In 2024, data management leaders in Australia should assess and refine their strategies to ensure success in this new year. Data-centric organisations need to recognise the mission-critical aspects of data management and focus their efforts on improving quality and accuracy. More and more, business stakeholders are seeking to actively participate in the data and analytics journey and have an active role in critical decision-making. The role of the IT department now incorporates deploying the right architecture to respond to this growing array of demands.
The impact of Generative AI (GenAI) on data management and vice versa necessitates high-quality and trusted data, as well as a stable foundation on which to build applications and services. In 2024, data anti-gravity, or distributed data management, will become the new norm. With the rapid rise in generative AI - indeed, Gartner forecasted in October that over 80 per cent of enterprises will have used GenAI by 2026, up from less than five per cent in 2023 – it will become increasingly important to access data fast and seamlessly right across the enterprise. As such, distributed data management will gain importance and become crucial for the successful adoption of GenAI in the enterprise.
Data Anti-Gravity Will Prevail in a World of Distributed Data
This year, data gravity will no longer hold sway, and organisations cannot meet their business needs with a single data lake, data warehouse or data lakehouse strategy, whether in the cloud or on-premises. While these repositories were initially adopted to solve data silo problems, they often exacerbated the issue by being spread across different locations, both on-premises and in the cloud. Furthermore, organisations increasingly operate in a multi-cloud environment, utilising services from various cloud providers. As the volume of data has continued to grow, it has been a natural reaction for applications, services and other data to be ‘pulled’ into a repository in order to use that data. Moving forward, however, organisations will increasingly look for ways to make that data instantly accessible across systems and repositories without duplication or delays. As a result, data anti-gravity will become the norm, driven by technology, geography, and ownership factors. Organisations must invest in technologies that embrace distributed data management to effectively navigate this new era.
The Rise of Data Products in Enterprises
2024 will mark a pivotal moment for the rise of data products in the Australian enterprise landscape. The significance of data products will be recognised, with enterprises treating them as valuable offerings akin to any other product. Similar to a bookcase or shelf from Ikea, the value of data products lies not only in the data itself but also in the comprehensive package surrounding it, including descriptions, assembly instructions, intended use, and safety measures. Data catalogues will play a crucial role in turning raw data into reliable, consumable assets by providing attractive packaging and enhancing the end-user experience. These catalogues will incorporate features such as personalised recommendations, popular product highlights, user endorsements, data lineage visibility, real-time queries, and interactive feedback loops. Enterprises must prioritise quick and dependable access to data, just as e-commerce values prompt delivery, with both being crucial for success in their respective domains.
Data Architectures Emerge to Ease GenAI Adoption and Ensure Success
The adoption of Generative AI and Large Language Models may face obstacles in the absence of superior data quality, governance, ethical compliance, and cost management. Effective data management is essential to overcome these obstacles and successfully implement GenAI initiatives. A robust data architecture emerges as a critical necessity, as high-quality, well-governed data form the foundation for reliable and ethically compliant AI outputs. Organisations that proactively invest in a strong data management framework will unlock the full business potential of these advanced technologies. Data management will become pivotal for successful GenAI adoption, with data mesh architectures and data product creation gaining prominence. Data literacy programs will empower business stakeholders to prioritise data-driven decision-making, ensuring that IT departments do not become bottlenecks.
Conclusion
Data management leaders must recognise the evolving landscape and refine their strategies accordingly. Data anti-gravity, the rise of new data products, and the emergence of robust data architectures are key trends that will shape the data management landscape. Organisations must embrace distributed data management, treat data products with importance, and invest in strong data architectures to ensure successful GenAI adoption. By aligning their strategies with these trends, Australian businesses can navigate the data-driven future and unlock the full potential of their data assets.