Ben Waters, trader, is an alumnus of the London School of Economics and Political Science, having earned a BSc degree in accounting and finance. As an experienced portfolio manager, Benjamin Waters, trader, will recognise all too well the potential of leveraging data analytics and advanced technologies to better inform trading decisions. This article will take a closer look at AI and its potential to help advance the field of data analytics.
According to research by Gartner, by 2025 approximately 90% of current analytics content consumers will become content creators themselves thanks to AI. In July 2024, Gartner, a leading research, analytics and expert guidance firm, shared its insights into the future of the data analytics industry, including predictions on how emerging AI technologies could help ensure high-quality data management.
Business leaders all over the world are relying on data and analytics more and more, working with leading AI tools, investing in data products and recognising data as an organisational asset. Gartner predicts that the role of chief data and analytics officers will evolve as 2025 approaches, underscoring the importance of CDAOs listening to their peers, creating common success stories, sharing in those common successes, effectively collaborating and gaining an intimate understanding of the organisations they work for. Effectively, Gartner asserts that CDAOs will need to ‘step up or step out’, with growing pressure placed upon them to not only focus on data quality and governance but evolve to become key communicators and strategists capable of driving forward the company’s data agenda.
Gartner also predicts that automated analytics will become mainstream, with businesses leveraging AI and machine learning to automate data preparation and insight generation and explanation. Experts predict that by 2025 the majority of analytics processes will be augmented, enabling a broader audience to utilise advanced analytics.
As AI technologies are increasingly integrated into business operations, companies will need to prioritise investment in people. This includes helping staff hone their skills in understanding, interpreting and acting on AI-generated insights. Gartner predicts that training in data literacy and critical thinking will become essential for all team members, enabling them to effectively leverage AI technologies and interpret and apply the valuable insights they provide.
Impacting virtually every industry today, from computer science to advertising, generative AI is poised for even more growth in coming months, with Google reporting that 64% of developers agree that there is a sense of urgency in terms of implementing generative AI. In a survey of business leaders, 85% had already created a plan to use AI to automate low-level tasks by the close of 2024.
AI start-ups help enterprise clients to clean large data sets and create machine learning models. In this way, industry titans like Unilever and General Electric can gain incredibly valuable insights from vast troves of data. AI also helps businesses to automate important data management tasks, streamlining processes and eliminating the risk of human error. End-to-end AI solutions are becoming an increasingly popular addition for forward-looking businesses, enabling them to rely on a single product to handle an entire data science cycle from start to finish without needing to seek out specialist help on all of the different elements then piece it all together themselves.
As data analytics becomes even more sophisticated and pervasive, data privacy and security are tipped to take centre stage. As regulations are tightened and public concern and awareness of data breaches grows, the onus will be placed on businesses to invest heavily in robust security frameworks. Experts predict that blockchain technology may play a game-changing role in ensuring data integrity and nurturing trust between stakeholders by reassuring customers that their data is secure and will be handled ethically.
