As of 2024, almost 50% of enterprises store more than 5 petabytes of unstructured data (which equates to an eye-watering 5 quadrillion bytes). Within this, lies a host of opportunities, from analytical gold-mines, to data-driven insights that could drive better decision-making overall.
And yet, many businesses are drowning in this data, without the structures in place to take advantage of a mountain of information. Worse yet, this mountain of ever-growing data is raising storage concerns, security risks, and more.
So how do you get the most out of unstructured data, while mitigating the challenges that come with it?
In this article, we tackle unstructured data - and outline how businesses today can tap into the information at their very fingertips.
We'll cover:
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What is unstructured data?
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Why is unstructured data valuable?
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Key challenges in managing unstructured data
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Best practices for managing unstructured data
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Modern solutions for storing unstructured data
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What's next? The future for unstructured data
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Support with unstructured data
Let's dive right in - what do we actually mean by "unstructured data"?
What is unstructured data?
In simple terms, unstructured data is any data that lacks a pre-defined format, schema, or model. For example, a business is likely to sit on a wealth of unstructured data, populated by company emails, text files, images, videos, audio files, sensor data, and more. Data like this won't be stored in a structured format (for example, a table database), but instead will require a more advanced method of storage and analysis.
For example, a company might possess a large dataset of images, which require a specially trained AI model to decipher at scale.
Why is unstructured data valuable?
Unlike structured data, unstructured data offers rich, contextual information. For example, an image can convey context, meaning, and minute details, in a way numbers in a spreadsheet simply can't. And yet, this presents a challenge. Gaining insight from a vast amount of unstructured data often relies on advanced technologies like natural language processing (NLP), machine learning, and artificial intelligence (AI).
Today, 80% of data in businesses is considered unstructured, with just 20% being structured - and, that figure is only rising.
Despite the opportunities this wealth of data presents - there are also a number of key issues. So much so, that 95% of businesses state that unstructured data poses a significant problem to their company.
So, what's the setback? And how do you get the most from unstructured data within your business?
Key challenges in managing unstructured data
From data storage issues, to navigating its sheer scale - unstructured data can cause a headache or two for business leaders. Let's dive into some of the most common ones.
Volume and variety of data
Unstructured data is, by its very nature, complex. And, with up to (and often over) 80% of business data being unstructured, the scale of that complexity can be mind-boggling. Worst yet, because this data doesn't fit into neatly pre-determined formats (for example, in a database or spreadsheet) navigating it can become almost impossible.
For all its opportunities - without the right structure in place, unstructured data can become an expensive, and at times, risky, storage issue.
Later, we'll touch on best practices for handling, and analysing, that data at scale.
Accessibility and analysis
Unlike the type of data you could navigate with a CRM, unstructured data is unusually challenging to, firstly, access, and secondly, analyse. Spread across images, video, text files, and more - unstructured data is likely littered across a business, poorly organised, or worse - buried and unfindable.
For businesses that have managed to collate their unstructured data, analysing it becomes a challenge of its own - with most traditional methods falling short. While advanced methods like natural language processing and AI are being used to gain insights from unstructured data, this remains a growing practice rather than standard, thanks to often prohibitive costs.
Storage and security
Perhaps one of the biggest challenges of unstructured data is the storage and security issues that come with it. As an ocean of information that's expanding by the day, the storage requirements of unstructured data can be lofty. This type of data is only going to continue to expand, and it's key for businesses to find a cost-effective solution - sooner, rather than later.
Similarly, as unstructured data grows within a business - so do its security concerns. Your text files, images, and videos can contain protected employee information, sensitive company details, and more. Without the right data protection measures, this information can be breached, and expose your business to grave risk.
From regulatory and compliance obligations to the ever-looming threat of the GDPR, poorly managed data in a company can be a grenade waiting to go off.
Scalability
As unstructured data has grown over the years, the scalability of its management has become an increasing issue. Legacy systems are increasingly ill-equipped to handle the volume, variety, and complexity of unstructured data - and its becoming crucial for organisations to invest in scalable infrastructure that allows a business to grow with the data it creates.
So... how are businesses today managing unstructured data?
Modern solutions for managing unstructured data
From data storage requirements to analytical needs, managing unstructured data might seem overwhelming. However, handled correctly, and it can result in contextually rich customer insights, innovation-fuelling information for products, intelligent business-wide oversights, improved data security confidence, and more.
