CIO OPINION 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, AIdriven 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 development risk falling foul of the classic‘ garbage in, garbage out’ scenario that was a feature of early computer programming and still remains valid to this day.
Secondly, AI and automation themselves play a key role in optimising data management by enabling businesses to implement automated rules around data access, retention and movement in line with compliance and security policies. AI-powered data governance also reduces the risk of manual human errors, and instead ensures consistency and compliance across all data operations.
The first priority should be to implement a scalable data management framework.
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