The growth of data has been enormous in recent years, all thanks to the widespread use of technological systems in our everyday lives. However, it is essential to consider that not all the data that gets generated is equal. For example, the data from retail businesses will be different from the one you see on online platforms or social media. And this is where the battle of structured vs unstructured data comes into the picture.
In this article, we present a structured data vs unstructured data guide to help you understand the differences between the two. We also explain to you their advantages, disadvantages, and major use cases.
Table of Contents
Unstructured data is qualitative information you cannot handle using traditional methods and software analytics tools. For instance, it might flow from customer surveys or social media feedback in text form.
Respondents may provide subjective descriptions rather than precise numbers or dates and times as with quantitative measures. A structured dataset contains varying degrees of precision categorized by relevance to counting something specific.
It could be software subscriptions sold by a business store over time. In contrast, an unstructured set may contain any number of words relating closely together meaninglessly.
The idea of structured data is to standardize formats to make them user-readable. For example, when you open up a .csv file with Microsoft Excel, it recognizes the columnar information and displays accordingly for easy consumption.
It is unlike previously, where users had to guess which cell had numbers or text. The unstructured nature makes it more difficult, but tools like Google Sheets make it easier.
Structured data is less flexible than unstructured or semi-structured data because every entry in the schema must follow a strict set of rules. But this has its advantages, like searching for specific information quickly and easily without having to find multiple records that may not match what you’re looking for exactly.
All you need are fields filled with predefined types. Unstructured data is much more flexible and scalable than structured data.
It lacks the predefined purpose of unstructured, meaning you can store it in various file formats. However, that is subjective. Working with this type may be difficult sometimes, especially if there isn’t enough information available to work from.
The size of your data is something to take into consideration when you’re trying to store it. A picture with high resolution takes more space than an entire text file. So unstructured files are usually in lakes or storage repositories that can house unlimited amounts without getting compressed.
Though native applications still exist for storing these types. Businesses can also opt for cloud storage for both data types.
Unstructured data is difficult to analyze because it does not follow any particular formatting or structure. As such, you need to analyze unorganized text before determining its true worth. You can then use it appropriately through programs that process this information. Structured data has been the norm in analytics for quite some time.
It makes sense as structured formats offer more opportunities to explore and extract meaning from your information than unstructured sources do. However, this doesn’t always mean they’re better.
The lack of mature tools can make it difficult to use advanced features like predictive modeling or machine learning, which rely on knowing how different types behave when processed together.
At Gramener we believe in data and automation. Our goal is to enable business users to accelerate their decision-making with actionable insights from unstructured data.
We used the Named Entity Recognition (NER) Technique in support of Natural Language Processing (NLP) and Natural Language Generation (NLG) to innovate solutions for patient analytics in clinical trials.
Moreover, our NLP solutions have helped drug researchers reduce manual interventions in research and journal reviews.
Based on customer ratings and comments, Customer Journey Identification aids businesses in prioritizing initiatives. An NPS Analytics solution architecture with a Machine Learning model that determines NPS score using Customer Sentiment Analysis with an accuracy of 84 percent is shown below.
Gramener’s Customer Analytics solution solves unstructured data in the form of text from multiple sources such as social media, review websites, forums, etc. Moreover, this solution may help you detect characteristics of customer intimacy and curate compelling customer experiences throughout the journey.
Structured data refers to any information structured into groups before analysis for specific information needs. It could include names and contact details, bank account information, etc. You can also consider employee names stored in an Excel spreadsheet with their contact information and credit card numbers for payment processing purposes.
Structured data is the type of information existing in a format where you can organize and process it seamlessly. What makes structured format different from unstructured data? You can arrange structures neatly with rows linking together particular pieces to make up larger wholes.
You need a relational database system installed to access these databases. A tool like RDBMS is ideal as it allows you to search specific relationships between its parts without any problems. However, all the relevant information should fall within their defined boundaries. Let’s consider the analysis part now.
A data warehouse is essential to the analysis of large datasets. It is a central data storage system that enables easy analysis and reporting of data. You can also use the programming language SQL (structured query language) to handle warehouses and relational databases.
When we talk about structured vs unstructured data, the former has better advantages over the latter. For example, the major benefit is the amount of time taken to clean the unstructured data. Let’s find out a few more differences.
Structured data doesn’t always top the unstructured format. There are a few disadvantages that come with time and limit the use of structured data.
Here are some standard data tools, relational databases, and technologies used in structured data:
Structured data has a variety of use cases as it comes in an analyze-ready format. When you download structured data vs unstructured, it is readily available in a row & column format.
Unstructured data is a challenge for everyone as you cannot use the usual tools to process and analyze it. One way to manage such large volumes would be with NoSQL databases. The importance of unstructured data is immense as most of the information we see around us is unstructured.
Emails, text files, and social media posts are among the many types of unorganized information becoming more prominent as technology advances at an unprecedented pace. A good example would be video content on Snapchat or Instagram Stories, for example.
The content is not on the same topic but varies from person to person. It makes analysis much harder than if you had one large file with all information categorized.
Let’s take a look at some examples of unstructured data generated daily by people:
Some examples of unstructured data through tech systems include:
As structured data offers a clean way of analyzing and exploring insights, unstructured data is rather easy to put together. When it comes to structured vs unstructured data, the storage capacity also acts as a prominent factor for users. Let’s find out what makes unstructured data shine bright in the users’ eyes.
Here are the various disadvantages of unstructured data:
When compared with structured data, there are some standard data tools, relational databases, and technologies used in unstructured data:
As mentioned above, the battle of structured vs unstructured data is not limited to just pros and cons. Even the use cases are quite distinct.
Image Recognition: Retailers are taking advantage of image recognition technology by automatically recognizing what people want and efficiently getting them as an itemized list on the screen with just one tap.
Chatbots: Chatbots are a hot new trend in customer service. Using natural language processing (NLP), chatbots help companies boost customer satisfaction through comprehensive answers to customer questions.
Sound Recognition: Speech recognition with audio analytics allows call centers to better connect with customers. They use it to identify who is speaking and the emotion the customer might be feeling. It helps them provide an appropriate response or remedy their problems quickly.
Text Analytics: The use of text analytics has made it possible to examine warranty claims from customers and dealers. Businesses can also use it to elicit specific items needed for further clustering with the help of advanced algorithms.
Contact us for custom built low code data and AI solutions for your business challenges and check out unstructured data analytics solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
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