In today’s AI-driven and digital landscape, the sheer volume of data being generated is staggering—roughly
400 million terabytes each day,. This influx comes from countless sources, ranging from social media interactions and business communications to logistics records and customer information. All of it must be stored somewhere, creating a growing challenge
for organisations.
Effectively managing this data while staying compliant with an increasingly intricate regulatory environment is no small feat, especially in the financial services industry. However, institutions that take a strategic approach—one that carefully balances
scalability with security—can successfully navigate these complexities.
A cornerstone of sound data management lies in proper data indexing and classification. Without a clear understanding of what information is being stored, why it’s retained, its significance, and who has access to it, managing storage efficiently becomes
nearly impossible. The consequences of poor data organisation extend beyond inefficiency; they can hinder regulatory compliance, making it difficult to produce accurate records for audits. Additionally, disorganised data limits a bank’s ability to capitalise
on emerging innovations, such as AI-driven analytics.
Taking a step back to build a well-structured data strategy is essential. By implementing strong indexing and classification practices, financial institutions gain clarity and control over their stored information—ensuring both regulatory compliance and
the foundation for data-driven innovation.
Success from organised data
When businesses index their data, they gain comprehensive knowledge about it, including details such as time, date, age, size, and creator. Classification further enhances this by identifying what the data is and determining how long it needs to be retained
based on the company’s relevant records policy. This approach has powerful implications for regulatory compliance, cost savings, AI insights, sustainability, cyber security and access control.
Cost savings are a significant benefit of data classification. By assigning data to the right storage tier, businesses can optimise both cost and access. By understanding what the data allows businesses to make defensible deletion decisions, so what to retain
and what to remove. These prevent indiscriminate storage and reduce unnecessary data bloat, ultimately cutting storage costs. In an era where data storage can be costly, this is a crucial consideration for businesses looking to manage their budgets effectively.
Compliance by data organisation
From a regulatory compliance perspective, proper data classification ensures that data is retained for the appropriate length of time, helping businesses avoid legal risks. This is particularly important in light of regulations such as GDPR, NIS2, and most
recently, DORA, which impose stringent requirements on data management and processing practices. By classifying data, businesses can ensure they meet these requirements and avoid hefty fines.
However, simply complying with each new data regulation as it emerges is not best practice nor is it strategic. This approach can lead to a reactive and fragmented compliance strategy, where businesses are constantly scrambling to meet the latest requirements.
This not only increases the risk of non-compliance but also consumes significant resources and can disrupt business operations.
Instead, businesses should focus on creating a standardised process for data governance. A robust data governance framework provides a consistent and comprehensive approach to managing data across the organisation. This framework can serve as a template
for regulatory compliance, ensuring that all data management practices are aligned with the highest standards and can be easily adapted to meet new regulations as they arise.
Ensuring AI success
AI needs no introduction currently as it grips conversations at both a technical and economic level. As businesses increasingly adopt AI, having a robust data management strategy becomes even more critical. Having Redundant, Obsolete, and Trivial (ROT)
data can often lead to hallucinations or to the sharing of private data in LLMs, potential reputational damage can follow if data is exposed, or inappropriately accessed.
Proper data classification and indexing are essential for effective AI systems. Organised and categorised data allows AI algorithms to identify patterns and relationships more easily, leading to precise predictions and recommendations. Indexed and classified
data enables AI to access structured, relevant datasets, leading to more accurate and actionable insights. This can drive informed business decisions and enhance overall operational efficiency.
Effective data classification and indexing also maintain data integrity and security. By segregating sensitive information, businesses can implement access controls and encryption, reducing data breach risks and ensuring regulatory compliance. This is vital
for AI, where misuse of personally identifiable information can have significant legal and ethical consequences.
Ensuring security
Ransomware campaigns and wiper attacks are common threats faced by cybersecurity professionals. In any successful breach, threat actors intend to take full command and control over corporate systems and associated backup data, and either ransom the data,
or delete it outright to maximise damage. Having proper data indexing and classification can improve access control, and help IT security teams truly understand the value of data that is ransomed or destroyed.
By restricting access to those personnel who need, or are authorised to see that data, businesses can minimise the risk of destructive data breaches permeating throughout the business and ensure that sensitive information remains secure. This is particularly
important in an age where cyber threats are becoming increasingly sophisticated. By moving towards a cyber resilient posture business will be in a greater position to safely recover from a cyber event without compromising themselves or their customers.
Frameworks for success
Implementing robust data governance frameworks is another critical step in balancing scalability and security. This includes establishing clear data privacy policies, conducting regular data audits, and training employees on data privacy and security best
practices. By fostering a culture of data hygiene across all levels and business units, businesses can ensure that data is managed responsibly and securely.
By implementing a standardised data governance process, businesses can streamline their compliance efforts, reduce the risk of non-compliance, and ensure that they are always a step ahead of new regulatory requirements. This proactive approach not only enhances
regulatory compliance but also improves overall data management, leading to better data quality, increased operational efficiency, and greater trust from stakeholders.
Utilising scalable infrastructure, such as cloud solutions and hybrid models, can also help businesses balance scalability and security. Cloud solutions offer flexibility and scalability while ensuring robust security measures. Hybrid models, which combine
on-premises and cloud solutions, provide a balanced approach that meets both scalability and security needs.
Enhancing cybersecurity measures is essential for protecting data. This includes using encryption to protect data at rest and in transit, implementing strict access controls, keeping systems and software up to date with the latest security patches, and the
use of threat detection and hunting capabilities. By adopting these practices, businesses can safeguard their data against potential threats such as ransomware.
In conclusion, a comprehensive strategy combining cutting-edge data protection technologies, strong governance frameworks, scalable infrastructure, and strict cybersecurity measures is needed to balance scalability and security in data management. Businesses
may maximise the value of their data while navigating the complicated regulatory environment by taking the lead in data indexing and classification, utilising scalable technologies, and cultivating an organisational culture of data hygiene.
