We are generating unprecedented amounts of data which is a gift and a curse at the same time. Storing, processing, and interpreting large amounts of data can be challenging and expensive, however, doing so in an efficient and meaningful way can offer you valuable insights. Big data analytics techniques allow business owners to study their customer base, predict market trends and changes, and make data-driven decisions. However, before proceeding with data analytics, we need to discuss the important distinction between structured and unstructured data. In this article, we will focus on the comparison between structured vs unstructured data, explore the benefits and drawbacks of both data types, and learn about their applications.
Structured data is clearly organized data that can be easily analyzed and utilized for business purposes. Stored in relational databases, structured data is quantitative and allows businesses to draw meaningful conclusions. Simple examples of structured data entail a library database that contains organized storage of every guest and book as well as a barcode system that is used to organize products at a point of the supply chain process.
Structured query language (SQL) is commonly used to interact with a database and search, update, alter or remove data. The data itself is stored in relational databases that enable users and machines to enter and operate the information. Structured data is highly useful for data analytics, data organization, and quantitative calculations, however, is not suitable for storing large amounts of data. It is often stored in data warehouses with inflexible schemas which complicate making even small changes.
The largest benefit of structured data is accessibility for non-tech personnel. Even without in-depth technical skills and programming abilities, business users can easily understand the types and layout of data and utilize it for their decision-making processes. This type of data also provides more analytical opportunities due to its clearly defined structure. There are a plethora of data analytics tools available on the market that allow business users to process and interpret large quantities of data.
The disadvantages of structured data include its inflexibility and lack of repurposing. The specific structure does not allow users to apply it to different cases without making significant changes to the system which takes a lot of time and money.
One of the most prominent examples of structured data is a Customer Relationship Management (CRM) system that stores customer information and allows users to extract data pertaining to customer behavior patterns. Another example of structured data is accounting and financial services. You can also encounter structured data systems in the financial sphere where accountants use a predefined format to record and process financial transactions.
Before we move on to the structured vs unstructured data comparison, let’s define unstructured data and identify its benefits. Unstructured data lacks consistent organization or format and does not fit into a well-defined framework. However, it does not mean this type of data cannot be structured and organized for various purposes in the future. Traditional data analytics tools are not able to analyze unstructured data in a meaningful way, which can be an issue since up to 80% of enterprise data is usually stored in an unstructured format.
Unstructured data is collected in non-relational databases and is categorized as qualitative data. Examples of unstructured data include audio, images, video, emails, social media posts, and more. When left unorganized, extracting insightful information is extremely difficult as you will be drowning in the endless pool of data. But when properly organized and structured in a purposeful manner, businesses can receive highly valuable results.
The main advantage of using unstructured data is its affordability and ease of storing. Since information does not need processing, definition and organization, it can be promptly collected and stored. On top of that, with the usage of data lakes, users can accumulate large amounts of data without spending too much money.
The downside is associated with the complexity of preparing and analyzing unstructured data. Unlike structured data that is accessible to non-tech specialists, you will need experienced data scientists to handle your data. Additionally, you will require specialized data analytics tools to work with unstructured data which can be pricey.
Unstructured data analytics can be employed to ensure compliance within the organization. The costs of noncompliance are high in any industry, especially in high-risk spheres like healthcare or finance. As one of the unstructured data examples, companies utilize pattern recognition and email threading to identify possible noncompliance and avoid potential repercussions.
One of the examples of unstructured data use cases is sentiment analysis which allows companies to monitor the results of marketing campaigns. Unlike a simple keyword search that calculates how many times a certain company or product has been mentioned in a period, sentiment analysis evaluates the content of the reviews. You can identify if the posts are positive or negative and assess the tone of reactions and overall opinions about the product.
Combined with machine learning tools, unstructured data can give insights into customer behavior and interests. You can evaluate which customer segment prefers a certain product or service and target your promotions more narrowly.
There are other types of data that do not fall under the umbrella of structured and unstructured datasets. For example, semi-structured data is a form of structured data that does not follow the schemas that are usually associated with relational databases. At the same time, it cannot be deemed unstructured data as it contains tags that highlight semantic elements and offer systematic records. Types of semi-structured data include XML, language for data exchange on the web, and JSON, an open standard format that is used to transfer data between servers and web applications.
Another example is metadata, a master dataset that provides additional information about a certain data item. Metadata has an organized structure, including metadata models and standards, and offers details to simplify and streamline data analysis. You can understand this data type better with an example of an online article. Metadata in this scenario would describe the title, headings, image, alt text, snippets and other relevant information. The purpose of metadata is to help differentiate between different articles and other pieces of content.
In this section, we will explore structured vs unstructured data and pinpoint the various differences between these types of data. What is the main difference between structured and unstructured data? The primary distinction lies in data formats—structured data contains quantitative elements, whereas unstructured data encompasses a range of formats that are hard to analyze using conventional methods.
In large volumes, structured data is mostly preserved in data warehouses which act like long-term depositories. At a smaller scale, structured data is stored in relational databases in a predefined format. There are various cloud data warehouses that make data storage more accessible and available to the masses. In structured data vs unstructured data, the latter is stored in data lakes and predominantly in large volumes. These depositories contain data in its initial format without any major changes. As a result, they allow more flexibility and require less maintenance compared to data warehouses.
In order to preserve data in a meaningful and efficient way, relational databases follow schemas for column configuration that correlates with the types of data inside the columns. This dependency can be beneficial for analysis but also pose a disadvantage and cause inflexibility when it comes to any changes. Although the information can be quickly found and processed, all entries have to follow a predefined. strict schema. When it comes to structured data vs unstructured data, the latter does not follow any particular formatting which makes it more flexible but also harder to work with and analyze.
