Structured Vs Unstructured Data: Major Differences for Business Users

structured data vs unstructured data key differences
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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.

Structured vs Unstructured Data: Key Differences

structured data vs unstructured data key differences
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Courtesy: Curvearro

Difference #1: Nature of Data

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.

Difference #2: Formats of Data

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.

Difference #3: Models of Data

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.

Difference #4: Data Storage Options

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.

Difference #5: Ease of Use

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.

How Gramener Solves Unstructured Data For Top Clients

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.

Solving Unstructured Data With Machine Learning

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.

machine learning algorithm flowchart to solve unstructured text data to find out NPS score
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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.

What is Structured Data?

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.

Pros of Structured Data

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.

  • It Offers Increased Access to Tools: Structured data has been around for much longer, which means you have more tools for analysis. Data handlers also have many different products when deciding how they want their structured data handled. It might be something worth considering if you’re not sure what would work best.
  • ML Algorithms Understand it Better: There’s a lot of information in our daily lives that you can use for machine learning. Structured data might not seem like it at first, but structured data is one way to make sure this happens. Their specific nature allows easy manipulation and querying with your algorithms, so they work better than ever before.
  • Business Users Find It Easy to Use: The interactive data explorer makes it easy for anyone who understands the topic to use unstructured data. Users don’t need extensive knowledge of the different types of relationships between that particular set of data.

Cons of Structured Data

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.

  • A Lack of Storage Options: Data warehouses are the backbone of most businesses. They store all your company’s data but can be expensive to maintain. As they are on-premises systems with strict schema requirements, there is a need to update them regularly.
  • Has a Predefined Purpose: The value of structured data lies in its ability for easy understanding by users. However, you can only use it for the purposes intended. It limits the flexibility and usefulness across different situations.

Tools used in Structured Data

Here are some standard data tools, relational databases, and technologies used in structured data:

  • Microsoft SQL Server: SQL Server is a widely used database system that can store and retrieve large amounts of data for other programs. Developed by Microsoft, it is reliable with functional features to meet your needs.
  • PostgreSQL: It is an open-source RDBMS you can use for free. You can use it for JSON and SQL querying, which also supports programming languages like Python and C/C+.
  • Oracle Database: The Oracle database is a powerful and versatile tool ideal for use in the data center. You can use it for data warehousing or online transaction processing, with its multi-model structure designed to work well with varying workloads.
  • SQLite: SQLite is an excellent choice for small to medium-sized applications because it doesn’t require much memory space. SQL queries are lightweight, which means the database will only grow in size if you make changes or add new rows.
  • MySQL: MySQL is a popular open-source database management system that performs large and small operations quickly. It runs on a server, so it’s ideal for creating personal and massive web applications.
  • OLAP Applications: Online analytical processing (OLAP) is a sophisticated approach to analyzing large amounts of data. OLAP tools allow users access to different perspectives on their information because they combine the mining and reporting features within one system.

Examples and Use Cases of 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.

  • ATMs: The ATM is an excellent example of how relational databases and structured data work. It has carefully designed menus, screens with clear instructions for each action you can take.
  • Online Booking: All hotel booking and ticket reservation services use the advantages of a predefined data model, as all their information fits into a standard structure. For example, dates get recorded in rows with prices next to them for easy reference.
  • Banking and Accounting: Financial transactions get processed and recorded by companies globally, which means they need databases to handle data efficiently. Traditional database management systems do a good job here.
  • Inventory Control: It’s not easy to keep track of inventory. There are many different ways that companies use this. However, the best way is by using databases in an organized environment with good relations between them.

What is Unstructured Data?

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:

  • Email: The message field of emails is an example of unstructured data as you cannot use analytics tools to parse through that data. The metadata of emails is semi-structured.
  • Mobile data: GPS-based location data and text messages
  • Text files: Word documents, spreadsheets, and PowerPoint presentations
  • Social media: Data related to Facebook, Instagram, and Twitter
  • Communications: Audio and video call data

Some examples of unstructured data through tech systems include:

  • CCTV: Digital surveillance data of photos and videos
  • Satellite imagery: Weather updates and military movements.
  • Sensor data: Weather and traffic-related data.

Pros of Unstructured Data

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.

  • Quick Rate of Accumulation: Unstructured data is excellent for collecting and storing information because it doesn’t have to be in any particular format. It means when you want the unorganized bits, they’re right at your fingertips without having to do extra work.
  • Does Not Rely on Native Format: Unstructured data in its native format does not remain restricted by a specific file type. You can store a large amount of unstructured information. It is adaptable and does not have any predefined purpose or meaning until needed. Preparing analytics is easier since you are only analyzing what matters most.
  • Increased Storage Capacity Through Data Lakes: In this day and age, it is no surprise that most businesses are using cloud storage for their data. The reason? Data lakes offer a low-cost alternative with easy scalability, making your business more competitive in the long run.

Cons of Unstructured Data

Here are the various disadvantages of unstructured data:

  • Requires Special Tools: Unstructured data represents an exponentially growing problem in the world of information management. The lack of intricacies within this type of unorganized data makes it difficult for users to make sense of the data available at their disposal.
  • Needs Data Science Knowledge: The biggest drawback to unstructured data is that it requires a significant amount of expertise and knowledge. Business users cannot use it without understanding its structure, which limits the use of the information.

Tools used in Unstructured Data

When compared with structured data, there are some standard data tools, relational databases, and technologies used in unstructured data:

  • Apache Hadoop: Apache Hadoop is a powerful and flexible framework that processes large amounts of data efficiently. It does not require any schema or structure for stored information. Instead, it helps with structuring unstructured datasets before exporting them into relational databases like MySQL.
  • Microsoft Azure: With Microsoft Azure, you can build and manage applications powered by data centers. The service offers comprehensive cloud services for storing the information in an accessible form, whether structured or unstructured.
  • Amazon DynamoDB: Amazon’s AWS package comes with the advanced NoSQL database DynamoBD. The service supports document and key-value data structures, making it an excellent choice for unstructured information not stored in structured formats.
  • MongoDB: MongoDB is a database you can use without any rigid schema. The documents in Mongo are similar to JSON-like objects, and they’re easy for humans and machines to understand.

Examples and Use cases of 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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