Unstructured data can be very challenging to analyze. However, unstructured data comprises 80 percent of the data that organizations process on a regular basis.
Structured data gives you aligned, defined, and organized fields of data that you can easily transfer and analyze (given the right algorithms). But combing through unstructured data, which often comes from documents, digital media, and social media feeds, affords you no such luxury.
At the same time, this data can be extremely valuable. So what are best practices for making unstructured data manageable, and getting the most out of it?
How to Deal With Unstructured Data
Try these strategies to make the most of your unstructured data:
1. Work with a partner. If you feel overwhelmed by the possibilities contained within your unstructured data, and don’t have the technical expertise or experience necessary to manage it, your best option may be to work with a partner that specializes in cleaning, sorting, or analyzing unstructured data. Though not all businesses will have the budget necessary to pursue this option, it’s definitely the most efficient and the most convenient. Some specialized tools even allow you to automatically parse, sort, and analyze unstructured data—though this is a relatively new area of development.
2. Evaluate the value of your data, and clean your records. Not all unstructured data is worth analyzing, or even worth keeping. It costs money to gather and store your data, and even more money to clean those data into a format that’s capable of being analyzed. If the data are coming from a source that won’t yield much value for your organization, you should consider deleting it.
3. Take a random sample and create a “dictionary.” Analyzing the entire text file of your data manually is a virtually impossible task—or at least an incredibly time-intensive one. Instead, it’s better to take a random sample or stratified sample from the collection, and use that to build a “dictionary” that you can use to find similar patterns in the rest of the data. There are many ways to approach this, including using natural language processing or text analytics, but the end result is the same—creating a framework that can be used to sort or identify the rest of your data fields.
4. Clean the entire dataset. Your goal should be to take unstructured data and turn it into structured data. By using the framework you created from a random sample, you should be able to write a script that allows you to clean your entire dataset. Ideally, you’ll be able to classify and segment those data so you can analyze it easily in the future.
5. Analyze it. Assuming your data is properly structured and easy to digest, you can analyze those data and start making decisions based on the insights you gain. Once structured, you can treat your data like any other structured dataset you come across.
Prioritizing Structured Data
While unstructured data can be valuable, and is practically unavoidable in today’s data-rich environment, structured data is much easier to analyze. When possible, lean toward sources that enable you to start with clean, structured records from the outset. That way, you’ll be able to bypass the structuring process and head straight to analytics.
As big data-related technology grows more sophisticated, it’s going to be easier for businesses to structure and analyze unstructured data. In the meantime, work with a specialist or employ your own data structuring algorithms to milk every bit of value out of your unstructured sources that you can.