Data is everywhere, and it’s growing faster than ever. There’s broad agreement that most of the data flowing in and out of organizations – around 80% – is unstructured. That means it’s difficult to store, challenging to analyze, and even harder to protect.
Think of all the data your business generates on a daily basis. From emails and messages, to text, video, and audio files, right through to sensor data from Internet of Things (IoT) devices. On premise, or in the cloud, there’s a lot of unstructured data floating around.
In this blog post, we shine a spotlight on unstructured data, what it is, some common examples, as well as tools you can use to keep it safe, secure, and private.
What Is Unstructured Data?
Unstructured data describes any dataset which cannot be easily stored in a structured relational database. Without a defined data model, unstructured data is generally referred to as being qualitative as opposed to quantitative.
Data that is unstructured can have its own internal structure, but it doesn’t conform to a defined schema. The most common type of unstructured data is text. Text can take a multitude of forms, from documents and presentations to call transcripts and social media posts.
But unstructured data doesn’t have to be text based. Photos, videos, and audio files are all considered to be unstructured data. It also includes machine generated content, such as website log files.
How Is Unstructured Data Stored?
Unlike structured data, which resides in a relational database and is easily managed via Structured Query Language (SQL), unstructured data is stored in a NoSQL or non-SQL database.
A non-SQL database keeps everything within a single structure, so it handles storage and search functionality differently to a relational database. Unstructured data can also be held in a data lake — a repository which stores data in its raw and original form.
Structured Vs Unstructured Data
Structured Data
Stored in a relational database or data warehouse, structured data is easy to search using SQL. Its data points are related to each other through rows and columns, and it uses pre-defined fields. Structured data is categorized, making it much easier to input, extract, and compare information. It’s traditionally found in business and finance systems, which require consistency and conformity to rigid formats.
Unstructured Data
Stored in a non-relational database or data lake, unstructured data is more difficult to search because it’s held in a variety of formats. Whilst basic software might enable a simple content search, deeper search and analysis of unstructured data requires much more sophisticated tools. Unstructured data is increasingly used for Business Intelligence (BI) and analytics.
From a business perspective, structured data gives a top-down view of customers, whereas unstructured data provides deeper insights into customer behavior.
What Is Semi-Structured Data?
Semi-structured data is essentially unstructured data, like photos, with the addition of metadata and tags. By identifying certain characteristics or elements of the data, it’s possible to bring a certain amount of order to the chaos. Semi-structured data can be organized, grouped, and even put into a hierarchy. This makes it easier to catalog, search, and analyze than unstructured data.
Unstructured Data Examples
As we’ve seen, there is a huge variety of unstructured data types. Here are some common examples of unstructured data:
- Email and messaging: Whilst emails and messages can be categorized by things like subject or sender, their content is unstructured.
- Social media: It might be organized by hashtags, but the content of social media posts – text, video, images – is also considered unstructured data.
- Customer feedback: Taking many forms – reviews, surveys, social posts – customer feedback provides a wealth of information in unstructured formats.
- Media files: Like other examples, tagging and metadata can turn videos, images, and audio files into semi-structured data, but the content remains unstructured.
- Documents: Wherever there’s text, there’s unstructured data. From contracts to keynote presentations, business documents contain a wealth of data.
- Webpages: They may contain structured metadata and code, but webpages contain a wealth of unstructured data, from text to embedded media.
- Satellite imagery: From weather and topography to military positions, satellite imagery is another form of unstructured data.
The Importance Of Unstructured Data
Unstructured data is growing in importance. That’s because most of the data we produce today is unstructured. Unstructured data is less useful for financial or transactional functions – those applications are the domain of structured data. Instead, it adds value in other areas:
- AI: Large sets of unstructured data can be analyzed by AI and Machine Learning to provide fresh insights. For example, chatbots use text analysis to find answers.
- Data mining: Customer analytics, customer experience, and even insights into customer sentiment can all be enhanced by unstructured data.
- Business Insights: Collecting and analyzing all the available data can provide deeper and richer insights into how an organization is performing.
- Predictive analytics: Drawing upon a wealth of unstructured data, business can spot market trends or changes ahead of time.
For these reasons, unstructured data is a valuable business asset, and one worth protecting.
Unstructured Data Tools
There’s no denying that leaving large volumes of unstructured data unprotected makes your organization vulnerable to a cyberattack or security breach.
Doing nothing is not an option. It’s only a matter of time before sensitive data becomes compromised. Sensitive data needs protecting, to prevent fraud, and maintain privacy.
ABMartin offers complete data management solutions which help you mask sensitive data held in both unstructured and structured formats. So you can feel fully data confident.
We can help keep sensitive data safe. Even when it’s unstructured. Find out how
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