real-time monitoring and alerting. The result is more efficient data processing and faster deployment of new features or updates.

How does data governance fit into modernisation efforts?

Data governance is a critical element that must be embedded throughout the modernisation process. As organisations migrate to modern data platforms, they need to ensure that data cataloging, access control, and compliance auditing are maintained across all systems. This ensures that data is secure, privacy is upheld, and regulatory requirements are met, even as new technologies are introduced.

Strong metadata management also supports better data discovery and traceability, allowing stakeholders to find and use data more efficiently while ensuring transparency and accountability. Without robust data governance, modernisation efforts may introduce vulnerabilities, compliance risks, and data inconsistencies.

How long does a data platform modernisation project typically take?

For smaller migrations or cloud transitions (e.g., managed services or basic data warehousing), the process can take months. Larger, complex transformations with advanced data warehousing and integrations may take over a year, influenced by data quality, governance, and available resources.

How do you measure the success of data platform modernisation?

The success of a data platform modernisation can be measured using a range of performance and operational metrics. Key indicators include:

• Query performance improvements – faster and more efficient data queries demonstrate better system optimisation.
• System uptime – higher availability means fewer disruptions, indicating the platform is more reliable.
• User adoption rates – if users are engaging with the new system effectively, it’s a sign of success in terms of ease of use and meeting business needs.
• Data processing speeds – improved speed in processing large data volumes means greater operational efficiency.
• Reduced storage costs – cloud-based or modernised platforms often help reducing costs associated with on-premise storage.
• Analytics output quality – the ability to deliver accurate, actionable insights faster reflects the platform’s enhanced analytical capabilities. These metrics together provide a comprehensive view of whether the modernisation efforts have achieved their intended goals.