Navigating Data Transformation in the Age of AI: Insights from Gareth Powell
Digital transformation has brought about a paradigm shift in the business landscape, reshaping traditional operating models globally. In this interview, we explore the complex world of data management amid the ongoing digital revolution, drawing on the experience of Gareth Powell, Group Data Officer & Partner at Irwin Mitchell and a recognised leader in Data & Analytics with more than 21 years of experience.
Named among “The 100 most influential people shaping the data-driven business narrative in the UK,” Gareth shared his perspective on how organisations can begin data transformation journeys, harness the potential of AI, and build the infrastructure required for long-term success.
Starting the Data Transformation Journey
Data transformation initiatives involve converting data from one format or structure into another to make it more accessible, valuable, and useful for analysis. These projects often begin by identifying an organisation’s data needs and goals, assessing the current state of its infrastructure, and determining where improvements are required.
Aligning these initiatives with wider business objectives such as improving decision-making, enhancing customer experiences, or increasing operational efficiency is essential if transformation programmes are to deliver tangible value.
We began by discussing how organisations typically start their data transformation initiatives.
“We are running at a couple of speeds. Our data strategy is a three-year vision to transform data into a strategic asset. This includes implementing a new structure, building a trusted data platform, enhancing data governance, enabling data fluency through the business and driving value through data products.
Alongside this, we have a wider business transformation underpinned by data migration. Building a single, simplified case management system has been a top priority to drive efficiency and simplify our estate. Many firms like ours have accumulated various technology solutions over the years, resulting in a fragmented landscape that hinders efficiency and productivity.
Implementing a single case management system with a unified data structure will improve efficiency, enhance productivity, and ultimately deliver better service to our clients.”
Machine Learning and AI
With the rapid development of generative AI and machine learning technologies, understanding how these capabilities are being applied in practice has become increasingly important.
Gareth explained that while AI has gained huge visibility recently, its true value still depends on strong data foundations.
“The use of AI in various industries has become increasingly prevalent but is fundamentally nothing new. This trend is driven by the potential for AI to simplify and expedite tasks, leading to increased productivity, particularly with the onset of Generative AI.
Implementing AI involves several steps and considerations, including having a robust cloud data platform and good data quality. I believe the real value add will be those use cases that harness proprietary data for each organisation. Data collection and quality is still hugely important.
We have an Innovation stream as an organisation focussed on harnessing GenAI and are building out a scalable data platform to enable us to capitalise on this further in future.”
Implementing Emerging Technologies
Our conversation then turned to how organisations evaluate and implement emerging technologies within their data ecosystems.
One practical example Gareth highlighted was the use of low-code technology to improve internal processes and reduce operational risk.
“We are starting to leverage PowerApps now as a business. In the context of anti-money laundering, it is necessary to have a thorough and efficient process in place for evaluating clients and matters.
At Irwin Mitchell, we have developed a solution that replaces the need for Excel spreadsheets and streamlines the entire policy evaluation process. This ensures we can effectively address potential risks and maintain compliance with anti-money laundering regulations.
By implementing this solution, we can provide our internal clients with a data-driven solution that mitigates risks, is resilient, reliable and drives efficiency.”
The Talent Challenge in Data Engineering
As the discussion moved towards talent and capability, Gareth highlighted the growing demand for skilled professionals in the data ecosystem.
Data engineering in particular has become one of the most sought-after disciplines in modern organisations, yet the available talent pool remains limited.
“Data engineering is one of the most in-demand disciplines within data. It is often the unsung hero, quietly working behind the scenes to ensure data is managed, processed, and served effectively.
From building robust data pipelines to designing scalable architectures, data engineers play a crucial role in enabling organisations to make informed decisions based on reliable data.
With the exponential data growth in today’s digital age, the need for skilled data engineers has never been greater. They are the backbone of any data-driven organisation, laying the foundation for successful analytics and insight.”
Advice for Future Data Leaders
As our conversation drew to a close, Gareth reflected on the lessons he has learned throughout his career and the advice he would give to someone starting out in the field.
“I have seen some brilliant data science solutions developed in my time, but without a well-thought-out implementation plan these can result in project failure.
Data science projects must consider the end-to-end solution rather than solely focusing on creating a perfect model. Business buy-in and implementation should be given equal importance to ensure success.”
Conclusion
As organisations navigate increasingly complex data environments, Gareth’s insights highlight the importance of combining strategy, infrastructure and talent.
Building strong data platforms, maintaining data quality, and investing in skilled engineers will ultimately determine which organisations succeed in turning data into a genuine strategic asset.
