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.
Every business depends on data, and while it has become clichéd to claim it is ‘the new oil’, it increasingly serves as the fundamental foundation for decision-making and innovation.
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:
- 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.
- 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.
- Implement robust data governance and compliance policies
Businesses tend to view compliance in one of three ways. Some consider it as little more than a box-ticking exercise to avoid penalties, others view it as a risk management strategy to protect data and reduce security threats, while some utilise compliance to build a competitive advantage that fosters trust and drives innovation.
But whatever the perspective, regulatory compliance is now non-negotiable. Organisations must maintain structured policies to align with various domestic and international rules, with authorities better armed than ever to impose painful sanctions when breaches occur.
From ensuring proper data documentation and access controls to retention practices, best practice also hinges on continuous monitoring and auditing of stored data. Businesses should also be in a position to categorise data based on value, risk and relevance, ensuring that critical data is stored securely while obsolete data is archived or deleted. All of these factors make an important contribution towards good governance.
- Operationalise data strategy to embed it into business culture
On an operational and cultural level, businesses must focus on practical execution and ensuring that data policies, technologies, and governance frameworks translate into day-to-day operational success. Ideally, data from disparate sources will be integrated so users have a unified view that facilitates effective decision-making, such as the links between customer trends, inventory and finances.
With the appropriate data hardware and software systems in place, organisations can then set out the methods required to ensure data is accurate, organised and subject to proper governance. This provides a trusted foundation on which to build a data culture and where each stakeholder can be confident that the data they access and use is reliable, secure and fit for purpose. This confidence enables teams to make data-driven decisions, innovate with analytics and derive meaningful insights that support the underlying business objectives.
- Future-proof infrastructure and processes
Data management should also be future-proof and integrate seamlessly with evolving business applications. By taking a modular approach, for example, businesses can scale specific components independently, ensuring flexibility and built-in adaptability to changing data management needs and emerging technologies.
This approach also ensures interoperability across diverse storage environments, preventing vendor lock-in and enabling businesses to adapt to future data growth without disruption. Additionally, embedding automation and AI-driven data governance can enhance operational efficiency, proactively addressing security risks and compliance requirements as the regulatory landscape inevitably evolves even further.
Each of these components shares the requirement for effective, affordable and high-performance data management technologies. With these capabilities in place, businesses can move forward with high levels of confidence in their approach to data management no matter what the future brings.
