What is unstructured data and how to deal with it?

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Dealing with unstructured data is becoming an increasingly pressing issue with each passing day. The great majority of data generated in reality is unstructured, and unstructured data is critical to enhancing our knowledge of the world. While the study of organized data can assist us in understanding what is going on, it is the analysis of unstructured data that may expose the truth. According to an IDC analysis published by Network World, the amount of data that enterprises generate is likely to sextuple over the next five years. So there are many challenges to process unstructured data because it can not fit neatly in the columns and rows.

What is unstructured data?

A dataset that has not been structured in a prescribed manner is unstructured data. Textual unstructured data, such as open-ended survey replies and social media chats, is the most common type of unstructured data; but, non-textual data, such as photographs, video, and audio, can also be found. Geographical and IoT (internet of things) streaming data are two new types that are becoming more popular.

Because of the rising usage of digital apps and services, unstructured data is growing at an alarming rate. Despite the fact that structured data is crucial, unstructured data can be much more useful for businesses when properly examined. It has the potential to reveal a plethora of information that statistics and figures cannot explain.

How to deal with it?

Although Artificial Intelligence (AI) has started to take its part, some software has been developed that helps comb through data streams to find what you’re looking for quickly and efficiently. You can also try these strategies to get the most out of your unstructured data.

1.      Examine the worth of your information and tidy up your files:

Not all information is worth analyzing, and not all are worth storing. It costs money to collect and store your data, and it costs much more money to clean that data and convert it into a format that can be used for analysis. If the data originates from a source that is unlikely to be of any use to you,  you should consider removing it.

2.      Outsource the expertise:

If you’re intimidated by the possibilities in your unstructured data and don’t have the expertise or experience to manage it, working with a partner specializing in cleaning, classifying, or evaluating unstructured data might be your best option. Though not many firms will have the financial resources to explore this choice, it is unquestionably the most effective and convenient.

3.      Take a random sample and Clean up the rest of the dataset:

Manually analyzing the whole text file containing your data is nearly impossible. It’s preferable to take a random or stratification sample from the data collection and use it to create a “dictionary” of comparable patterns to process unstructured data.

It should be your goal to convert unstructured data into structured data. You should develop a script that cleans the complete dataset using the structure you developed from a random sample. You should be able to label and separate the data to evaluate it in the future readily.

4.      Structure it:

You may evaluate your data and make decisions based on the knowledge and insights you obtain if your data is correctly formatted and easy to comprehend. You can treat this data like any other organized dataset once it’s been structured.