As the demand for more efficient and scalable data management grows, businesses are increasingly relying on advanced data platforms to harness the power of their data ecosystems. What started with data lakes – centralised repositories to store massive amounts of structured and unstructured data – has now evolved into more sophisticated frameworks like data fabrics, which enable seamless integration, management, and usage of data across different platforms.
However, as the data landscape evolves, new challenges emerge, including growing data privacy concerns, the need for real-time data processing, and the increasing complexity of managing diverse datasets. To stay ahead, organisations must rethink their approach, adopting solutions that not only handle data efficiently, but also enable better insights and faster decision-making. This is where emerging models promise to reshape the future of data management.
Challenges with current models
While data lakes have been instrumental in providing a centralised location for large amounts of data, their limitations are becoming increasingly clear. One of the major issues is the lack of structure and governance; businesses often find themselves with huge, unorganised pools of data which are difficult to access and analyse. The volume of uncurated data in these lakes leads to what is often referred to as ‘data swamps’: repositories filled with data that has no clear quality control, making it less useful for business insights. A related challenge is the growing amount of ‘dark data’ – information stored within the enterprise that is neither indexed nor actionable. The inability to effectively use this data limits its potential value, further underscoring the need for better data governance.
Another challenge with current data lakes is integration. As businesses adopt more diverse data sources, the challenge of integrating structured and unstructured data becomes more complex than ever before. Data silos still persist across organisations, preventing the seamless use of data across various platforms and ultimately hindering the ability to leverage real-time insights.
In addition to the above, data security and privacy concerns are still glaring obstacles. With evolving regulations like GDPR and the end of third-party cookies, businesses are increasingly grappling with how to protect sensitive data while still making it accessible for analysis. This is further exacerbated by the lack of robust data dictionaries and catalogues, which are essential for efficient data discovery, indexing, and analysis. Without these tools, enterprises struggle to create actionable insights from the wealth of information they possess.
Emerging trends in data management
The shift from data lakes to data fabrics has largely been driven by the need for a more flexible and scalable approach to data management. One key trend in data management is the move to a cloud-first approach, whereby businesses are increasingly moving their data management infrastructure to the cloud. Cloud platforms offer better scalability, allowing companies to manage and access data in real-time across distributed environments. This shift also supports AI integration, enabling businesses to automate data processing and extract more meaningful insights from their data.
Additionally, the end of third-party cookies is pushing companies to think about how they manage and leverage data for personalisation and targeting. With the tightening of privacy regulations, businesses are now prioritising first-party data and developing strategies to collect and manage such data. This is where data fabrics are particularly useful, as they provide a seamless flow of information across channels, while ensuring compliance with evolving privacy laws. The ability to maintain a unified view of customer data across multiple sources is absolutely instrumental to delivering personalised, privacy-first experiences.
Role of AI and automation in data management
AI and automation are revolutionising the way businesses handle their data. Data fabrics are particularly well-suited to this transformation, as they provide the foundation for intelligent automation. With AI, businesses can automate data integration, governance, and quality control, which have historically been labour- and time-intensive processes. AI-driven data fabrics enable organisations to apply machine learning algorithms to their data in real-time, allowing them to identify patterns and insights that may otherwise be missed.
Furthermore, automation also plays a crucial role in the centralisation of data, ensuring that data is available when and where it’s needed across an organisation. Instead of manually moving or preparing data, automated processes within a data fabric can manage the entire lifecycle of data from ingestion to insight. This not only increases efficiency, but also reduces the risk of human error, leading to more reliable and actionable data.
How businesses can prepare for the future of data management
In order to take full advantage of the evolving data landscape, it is essential that businesses make efforts to modernise their data stacks. Acxiom’s recent Mass Martech Modernisation whitepaper revealed that martech is becoming increasingly important for organisations, with the majority (60%) of C-suite teams seeing it as a bigger priority in recent years, indicating a significant buy-in for martech modernisation at the highest levels.
In fact, nearly 65% of businesses expect their martech budgets to increase over the next 12 months, reflecting a heightened awareness of its strategic importance.
Modernisation begins with breaking down internal data silos and ensuring that data flows seamlessly across departments and platforms. A unified approach to data management, enabled by data fabrics, will allow businesses to move away from fragmented data sources, toward a more holistic view of their operations.
Adopting a composable architecture, which breaks down large applications into smaller, independent components, is also critical for future-proofing data management. This approach ensures that investments in existing data infrastructure, even cloud-based systems, are not left under-utilised. Instead, these components become building blocks for the broader data foundation of an enterprise-wide data fabric, anchored in a real-time platform like a Customer Data Platform (CDP). A composable architecture provides the flexibility to evolve and scale as new technologies emerge, allowing businesses to integrate various data solutions into a cohesive system without needing to overhaul existing infrastructure.
Embracing the cloud is another critical step. Moving data to the cloud offers enhanced scalability, agility, security, and flexibility. Cloud-based data management also allows businesses to leverage AI and machine learning tools more effectively, enabling real-time analysis and insights. Additionally, the cloud allows companies to adopt new technologies without the need for significant capital expenditure, as cloud providers continually introduce new features and innovations. As businesses modernise, they should also focus on data governance and privacy, ensuring they have the right frameworks in place to protect customer data while maximising its utility for business insights.
What’s next?
Data management is moving towards greater integration, agility, and intelligence. As businesses increasingly adopt cloud-first strategies, they’ll be able to manage data more efficiently, breaking down silos and enabling seamless access across departments and platforms. This shift also allows organisations to take full advantage of AI and automation, which will revolutionise how data is processed and insights are extracted.
Another key trend is the “breakout” of the single customer view from its traditional marketing confines. Going forward, customer profiles must be democratised across the entire enterprise. Unified customer profiles will become foundational to the success of new AI capabilities, allowing them to deliver more accurate and relevant results by “grounding” the AI capability with up-to-date customer profile data in real-time. These profiles will not only serve marketing but will increasingly drive operational use cases, such as service calls or chatbots for issue resolution, creating efficiencies that benefit the bottom line while supporting traditional top-line revenue objectives.
At the same time, businesses must prepare for the realities of a privacy-first, post-cookie world. Third-party cookies are still likely to be phased out at some point in future as privacy regulations and browser restrictions continue to tighten, meaning companies will need to rely more on first-party data. The emphasis will be on creating personalised, secure experiences for customers, driven by real-time data insights.
Ultimately, the next phase in data management is about creating adaptive, intelligent systems that can handle vast volumes of data while ensuring compliance and enabling rapid decision-making. Businesses that invest in modern data infrastructure and governance today will be well-positioned to thrive in the data-driven future.
Dimitrios Koromilas
Dimitrios Koromilas is Director of Platform Services EMEA at Acxiom. With over 15 years of experience in the IT delivery, and 9 years in the solution architecture space, Dimitrios is the implementation lead for multiple end-to-end solutions for Blue chip clients in the Finance, Telco and FMCG verticals.
