Unstructured Data and Management – Overview

To explain it in very generic terms, unstructured data is information which is not organised or immediately interpretable. It’s often text-based, although it can include images, numbers, dates, and other details which can be useful to a business, and which can be valuable for AI initiatives in the business.

Typical unstructured data examples include:

• Images downloaded to your computer or device from online resources
• Documents saved at random in a folder
• Communication data from mobile devices, social media, or emails
• Meeting notes and agendas
• Scanned documents like client information or receipts stored on your company computer system

At this point you may be wondering how or why these examples of unstructured data may be important to a business.

Well – for starters, unstructured data can be a security risk to an organisation, adversely affecting its productivity and efficiency. The longer your company data is left unstructured, lying around in different folders on multiple devices and systems, the higher the likelihood of locating specific information being difficult. You may want to do this to control access rights, for example, or to manage specific protocols across departments regard the storage, processing, and maintenance of data.

And, since unstructured data is usually difficult to access, it can be a liability waiting to manifest in case you face an audit or lawsuit.

Best practices to manage unstructured data + examples of unstructured data

The rise of many technologies, including AI, ML, NLP, and IoT, has led to a boom in unstructured data across multiple industries, where businesses are utilising it to edge ahead of the competition, improve the employee and customer experience, cut costs, and more.

If we take the automotive industry, for instance, where manufacturers are equipping their products with a broad array of sensors and devices which can collect data on multiple variables, including driving habits and engine performance – we’ll come to know that the data these systems use is often unstructured. So, it’s not organised in any specific order, but the unstructured text, images, or videos, for example, are helping to improve the driver experience in this use case.

To further build on this specific example of unstructured data, car manufacturers are now relying even more on unstructured data to acquire insights into specific customer behaviour, habits, preferences, and patterns – to improve not just the overall customer experience, but especially product design and performance.

One common example that comes to mind is the use of algorithms to power self-driving or autonomous vehicles which demands a mix of video, image, sensor data, and graph data, among other things. All of this is coming from raw, unstructured data, which cannot be stored or accessed through a storage device.

To give you broader examples of how unstructured data management solutions may be used across different industries, consider the following:

Medical research – Unstructured data like research papers, medical records, and clinical trial data can be analysed using NLP and ML algorithms in order to identify trends and patterns which may help healthcare providers make new discovers and uncover new insights to fuel their innovation or medical breakthroughs.

Image video and analysis – Unstructured data like images and videos may be analysed through computer vision techniques to identify people, objects, or other ‘points of interest’ within them.

Customer sentiment analysis – Unstructured data like product reviews, customer feedback from emails, or social media comments can be analysed through NLP techniques to better understand overall customer sentiment and identify trends as well as patterns which may help businesses improve their products and services.

Content recommendation – Unstructured data like browsing history, social media activity, and user preferences may be analysed by your unstructured data management solutions provider by using ML algorithms to offer more personalised content recommendations to users.

Fraud detection – unstructured data like email communications, transaction records, and web logs can be analysed while managing that unstructured data where, again, ML algorithms may be used to identify patterns and anomalies which may help to uncover fraud or other suspicious activities.**

However, the above are some very basic everyday use cases of how different types of unstructured data may prove to be advantageous for businesses. We will definitely shed more light on the unique examples of unstructured data across different industries later on in the article.

For now, we must ensure that we have a firm understanding of what unstructured data is and how it can be a game changer when we leverage it correctly. To gain a deeper understanding around this specific topic, we should quickly explain how managing unstructured data is different form structured data.

Key differences between structured and unstructured data

Structured data and when it is typically used

Structured data, as the term implies, is data which in organised into a specific and interpretable structure or format, making it easy and convenient to store on a device, and then access it later or analyse it. You will find that this kind of data is usually stored in a data warehouse or internal company database, carrying a clearly defined set of rules in terms of how it is organised.

It’s actually quite easy to transform structured data into numerical data, so that it can be used to train and also evaluate ML models – significantly easy compared to unstructured data, anyway.

Structured data includes:

Graph data – Data is presented in a network or graph structure, where nodes and edges link the different data points together. Graph data is most commonly used in fraud detection, social networks, and recommendation engines.

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