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What Is Unstructured Data? Definition, Benefits and Examples
Most of the data in the world comes from “unstructured” sources. These are sources and types of data that don’t easily map onto traditional data formats.
Unstructured data — and semi-structured data, for that matter — can’t easily be transferred to a spreadsheet or other predefined data model. Instead, it needs intelligent processing in order to extract useful information.
It’s typically worth doing that intelligent processing, too, because unstructured data often contains some vital insights. Here at Databricks, we talk about the 4Vs of unstructured data: Value, Value, Value, and Value.
Unstructured data can give nuanced, granular and big-picture insights. Brands that engage with this kind of data are sure to boost their business intelligence in important ways.
Databricks can help you store, process, manage and analyze unstructured data. Through artificial intelligence (AI), and particularly things like machine learning algorithms, natural language processing (NLP) and data analytics models, we can extract meaningful data from unstructured sources and present you with the intelligent insights you need to get the best out of your data.
But first, let’s tell you a bit more about unstructured data. Here, we’ll take you through everything you need to know about it, from what it is to how it can be used and how Databricks will help you to engage with it.
What is the definition of unstructured data?
Unstructured data is information that doesn’t fit in a standard format (one that can be easily analyzed, stored and joined with other data formats). It comes in formats and contexts that make it hard to store and process via conventional digital databases. Essentially, it doesn’t reside in a relational database management system (RDBMS) or other recognized transactional system — think a SQL database and the like.
Common examples are text files (like the content of this blog) and images. The information contained in these formats can’t be shipped straight to a database for unstructured data. Instead, it needs to be either extracted and made to fit traditional digital schemas, or managed and analyzed by new kinds of technology.
Up to 90% of all data generated is unstructured. What’s more, it can be very valuable. For example, social media posts, audio files, and videos can yield a lot of vital information about your audience, the mood of the market, current trends, and more.
All in all, it’s very useful to have a way to capture, store, manage and analyze unstructured data.
Structured vs. unstructured data
Both structured and unstructured data have their advantages. Their main differences lie in their schemas, the way they are stored and what they can be used for.
Structured data is quantitative, while unstructured data can be qualitative. Structured data is closely formatted in rigid and consistent ways (think standardized data sets in data warehouses, for instance) so that it can be processed and analyzed by any compatible program or analytics tools.
Think, for example, of accounts data. An accountant takes down transaction data in a formulaic way that can be understood by any other accountant or accounting software. The data is formatted in a consistent manner that can be easily transferred to spreadsheets and databases. It can then be processed and analyzed by any person or program familiar with the schema.
Structured data is usually stored in RDBMS. Spreadsheets are a form of relational database. For example, a spreadsheet storing customer data will store data points in related columns. The viewer can easily see a customer’s name, location, phone number, etc., by running their eye along the relevant row.
Structured data is easy to read, analyze and apply to your digital processes. For example, it’s a lot harder to streamline unstructured data workflows than it is structured ones. With structured data, everything tessellates neatly into a predefined format, making data management and visualization straightforward. For unstructured data storage and analysis, you may need specialist tools to work it into your systems.
Let’s take a look at some of the benefits and drawbacks of unstructured data.
Benefits and drawbacks of unstructured data
Benefits
Not restricted to one use case
Structured data, with its more rigid formatting, can often only be used for its intended purpose. That isn’t the case with unstructured data. This type of data is a lot more flexible, generally more objective, and can be used for a wide variety of purposes.
Let’s return to the accounting example for a second. While you can draw inferences from accounts data, you can’t use it to prove beyond doubt anything other than money in, money out, and profit patterns. For example, you could use accounts data to prove that profits have been declining, but you could not use it to say why profits are declining.
However, unstructured data, like transcripts of customer support conversations or even social media reviews, could give a lot more insight. With techniques like sentiment analysis, you can use this data in various ways to monitor performance, pick up on pain points, pulse-check your audience, spot complaint patterns, and more.
Wider possibilities within the data
Flexible formatting is the key to the value of unstructured data (though it can also be part of the challenge!). The lack of strict guardrails and schemas means that a wide range of data can be stored, and this can be kept in a diverse range of formats. While this can make it harder to extract the data, it also means it can help with a number of business applications.
Easy to store
Unlike structured data, unstructured data isn’t restricted to RDBMs for storage.
This is, admittedly, a bit of a mixed blessing. Some unstructured data (Microsoft Word documents, for example) is very easy to store and doesn’t take up much space. Others (lengthy audio or video files, for example) are larger and take up more storage space.
However, all in all, you have a lot more options when it comes to storing unstructured data than you do with strictly formatted structured data. For example, you can store it on a scalable cloud data lake, like the Delta Lake on Databricks.
