In today’s AI-driven business environment, data is more than just a by-product of operations; it’s the fuel that makes decision-making easier and drives competitive advantage.

But as organisations look to harness AI, simply collecting data isn’t enough. To turn raw information into actionable insights, digital leaders must build a data team with the right mix of technical expertise and soft skills.

Jack Capel, UK South Director at Harvey Nash, was quoted in a recent Computer Weekly article, emphasising that getting this mix right is critical. From engineers who structure and manage data flows, to analysts who uncover insights, to translators who bridge the gap between tech and business, the capabilities of your team determine whether AI initiatives succeed or fall short.

This article outlines the top technical and soft skills in demand that every digital leader should prioritise when assembling a modern, AI-ready data team.

Making data AI-ready

Before organisations can realise the full potential of AI, they must ensure their data is structured, accurate, and accessible. This begins with assembling a team with the right technical capabilities:

SQL and data engineering: Data engineers are the architects of the organisation’s information infrastructure. By establishing processes to collect, store, and manage data, they lay the foundations for reliable, AI-ready datasets. Proficiency in SQL and strong data governance practices are essential to ensure that data is accurate, secure, and fit for advanced analytics.

Python and advanced analytics: Data scientists and analysts transform raw data into actionable insights. Expertise in Python enables teams to build predictive models, apply machine learning techniques, and automate analysis, helping the business make smarter, data-driven decisions.

Cloud infrastructure: Modern data teams must be fluent in cloud platforms such as AWS, Azure, or GCP. Cloud expertise ensures data flows are scalable, resilient, and aligned with evolving business needs, providing a flexible foundation for AI initiatives.

Data architecture and scalability: Data architects ensure that connections between systems align with business objectives and can be scaled as the organisation grows. This role ensures that the technical framework can support both current analytics requirements and future AI ambitions.

Translating insights into action

While technical expertise is essential, Jack Capel, UK South Director, emphasises that data alone does not generate value. Organisations increasingly recognise the importance of roles that bridge the gap between technical teams and the business:

Storytelling and communication: The ability to translate complex datasets into clear, actionable insights is vital. Data translators, sometimes referred to as data solutions engineers, combine technical knowledge with communication skills to ensure insights are understood and applied effectively across the organisation.

Business acumen and collaboration: Successful data teams understand the organisation’s strategic goals and work closely with stakeholders to deliver real-world value. Cultural fit, collaborative working, and the ability to connect analytics to business outcomes are just as critical as technical prowess.

Capel also highlights that building a high-performing data team is not a one-off exercise. Recruitment challenges, particularly in areas such as data analytics and engineering, mean that team development is an organic process, often taking 12 - 18 months to mature.

Leadership and universal data literacy

Leadership support is key to embedding a data-driven culture. Capel suggests that organisations should consider placing their Head of Data or Chief Data Officer on a par with heads of technology and product, ideally with a seat in the boardroom. This ensures data priorities are recognised strategically and resourced appropriately.

Equally important is fostering data literacy across the business. Everyone in the organisation is a data user, and providing training and resources empowers employees to make informed decisions, increasing the return on investment in data infrastructure and AI initiatives.

Prioritising skills for sustainable success

Building a modern data team is a strategic investment, not just a hiring exercise. Organisations that prioritise both technical skills and soft skills are best placed to unlock the full potential of their data.

Given the competitive market for data professionals, securing the right talent can be challenging, but it is essential for AI readiness and long-term business success. Capel’s insights make it clear that a successful data team is about more than just technology; it’s about people, process, and culture working together to transform data into actionable intelligence.

If your looking to strengthen your data capabilities, Harvey Nash can support in sourcing and recruiting the professionals needed to build high-performing, AI-ready teams.

Submit a vacancy today or contact us directly and take the first step toward assembling the data team that will drive your business forward.