So, where should you start?
Metadata tagging
Let's start simple. The very first step towards organising your unstructured data should be in applying metadata tagging. This involves adding descriptive tags to your data, which in turn converts raw digital materials into searchable assets.
Natural language processing (NLP)
Next up, NLP. Natural language processing has the ability to autonomously analyse and interpret human language. By applying this to unstructured data, meaning can be gleaned from text-based data - whether that's emails, PDFs, reports, or more. By its very nature, NLP allows you to label, search, and analyse data - at scale.
Data lakes
A data lake allows you to securely store unstructured (and structured!) data at scale while enabling you to process it when needed. Unlike traditional databases, data lakes can more flexibly accommodate the needs of unstructured data.
AI and machine learning algorithms
AI-driven analysis of unstructured data is becoming increasingly popular, in part due to the models publicly available, and also - due to the increasing availability of bespoke in-house models. Many businesses are now training their own AI models, perfectly equipped for their use case, and the unstructured data they possess.
This allows businesses to categorise and analyse data at scale, minimize the risk for human error, and, in certain scenarios, reduce the overhead costs of human analysts. Common use cases here include company-wide analysis of financial information, categorisation of a product's sensor data, or the labelling and organisation of internal company data.
Data governance and frameworks
With great data comes great responsibility. By embedding robust data governance practices and clear frameworks from the outset, businesses can protect the quality, management, and security of their data.
Many businesses will set out clear policies and rules that define ownership over data, key responsibilities, compliance concerns, and company-wide expectations for the handling of data.
Modern solutions for storing unstructured data
Today, traditional storage methods just don't cut it when it comes to unstructured data. Ineffective at scale, costly, and often laggy, these legacy systems fail to bring real value into the business - and frustrate teams in the process.
Fortunately, solutions have emerged that allow companies to convert unstructured data, into a key asset. Let's dive right in.
Data lakes
We've already touched on the data lakes, and how useful they can be when managing unstructured data. When it comes to storage, data lakes are a particularly good option - thanks to their flexibility, cost-effectiveness, their ability to handle various data formats and sizes.
Cloud storage
Cloud storage has revolutionised many things within the modern business world - and the handling of unstructured data is one of them. Providing flexibility, scalability, cost-effectiveness, and, often, ease of use, cloud storage solutions (including Amazon S3, Google Cloud Storage, and Microsoft Azure) are perfectly designed to handle unstructured data.
Not only does this method reduce the costs associated with physical storage infrastructure, but it also allows businesses to approach data in a cost-effective, user-friendly, and scalable way.
Object storage
Object storage systems handle data as units, or "objects," which can include any kind of data – from documents and videos to social media content. Each object is tagged with metadata and a unique identifier, which simplifies the process of accessing unstructured data. This method is popular with large organisations, thanks to the ability to manage vast volumes of data.
AI and machine learning integrations
AI and machine learning can be particularly helpful when it comes to storing unstructured data, with the ability to autonomously label, categorise and analyse the data at hand. This allows companies to annotate, understand, retrieve, and organise the data they possess.
What's next? The future of unstructured data
The volume and variety of unstructured data are rapidly accelerating, and for the modern business, the management of it will be make or break.
Thanks to the digital transformation of the commercial world, almost every corner of a company collects data - and those who understand how to handle it will have more than a competitive advantage. Data compliance, robust governance, rich insight, the list goes on... the future survival of a company lies in the intelligent use of its unstructured data.
As technology evolves to handle this data, we can expect to see the increasing popularity of AI and machine learning algorithms, cloud storage innovations, smart object storage systems, and hybrid infrastructural environments that prioritise the scalability and security of this data.
Similarly, we can expect data protection regulations to tighten, with the responsibility of compliance falling directly onto businesses and their teams.
Support with unstructured data
Sitting on a treasure trove of unstructured data? Held back by legacy storage systems that make retrieval a challenge? Facing eye-watering infrastructure bills that could be avoided?
At Lyon Tech we're experts in unstructured data, and work closely with businesses to embed best practice structures that prioritise security, flexibility, and cost-effectiveness.
Addressing your unstructured data? Discover how we can help.
