What is the difference between structured and unstructured data—and why should you care? For many businesses and organizations, such distinctions may feel like they belong solely with the IT department dealing with big data.
While there is some truth to that, it's worthwhile for everyone to understand the difference, because once you grasp the definition of structured data and unstructured data (along with where that data lives and how to process it), you'll see how this can be used to improve any data—driven process.
Sales, marketing, operations, human resources—all of these groups produce data. Even the smallest of small businesses, such as a brick—and—mortar store with physical inventory and a local customer base, produces structured and unstructured data from things like email , credit card transactions, inventory purchases , and social media. Taking advantage of the data your business produces comes through understanding the two and how they work together.
Structured data is data that uses a predefined and expected format. This can come from many different sources, but the common factor is that the fields are fixed, as is the way that it is stored (hence , structured). This predetermined data model enables easy entry, querying, and analysis.
For example, consider transactional data from an online purchase. In this data, each record will have a time stamp, purchase amount, associated account information (or guest account), item(s) purchased, payment information, and confirmation number. Because each field has a defined purpose, it makes it easy to manually query (the equivalent of hitting CTRL+F on an Excel spreadsheet) this data. It's also easy for machine learning algorithms to identify patterns—and in many cases, identify anomalies outside of those patterns.
Structured data drills down to established and expected elements. Time stamps will arrive in a defined format; it won't (or can't) transmit a time stamp described in words because that is outside of the structure. A predefined format allows for easy scalability and processing, even if it's ultimately handled on a manual level.
Structured data can be used for anything as long as the source defines the structure. Some of the most common uses in business include CRM forms, online transactions, stock data, corporate network monitoring data, and website forms.
Just as structured data comes with definition, unstructured data lacks definition. Rather than predefined fields in a purposeful format, unstructured data can come in all shapes and sizes. Though typically text (like an open text field in a form), unstructured data can come in many forms to be stored as objects: images, audio, video, document files, and other file formats. The common thread with all unstructured data is a lack of definition.
Unstructured data is more commonly available (more on that below) and fields may not have the same character or space limits as structured data. Given the wide range of formats comprising unstructured data, it's not surprising that this type typically makes up about 80% of an organization's data.
Media files are an example of unstructured data. Something like a podcast has no structure to its content. Searching for the podcast's MP3 file is not easy by default; metadata, such as file name, time stamp, and manually assigned tags, may help the search, but the audio file itself lacks context without further analysis or relationships.
This also applies to video files. Video assets are everywhere these days, from short clips on social media to larger files that show full webinars or discussions. As with podcast MP3 files, content of this data lacks specificity outside of metadata. You simply can't search for a specific video file based on its actual content in the database.
In today's data-driven business world, using both structured and unstructured data is a good way to develop insight. Let's go back to the example of a company's social media posts, specifically posts with some form of media attachment. How can an organization develop insights on marketing engagement?
First, use structured data to sort social media posts by highest engagement, then filter out hashtags that aren't related to marketing (for example, removing any high-engagement posts with a hashtag related to customer service). From there, the related unstructured data can be examined—the actual social media post content—looking at messaging, type of media, tone, and other elements that may give insight as to why the post generated engagement.
This may sound like a lot of manual labor is involved, and that was true several years ago. However, advances in machine learning and artificial intelligence are enabling levels of automation. For example, if audio files are run through natural language processing to create speech-to-text output, then the text can be analyzed for keyword patterns or positive/negative messaging. These insights are expedited thanks to cutting-edge tools, which are becoming increasingly important due to the fact that big data is getting bigger and that the majority of that big data is unstructured.
Today, data is generated from many different sources. Let's look at a midsize company with a standard ecommerce setup. In this case, data likely comes from the following areas:
And there can be many more sources of data. In fact, the amount of data pulled by any company these days is staggering. You don't have to be a big corporation to be part of the big data revolution. But how you handle that data is key to being able to utilize it. The best solution in many cases is a data lake.
Data lakes are repositories that receive structured and unstructured data. The ability to consolidate multiple data inputs into a single source makes data lakes an essential part of any big data infrastructure. When data goes into a data lake, any inherent structure is stripped out so that it is raw data, making it easily scalable and flexible. When the data is read and processed, it is then given structure and schema as needed, balancing both volume and efficiency.
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