Unlocking AI Through Strategic Data Modernisation

The advancement of Artificial Intelligence technologies has fundamentally shifted how organisations view and utilise their data assets. As AI capabilities continue to evolve from machine learning algorithms to large language models and predictive analytics, companies across industries are recognising that their competitive advantage increasingly depends on their ability to harness data effectively. This realisation has elevated data from a by-product of business operations to a strategic asset that directly influences innovation, decision-making, and market positioning.

However, realising the true value of AI adoption can only be achieved with a robust data strategy as the foundation. Organisations are discovering that successful AI implementation requires more than just adopting the latest technologies; it demands clean, well-governed, and strategically organised data infrastructure. Companies must now consider data quality, accessibility, security, and compliance as critical business imperatives rather than technical afterthoughts.

Without a comprehensive data strategy that addresses collection, storage, processing, and governance, even the most sophisticated AI tools will fail to deliver meaningful business value, making data strategy the cornerstone of any successful digital transformation initiative.

Data Strategy as the Foundation for Modernisation

In today’s rapidly evolving technology landscape, organisations are increasingly embracing software modernisation to stay competitive, agile, and responsive to customer needs. Modernising legacy applications and infrastructure often involves shifting to cloud-native architectures, microservices, and event-driven systems. But amid these shifts, the role of a robust data strategy becomes more critical than ever.

A comprehensive data strategy is the foundation that enables organisations to unlock the true value of their data assets while supporting modern software initiatives. It ensures that data flows efficiently and securely through modernised systems, enabling real-time insights, improved decision-making, and better customer experiences.

Key considerations in defining a data strategy during software modernisation include:

• Data Accessibility & Integration: Modern applications require seamless access to diverse data sources, including databases, APIs, streaming platforms, and third-party services. The data strategy must define how to unify and integrate these data streams, often using APIs, data lakehouses, or event hubs.
• Data Quality & Governance: As systems evolve, maintaining data accuracy, consistency, and compliance becomes more complex. Establishing clear data governance frameworks, validation rules, and lineage tracking helps maintain trustworthiness and regulatory compliance.
• Scalability & Performance: Modern architectures must handle varying workloads efficiently. The data strategy should address scalable storage solutions and processing frameworks that support batch and real-time analytics without bottlenecks.
• Security & Privacy: With the rise of cloud deployments and distributed architectures, securing data at rest and in transit, as well as enforcing privacy policies, is paramount.
• Automation & Observability: Leveraging automation for data pipelines, monitoring, and alerting helps maintain resilience and visibility in fast-changing environments.

Ultimately, integrating a well-thought-out data strategy with software modernisation efforts empowers organisations to move beyond simply updating technology, it enables them to transform how data drives innovation and business outcomes

Data Engineering at Codurance

At Codurance, we understand the importance of a strong data strategy and any modernisation efforts need to consider data as a key aspect. We also understand that technology changes need to be aligned with business goals. For that reason, we take the time to fully understand our clients’ business challenges so that we can be sure we are solving the right problems in the right way. 

Our data engineering practice combines technical excellence with collaborative partnerships, embedding our engineers directly within client teams to transfer knowledge whilst delivering tangible results. We have experience in building scalable, cloud-native data platforms using modern technologies and engineering best practices, from real-time data pipelines to advanced analytics infrastructure. We focus on building sustainable data foundations through modern data architecture patterns. 

Beyond the technical implementation, we place equal emphasis on establishing engineering culture and processes that ensure long-term sustainability including automated testing, continuous integration, and monitoring practices that maintain data quality and system reliability. This approach ensures that organisations not only achieve their immediate data objectives but also develop the internal capabilities to continue innovating and scaling their data capabilities independently. 

IWSR Case Study: Building a Future-Ready Data Platform

IWSR, a global leader in beverage alcohol market intelligence, sought to enhance its forecasting capabilities to meet increasing customer demand for long-term predictive insights. Their existing manual forecasting methods were becoming inefficient and lacked scalability.

The Challenge 

To modernise their forecasting process, IWSR aimed to integrate an AI model with their data infrastructure. However, they faced difficulties in automating the full forecasting process and ensuring seamless data provisioning across their systems.

The Solution

Partnering with Codurance, IWSR implemented a scalable data platform using Databricks on AWS. This platform integrated the AI model and exposed its outputs via APIs, enabling other systems to consume the data. Additionally, a Microsoft Excel add-in was developed to allow researchers to retrieve and manipulate AI model results directly within Excel.

Outcomes

• Achieved a 10-year forecasting horizon, facilitating long-term planning for clients.
• Implemented biannual releases aligned with global database updates.
• Established a standardized process for data interaction across the business.
• Enhanced data governance through versioning and auditing, leading to improved data accuracy.
• Increased scalability into new global markets by automating data curation and transformation processes.

This transformation not only streamlined IWSR's forecasting process but also positioned them to leverage AI for future growth and innovation.

For a detailed look at IWSR's data engineering journey, check out the full case study.

Conclusion

Reach out to our team to discover how we can partner with you to turn your data and AI ambitions into reality. Whether you want to kick-start your modernisation journey, unlock the power of AI, or review your current strategy, we'd love to help.