Unstructured Data Storage Considerations for AI Innovation

Data is the lifeblood of the digital age, and according to IDC, enterprises generate 80% of their data in an unstructured format. In the race for competitive advantage, the finish line belongs to those that can effectively extract value from this information. This business intelligence includes videos, images, emails and webpages. Unlike structured data, these formats require different storage approaches. Despite being more difficult to store and access, this rich ocean of data is critical for fueling AI and encouraging growth.

Although businesses urgently need to use this data to innovate, most are operating within a challenging macro environment. As speed and agility become paramount, fluctuating energy costs make it difficult to guarantee a return on investment from spending decisions like storage. Knowing the potential within unstructured data, many leaders find themselves asking: How can I effectively store, manage and analyse this information while keeping costs to a minimum? Do I bring AI to this data or confront the cost and complexity of moving it?

Object storage

Given the huge percentage of data residing in an unstructured format, the reality is that a more efficient storage solution is often necessary. Traditional file-based systems struggle to keep pace with the sheer volume and diversity of this ever-growing dataset. Object storage provides a scalable and cost-effective alternative to traditional file-based systems by treating each data unit as an independent object with its metadata.

This architecture is ideal for managing vast amounts of content, including images, videos and sensor data. Market Research Future has predicted growth in the global cloud object storage market. This trend shows that more modern enterprises are adopting these flexible storage solutions to meet expanding data demands.

Likewise, as workloads increasingly become AI workloads, object storage enables more seamless integration. But before deployment, addressing data governance and security concerns are essential for maintaining both integrity and compliance.

Bring AI and data storage closer to the source through edge computing

Edge computing is transforming how organisations manage data and deploy AI, particularly where cost-efficiency and regulatory compliance are important. Edge computing brings computation and storage closer to the data source. This minimises latency, delivers real-time insights at a lower cost

and enhances security at the local level. Being closer to the source also makes it easier to comply with different data regulations such as GDPR within Europe.

Edge computation is crucial for industries with distributed operations, such as manufacturing, logistics and energy. In the energy sector, for example, edge computing is used to optimise resource allocation and improve grid stability by analysing data from smart meters and sensors in real-time.

However, successful edge adoption requires careful consideration of data layout, access controls and security protocols within the distributed architecture. Unlike centralised cloud systems, edge computing has a different and, in some sense, a greater attack surface when you consider the distributed nodes. Without robust security measures, sensitive data at the edge can be more vulnerable to breaches in some cases. While edge computing offers speed and efficiency advantages, it also necessitates more rigorous data management.

Data lakes for unstructured analysis at scale

Data lakes allow organisations to analyse both structured and unstructured data at scale. By using AI and machine learning, organisations can delve deeper into their data without the burden of complex preparation processes. To support modern AI workloads, open data lakehouse architectures that combine the flexibility of data lakes with the structure of data warehouses are a popular solution.

For example, in retail, AI-powered data lakes allow organisations to identify patterns and understand crucial business intelligence from unstructured data such as social media interactions. This helps leaders to understand behaviour and adjust marketing strategies to secure the best outcomes. Similarly, within manufacturing, AI-powered data lakes can read sensor data from production lines to predict equipment failures and optimise maintenance schedules. They can also use data lakes to track quality metrics in real-time, enabling proactive adjustments to ensure product consistency.

Again, however, strong security, governance and quality controls are vital for ensuring reliability and increasing the trustworthiness of the insights. Robust data management practices and access controls are essential to protect sensitive information and comply with regulations.

To remain competitive, businesses must tap into unstructured data and the insights it provides. While traditional storage systems struggle to handle the volume, velocity and variety of this data type, there are alternatives available. Object storage offers cost-efficiency and scalability, edge computing brings processing closer to the data source for real-time insights and improved compliance. Data lakes, particularly open data lakehouse architectures, provide the analytical power needed to extract meaningful value from both structured and unstructured data.

But the one thing each has in common is the need to prioritise security, governance and data quality when implementing these solutions. A robust data management strategy is essential in each case. Addressing these storage and management considerations could mean organisations experience greater access to their unstructured data to drive more tangible business outcomes in the age of AI.