According to the 2023 Digital Leadership Report, gaining actionable insights from data is one of the top five priorities for digital leaders in 2024, with almost half of the participants seeing data as one of the best ways to deliver a return on investment. However, only one in four IT leaders surveyed in the Digital Leadership Report say they are ‘very’ or ‘extremely effective’ at using data insights to generate more revenue.
So, this week, we caught up with Ian Bobbett, the newly appointed Chief Data Officer (CDO) at Crimson, to discuss the data trends we can expect to see in 2024.
The datasphere is moving very quickly, partly powered by the hype curve around generative AI. This means that companies are under ever increasing pressure to resolve their data landscapes and transform data from being not only functional but also a value-add asset.
However, going through a data transformation is no simple task – one size doesn't fit all, and it's hard to know where to start and where to finish. Adding into the mix rapidly evolving technologies, regulatory/compliance pressures, and a challenging and competitive marketplace makes for a complex path to success.
The companies that do data well understand that it's about more than just data and technology – good data needs a scaffold of support around it to succeed, from colleague engagement and organisational structure to data activation and user experience.
With this backdrop, keeping an eye on what's coming can be difficult, so here are a few things to think about over the coming 12 months…
While this is a bit of a bland statement, data volumes and velocity will continue to increase for the foreseeable future, which needs to be planned and managed. This will be complicated by edge computing, where data is analysed closer to source, reducing latency and enabling real-time insights in IoT and distributed systems.
Machine learning (a branch of AI) has huge untapped potential across every aspect of our lives, from the mundane, such as driving a car, to the profound, for example, our physical and emotional wellbeing. While not a seismic shift, machine learning will continue to play a growing part in almost every aspect of our lives in the future.
Many machine learning solutions are built on data created by human action – from buying a packet of biscuits to getting a new job – but that comes with its challenges. Issues such as unconscious bias can manifest in data, and there is a real risk that this will be perpetuated or even accentuated in ML solutions. Regulation and renewed focus on the ethical considerations in ML are important to ensure responsible data usage and support improved transparency and fairness moving forward.
Many ML solutions are ‘black box’, so while the outcome is visible, we don’t really understand how a solution was reached. Explainable AI will be a growing trend over the next few years to make ML solutions more interpretable and turn the black box into a glass box, again supporting transparency and fairness in data use.
Data mesh architecture is a de-centralised model for managing data across a business. Rather than having a central function that manages your data assets, that responsibility is transferred to business units to manage their own data assets (they are the subject matter experts, after all). The main challenge here is that it is more a cultural shift than a technological one, and implementing it successfully takes time. While there will inevitably be concerns around governance and control, this area has plenty of momentum.