It is a common practice for most businesses today to rely on data-driven decision-making. Businesses collect a large volume of data and leverage it to perform an in-depth analysis of their customers and products, allowing them to plan future Growth, Product, and Marketing strategies accordingly. In this era of Big Data, businesses are, however, generating huge volumes of Unstructured Data.
Unstructured Data Definition can include various forms of data storage, including audio, video, text data, sensor data, imaging, etc. Until recently, businesses found it hard to analyze Unstructured Data because of the immense resources required to go through it manually.
However, with the advancements in Big Data Analysis and Business Intelligence, it has now become much easier for companies to seamlessly derive insights from Unstructured Data. Hence, companies can now make data-driven decisions from powerful customer insights. This article will provide you with an in-depth understanding of what Unstructured Data is and how it can be analyzed.
Table of Contents
What is Unstructured Data Definition?
Unstructured Data (also called Qualitative Data) refers to data that does not have any predefined structure. Typically, Unstructured Data is text-heavy, such as Social Media conversations and open-ended survey responses, but it also includes audio, videos, and images.
The high volumes of Unstructured Data can be attributed to the increasing use of digital services and applications. Estimates show that 80-90% of data generated by companies is unstructured, and it continues to grow at a high rate each year.
Unstructured Data Definition contains facts and figures, but it isn’t easy to analyze. Due to its unstructured nature, it cannot be stored in any Relational Database Management Systems (RDBMS) that are primarily used for analytical purposes. Unstructured Data can be more valuable to companies than Structured Data if appropriately analyzed. It can be used to derive insights that would have ideally not been easy with numbers and statistics.
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Examples of Unstructured Data Definition
Any data that does not have a recognizable structure is Unstructured Data. The most common examples of Unstructured Data are as follows:
- Emails: The body of the Email does not follow a predefined format.
- Photos.
- Text files.
- Video files.
- Web Pages and Blog Posts.
- Audio files.
- Social Media sites.
- Call Center Transcripts / Recordings.
- Presentations.
- Open-ended Survey Responses.
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Advantages of Using Unstructured Data Definition
Unstructured Data is considered to be an untapped resource for most modern businesses. If appropriately managed, Unstructured Data can give numerous insights that can help businesses make informed data-driven decisions. This means that organizations should look for ways to collect and process Unstructured Data properly to help them make critical business decisions and prosper even in highly competitive environments.
Machine learning technology has made it much easier for companies to analyze Unstructured Data quickly and accurately. These companies use technological advancements like Natural Language Processing (NLP) and Artificial Intelligence (AI) to understand Unstructured Data. This saves companies from having to do repetitive tasks like sifting through the data manually.
Businesses that analyze their Unstructured Data can benefit in the following ways:
- Improved Customer Experience: Companies can use the insights gained from Unstructured Data to improve the customer experience. Analyzing Unstructured Data may mean monitoring Live Chats, Emails, Customer Support tickets, and Social Media posts in real-time. This can help businesses know how to serve their customers better for an improved User Experience.
- Identify Market Gaps: Analyzing Unstructured Data can help a company identify new and untapped opportunities in the market. This is possible by monitoring their competitors’ Social Media posts and reviews and comparing them with their own metrics. By doing this, businesses can know what works well and what doesn’t work for their competitors. Such insights can give businesses ideas on the areas to venture into in the market.
- Listen to Customers: By the use of Artificial Intelligence (AI) technologies, businesses can read through a large number of emails and open-ended customer surveys. They can also track unsolicited feedback from online reviews, surveys, blogs, etc. This can help the business know what their customers expect from their brand and customize their products to meet the customer’s needs accordingly.
Disadvantages of Using Unstructured Data Definition
The following are the disadvantages associated with the use of Unstructured Data Definition:
- Storage: Most businesses are generating huge volumes of Unstructured Data, running up to terabytes in size. Handling such data is a complex process as more resources are required for storage and computation.
- Complex Indexing: Indexing Unstructured Data is a difficult and error-prone process due to an undefined structure and no pre-defined attributes. As a result, analysis of Unstructured Data sometimes does produce very accurate insights.
- Processing: Most Data Analysis tools were designed for processing Structured Data. This means that businesses have to go through many steps to transform Unstructured Data into a form suitable for analysis.
Analyzing Unstructured Data Definition
Initially, there weren’t many reliable techniques for the analysis of Unstructured Data Definition, and hence, it had to be done manually. Today, there are a wide variety of Data Analysis tools that use robust Machine Learning algorithms for the analysis of Unstructured Data Definition. Users can perform an analysis of Unstructured Data by implementing the following steps:
Step 1: Determining the End Goal
First, you should define a set of clear goals. The goals should help you understand what insights you need to derive from the Unstructured Data Definition.
Step 2: Collecting Relevant Data
There are numerous data sources available from where you can extract or collect the necessary data. However, you may need to focus on just one channel, such as Social Media posts, Customer Reviews, Surveys, etc. So collect relevant data based on your area of focus.
Step 3: Cleaning Data
This will make it easier for the Data Analysis tool to process the Unstructured Data Definition. You also have to remove noise and outliers from the data and reduce it into smaller and manageable pieces.
Step 4: Implementing the Necessary Technology
Other than the implementation of Data Analysis tools, a lot of effort is required to extract insights from Unstructured Data. You will need data storage tools such as NoSQL databases and Data Visualization tools like Tableau, Microsoft Power BI, Google Data Studio, etc.
Conclusion
This article provided you with a comprehensive understanding of Unstructured Data Definition, its advantages, and disadvantages, along with the steps involved in Analyzing Unstructured Data.
The data to be analyzed, however, has to be imported from numerous sources. Setting up a data pipeline from all your data sources would require immense engineering bandwidth and resources in developing and maintaining the pipeline and ensuring high data accuracy. Businesses can instead use automated data integration platforms like Hevo.
Hevo helps you directly transfer data from 100+ data sources of your choice to a Data Warehouse or desired destination in a fully automated and secure manner without having to write any code or export data repeatedly. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.
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Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects. He has written more than 150+ blogs on databases, processes, and tutorials that help data practitioners solve their day-to-day problems.