Easier to interpret for nonexperts
The predefined nature of structured data means that it often can’t be interpreted by someone without a data science background. Unstructured data, on the other hand, is usually more accessible.
For example, campaign performance metrics are hard to comprehend if you don’t have inside knowledge of the campaign, its KPIs and the data schematics. However, a text document can be read and interpreted by anyone who speaks the relevant language.
It is worth noting that unstructured data can be subjective. Different people may come to different interpretations. It differs from structured data in this way. For example, an art historian and a casual viewer are likely to see different things in a painting. However, anyone who can interpret performance data is likely to see the same trends and patterns in performance metrics.
Simple and fast to collect
Gathering structured data can be a long and drawn-out process. However, gathering unstructured data is as simple as taking screenshots or downloading a document.
In fact, your organization probably already has a lot of unstructured data. It is by far the most common kind of data, so you undoubtedly have plenty of it at your disposal. Collecting it is just a case of determining what’s useful and gathering it up.
However, it’s worth noting that in order to make sense of it at scale, it can involve quite a bit of work. You’ll need to put it into predetermined structures, or spend time creating a custom routine. But in terms of the initial collection — and being able to access specific data points at a glance — it can be much quicker.
Drawbacks
Requires specialized tools
Data tools like Excel can’t deal with unstructured data unless you pick it apart and enter the relevant information by hand. And even then you often have to force it to fit the format.
So, you may need to get specialized tools and software in order to manage and analyze it. Luckily, platforms like Databricks can provide the services you need to get the best out of unstructured data, including big data analysis.
Complex data which requires experience
Unstructured data is often more accessible than structured data, but it can also be complex. You may need to bring in experienced data scientists in order to analyze and interpret it properly.
9 unstructured data examples
Which of the following is an example of unstructured data? Email content, social media metadata, or a marketing video? Answer: all of them — and some.
Emails
Emails contain important information in a variety of forms, including text, images, and maybe even video and audio files.
Web pages
Web pages are a great repository of data both past and present. Your own archived web pages are great for comparison purposes, and can tell you a lot about how your brand is developing. Competitors’ web pages are fantastic for market analysis and honing your own USP.
Social media
Social media data is a goldmine, and we don’t just mean for user metrics. Social media posts can tell you a huge amount about your customer base, their likes, their interests, the things that are preoccupying them, what their pain points are, what they need, and so on. All of this helps you to connect to your audience in relevant and valuable ways.
Multimedia files
Video files, audio files, image files, and more can all contain vital information. But they can’t be managed and processed in the same way that you would manage and process structured data files.
Publications and listings
Industry publications and listings are very revealing about the state of the market. By applying unstructured data analytics to the data contained within publications and listings, you can get ahead of the competition and corner the market.
Survey results or responses
Surveys are a brilliant way to learn about your customers, but to get a real feel for what they think, you need to let them give unstructured responses — writing down their own experiences, for example.
Business and medical reports
Business and medical reports may contain vital data, depending on the industry you are in. The data within medical reports, for example, can save lives. However, reports don’t always fit neatly into structured data schematics.
Directories and publications
Directories and publications are very useful if you can extract the data they contain. That data is often in an unstructured format.
Customer reviews or feedback
Customer reviews, feedback and social media mentions are vital both for improving your service and boosting your brand. As such, they are a very valuable form of unstructured data.
Simplify most types of unstructured data with Databricks
In this day and age, brands need the ability to manage, store, process and analyze unstructured data. The amount of unstructured data available to organizations is growing exponentially, as is its value.
With Databricks, you can draw meaningful and actionable insights from almost any kind of data, including most forms of unstructured data. Databricks uses machine learning and AI to extract valuable insights from all your data and to process what’s useful.
Put briefly, Databricks simplifies unstructured data by structuring it. Our algorithms gather the useful data from all unstructured sources, collate it according to your own parameters and the kinds of insights you want to draw, and model it.
Ultimately, Databricks allows you to get all the benefits of unstructured data without the drawbacks. Our specialized platform helps you mine all the best data from unstructured sources with ease. Get in touch today for a demo.
FAQs about unstructured data
What is unstructured data used for?
Unstructured data has a variety of use cases. Because of its variety, it isn’t restricted to just one purpose.
For example, the data in customer service emails can be used to improve your product, give insights to your marketing team, examine the state of the market, improve your customer service team performance, establish common pain points, and much more.
What is an example of unstructured data?
Unstructured data is all around! Examples include word documents, emails, social media posts, images, videos, reports and survey results.
Where is unstructured data stored?
Unstructured data is often easier to store than structured data, as it does not need specialized formatting. However, to be effective it does need an organized and manageable storage system.
Databricks offers data lake storage, which is perfect for unstructured data. You can store, manage, process and analyze all your unstructured data from one intuitive platform.