Data is critical to almost any business: it is the lifeblood that keeps the organization running. But managing, leveraging and realizing the business benefits of data is also complex.
While making full use of data is challenging enough, the bar is only getting higher as AI changes the rules of the game. Traditionally, approaches to data have been viewed through a systems-oriented lens, ensuring that the right data flows through the systems in which a business operates. But now the challenge has shifted to making data AI ready. This means that this need to be marked correctly so that AI applications know what the information is, where it came from, and how it has been used before. This allows the AI to understand what the data means without having to interpret it itself. Without this, the AI can make mistakes, leading to errors, unintended consequences, and even hallucinations.
In the era of AI, reliable data management never been more critical. Organizations need clear policies, strict standards, and clearly defined processes to ensure data quality. Equally important is systematic data management throughout its lifecycle, including consistent management, secure storage and management to ensure data remains accurate, reliable and suitable for advanced analytics and artificial intelligence applications.
Building a data team
All this means that having the right data team in place is critical. However, this has also led to increased competition in the professional market with advanced data skills and experience. Therefore, clarity about the team and roles you are trying to create is very important. So what does a “data dream team” look like?
In our experience, both from the recruiting side serving the market and as a data scientist building and managing a team, you need to get your data team right and the necessary processes and data structures in place before you can even think about getting very far in AI.
Several roles are critical. Firstly, data engineers are fundamental because they establish processes for collecting, managing and storing data for business use; they lay the foundation. Data Architects Ensure data flows and connections between systems meet business needs and can be properly scaled and supported. Then, qualified data scientists And data analysts use the data to generate actionable insights, including the application of artificial intelligence techniques and perhaps the beginning of the transition to the machine learning and automation stages. BI Analysts (Business Analysts) also play an important role by looking at data from a business/industry perspective. As businesses mature through the use of both data and artificial intelligence, the need for Artificial Intelligence and Machine Learning Engineers design intelligent systems using generated data streams.
Another key new role is what might be called 'data translator' or perhaps 'data solutions engineer'. These people form the link between the data team and the business, serving as a conduit to help translate insights from data into business actions that can be taken. This requires both technical skills and knowledge and business acumen and understanding. This role is often overlooked, although more companies seem to understand that it is a vital piece of the puzzle.
It's worth noting that all of these positions are in high demand and can be difficult to fill. As a result, data salaries have risen significantly over the past 18 months or so. While many tech workers' salaries have risen only roughly in line with inflation, some data science jobs have risen perhaps 15-20%. A good analyst can earn around £70-90k, engineers and scientists £80-110k, and an experienced data translator/solution engineer can attract £120k or more.
As you can see from the above, a good approach to data requires a multi-disciplinary team, consisting of different roles and working closely with each other. So it's never just about finding people with the right technical skills: cultural fit within the team and business must also be a key factor. As is often the case, it is not just about technology, but also about people. Companies shouldn't expect to assemble the right data team overnight. This is an organic and gradual process that can take around 12-18 months to fully bear fruit.
A question about leadership
It also raises the question of leadership: who should take executive responsibility for the data and the data team? Most businesses have Chief Data Officer or equivalent (Director of Data or Head of Data, etc.) – the key question is where this person fits in the management hierarchy. In an ideal world, the chief data officer would be on the same level as technology and product leaders and have a seat in the boardroom. In practice this is often not the case, but we expect this to change in the coming years, especially with the continued development of AI. There are other options. For example, in companies pursuing large-scale AI deployments, we sometimes see the Chief Data Officer working alongside the Chief AI Officer, but in some companies the two roles are combined into one. There are no “right” answers here – it really all depends on the individual dynamics within the organization.
Finally, it is also important to understand that data is not just the business of the data team: everyone is a data user. Therefore, data literacy across the business must be high on the agenda, and training and resources must be available to help everyone improve their data competency and confidence. Only in this way can you fully realize the benefits of all the work you've put in to ensure that high-quality, granular and relevant data flows across your organization to support business decisions and drive commercial returns.
Jack Capel is the British Southern director of Harvey Nash. Adam Asprey is Chief Data Officer at Maximus UK.