As we mentioned above, structured data is kept in relational databases that use Structured Query Language (SQL) that offers syntax close to the English language. This makes it easier to learn and operate the database, while the strict structure allows straightforward data storage, processing, and interpretation.
Working with unstructured data prompts for a NoSQL (not only SQL) database or non-relational database. These types of databases offer various data models and store information in a non-tabular manner. Instead of using rows and columns, NoSQL databases store data in collections with various documents. Although some documents may contain a tabular form, they do not follow any preset schemas. There is no to little connection between the collections with the main objective to have efficient queries. Non-relational databases are able to process larger amounts of data compared to relational ones and can handle higher traffic.
To understand structured vs unstructured data, let’s explore in more detail the difference between quantitative and qualitative data. Structured data is often called quantitative—data that contains numbers or countable elements. This trait allows for simpler and clearer data analysis, including classification, regression, and clustering.
Unstructured data is also referred to as qualitative and holds subjective information that cannot be easily counted or classified. As opposed to countable items, qualitative data can contain social media posts about the product, various images, audio files, and more. Processing qualitative data requires advanced techniques such as data stacking and data mining.
A notable difference between structured data vs unstructured data is access to the talent pool. Due to the well-defined nature of relational databases and structured data, finding appropriate talent to handle the workload will not be difficult. Such databases have also been around much longer which has created a large talent pool to perform this job. Data analysts of junior and middle levels can work with relational databases and extract important findings.
Operating unstructured data is notably more challenging than working with structured data. Your company will require data scientists with a lot of expertise in the matter, including an in-depth understanding of the data topic and the product itself. Analyzing unstructured data also demands advanced machine learning skills and complex software.
Finally, the source of data is also crucial to the structured vs unstructured data distinction. Structured data is extracted from sources with consistent formats that can be easily identified, classified, and categorized. Unstructured data, on the other hand, comes in from various sources, regardless of the format. For example, a presentation that contains images, audio, and text will not be located in a relational database in its raw form. It can land in a non-relational database for further processing and analysis.
As you can see, the differences between structured and unstructured data are vast and important. Let’s build a simple overview of structured vs unstructured data in a tabular form.
Easy to analyze
Absence of schema
Hard to access
Processing needed to analyze
A few predefined formats
A vast amount of different formats with no schema
SQL (relational databases)
NoSQL (non-relational databases)
Ease of search
Easy to search
Difficult to search
CRM and ERP systems, accounting software, hotel management systems, inventory control
Word processing, image and voice recognition, sentiment analysis
Structured data examples include dates, credit card numbers, customer information, addresses, transaction information, product names
Unstructured data examples include text files, emails, audio and video files, images
Classification, regression, clustering
Data stacking, data mining
OLAP, SQLite, MySQL, PostgreSQL, Oracle Database
MongoDB, DynamoDB, Apache Hadoop, Azure Cosmos DB
Software engineers, business analysts
Data scientists and data analysts with deep expertise
Roughly 20% of enterprise data is structured
Up to 80% of company data is unstructured
As we discussed in our structured vs unstructured data comparison, analyzing unstructured data is immensely difficult and requires a lot of expertise. However, there are ways of converting unstructured data and making it easier to process, analyze, and interpret. In this part, we will go through a step-by-step guide to transforming unstructured data.
The first step is accessing the data lake and choosing which datasets are required for further analysis. This requires knowing the goals of the analysis and understanding what data you need and for which purposes.
Depending on the project purpose, select a processing tool that will yield the best results: text mining and natural language processing tools. You also need to clean the data by deleting redundant symbols, duplication, whitespace, etc.
If you choose the text mining method, the next step involves sifting through data and looking for certain words and phrases in order to extract key information from each document. Natural language processing (NLP) employs AI algorithms to sift through textual data to extract key features.
Data scientists utilize different tools for structured and unstructured data analysis, which we will discuss in this section.
One of the most prominent relational databases is PostgreSQL, which is free and open-source. The system supports SQL and JSON alongside popular programming languages, including Python and Java. Another commonly used tool is SQLite, a lightweight system that does not require an additional server process. MySQL is a fast and stable relational database, ideal for building applications of any size and complexity.
For a more complex project, you can use an Oracle database that offers a multi-model structure and is often applied for data warehousing. Finally, organizations employ OLAP applications (online analytical processing) that effectively handle multi-dimensional queries. The tools contain data mining features, reporting functions, and a relational database that allows users to perform different analytical processes and achieve various business goals.
MongoDB is the most commonly used NoSQL database that does not require any strict schemas or structures. If you are working with AWS cloud data analytics, you can take advantage of Amazon DynamoDB, a NoSQL database that supports document and key-value data structures. Apache Hadoop is an open-source framework that allows users to store and process vast amounts of data. The tool aids companies in structuring unstructured data and migrating it to relational databases. Finally, you can work with Microsoft Azure and utilize Azure Cosmos DB as a reliable database that enables storing and processing large volumes of unstructured data.
Nowadays, running a successful business without tracking data is nearly impossible, which leads experts to believe that the future of business decision making is intrinsically connected with data analytics. Without collecting and analyzing data, business owners may not even realize whether they took the right step, launched a fruitful marketing campaign, or released a successful product.
While structured data allows business owners and their employees to evaluate their performance indicators, unstructured data can give them access to trends and patterns and learn more about their customer base. Combining data analytics with machine learning techniques can offer a plethora of insights and target their audience in the most efficient way. They can also discover new opportunities, assess risks and threats, find new perspectives, and improve their product and company.
If you would like to learn more about structured data vs unstructured data and choose the right data storage for your company, get in touch with NIX United. Our expert team offers seasoned big data analytics services and helps organizations uncover their potential using valuable insights using custom business intelligence solutions.
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