Carl D’ Halluin, CTO, Datadobi, tells us how businesses can balance scalability and security when managing large volumes of data in an increasingly complex regulatory landscape.

Despite its importance, the approach organisations take to data varies dramatically. Many collect vast amounts of data without a clear vision of how it will be used and, instead, spend significant sums storing various datasets in anticipation of progress later on. Elsewhere, organisational leaders fully understand the latent potential in their data but lack the skills, processes or technologies to translate objectives into deliverables.

Get data strategy right, however, and businesses stand to gain enormous operational, financial and competitive advantages. But failing to capitalise can see organisations fall behind their rivals, with management coming under significant pressure to raise their game. All of this is taking place against the backdrop of a highly complex regulatory environment, where the scope for non-compliance is greater than ever.

So, in the context of data management, what does‘ good’ look like? There are a range of important priorities to balance if organisations are to build scalable, high-performance data environments that also deliver the high levels of security and compliance required. Let’ s look at some of the key components in more detail:

1. Establish a scalable data management framework

The first priority should be to implement a scalable data management framework. Why? Well, up to 90 % of business data is now unstructured and can be anything from videos, images, scanned documents, and emails to social media posts and audio recordings, with data volumes growing at an exponential rate. As such, it lacks a predefined format and organisation, making it difficult to store and analyse in traditional databases.

For many businesses, this presents serious management challenges because, without the ability to organise, store and secure data to ensure its accuracy, accessibility and compliance, it becomes almost impossible to extract value. In these circumstances, any ambitions leaders have for their data assets will almost certainly remain unrealised.

Instead, organisations need clear insights into what data exists, where it resides, and how it can be used to enable better decision-making and governance. From a technology standpoint, these capabilities depend on vendor-neutral data storage and management solutions that ensure seamless integration across today’ s hybrid IT environments. For example, AI-driven visibility and automation technologies are increasingly integrated with robust and scalable storage infrastructure required to pursue data-centric objectives. This functionality must be delivered in a way that scales with changing requirements without the need for wholesale technology updates or unexpected costs.

2. Leverage AI and automation

Looking at AI more closely, it can be applied to various critical requirements. First, effective data management is essential to ensure GenAI models can deliver accurate and meaningful insights. Organisations that fail to focus on this crucial facet of AI

70 www. intelligentdatacentres. com