4 years ago

Structured Data vs. Unstructured Data

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Structured and Unstructured Data from social media posts.

With big data, enterprises now find themselves working with both structured and unstructured data. What are these and what’s the difference between them?

It’s important to be familiar with structured and unstructured data because it’s not something that only big data guys are using. In a competitive organization, all departments are data-driven. All departments generate data.

Structured Data

Like the name says, structured data uses an expected and defined format. The fields used for structured data is fixed, and it is stored as such. One of the best examples of structured data are those that come from online forms. When a visitor answers those forms, the system will generate a record number, along with the answers inputted by the user.

Similarly, online purchases will have a timestamp, the amount of everything that was bought, account information, items purchased, confirmation number, and the buyer’s financial details.

If you’re using a medical device, then that gadget also generates structured data. For example, an EKG will show the time of reading and electrical activity of your heart at the time.

Structured data is very easy to work with. For instance, when it comes to data entry, you can just find the correct field and enter the data. It’s also very easy for machine learning algorithms to identify trends, abnormalities, and other patterns in the data.

With structured data, everything is expected and established. The timestamps will follow a certain format, names will have only letters, while email addresses will have @ signs. Even if you do process structured data manually, it will be very easy.

Unstructured Data

Unstructured data is the exact opposite of structured data, and it will come in all formats, shapes, sizes, and forms. Unstructured data are mostly text, but there are instances when it would include images, video, documents, audio, and other types of files in different formats. It doesn’t have the definition you see in structured data and the fields are not the same.

The thing is, unstructured data is what most companies have a lot of. Around 80 percent of the data you have are unstructured. It may be more difficult to analyze, compile, and prepare, but you can’t disregard it.

An excellent example of unstructured data is an organization’s social media posts. Each post you have on Facebook or Twitter will have both structured and unstructured data. For instance, the number shares, views, likes, and the hashtag you use are all structured. There is a predefined purpose for each of these.

However, your posts are also unstructured. You can share a status message today and then share a video tomorrow. You can store all your posts in a file or repository, but if you ever need to find something, searching for it can be a pain. It gets even more challenging when you have to connect a particular post to certain metrics, like when you’re trying to find out which of your posts have the most likes.

What’s more, you cannot know what a particular post is about until you read or examine it.

Another example of unstructured data is audio and video files. Imagine an hour-long video or audio that talks about different things. There is no way for you to know what a particular media file contains until you watch or listen to it. These files will have metadata in it, and the file names might help you make searching easier. But most of the time, you’re stuck with having to go through these media files one by one.

Structured and Unstructured Data: How can they work together

To see how structured and unstructured data work together, let’s continue looking at the social media posts example.

You can use structured data to sort all posts by the highest number of views, engagement, shares, or likes. You can then filter out the hashtags that are not related to your search. For example, taking out hashtags that are not related to sales and advertisement.

After filtering out the non-sales posts, you can now use the unstructured data that are related to each one of your posts. For instance, you can look at the top 10 percent of your posts with the highest engagement and then look at the content. From this, you can know if videos are better than images when it comes to engagement. What types of status updates do people share the most? You can practically uncover a lot of insights on how your social media posts have performed in the past. You can also get insights on what you can do better, or what types of activities you can do more of.

In the past, you can only do this kind of sleuth work manually. But today, you can rely on both artificial intelligence and machine learning to do the work for you. Unstructured data stored in audio files undergo natural language processing and a transcript is created using speech to text technology. You can now analyze transcript using keyword patterns, positive or negative messages, or sentiment.

What are your possible data sources and where does all of these go?

Today, a business can capture and source data from just about everywhere. You get data from client transactions, customer accounts, feedback forms, sales forms, logistics tracking, inventory, internal employee data, search engines, social media and marketing engagement, and so on.

You can easily get these data, and the typical business gets lots of information from everywhere. It’s true that all businesses can join the planet’s biggest corporations in the world of big data. The only difference is not whether you can hoard lots of amounts of data, but what you do with this information.

You need a data lake, which is considered to be the best way to store all data your company gets. These are repositories that receive both unstructured and structured data.

When you deal with big data, you will need to be able to bring together various data inputs into a single repository. The data lake then becomes the single source if you need to prepare, process, and analyze your data. Data lakes make your data scalable and flexible. It can give your data schema and structure when necessary. This makes volumes and volumes of data easily stored and managed.

As more and more data are sourced, you will appreciate how data lakes are efficiently storing everything. It also helps in making way for you to use artificial intelligence and machine learning.

And when it comes to data lakes, you can never go wrong with a cloud-based service. With your data on the cloud, you can easily prepare for your big data initiatives today and in the future.

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See how Oracle Big Data can help you. Call Four Cornerstone today at 1-(817) 377-1144.

Photo courtesy of TT Marketing.

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