Structured vs. Unstructured Data: Everything you Need to Know
I write about fintech, data, and everything around it
Everything you must know about structured vs un-structured Data. What is it, why it matters, and how to move your data for better results.
In this challenging and competitive market, the one thing that has been helping businesses to strive and be relevant is data.
In fact, according to Forbes, 52% of businesses worldwide are making use of data and analytics to boost their business operation. 71% of businesses, on the other hand, believe that over the next three years and beyond, their investments in data and analytics will increase significantly.
From banking and finance to healthcare, every industry today is leveraging data to simplify their everyday operations and take better business decisions. Data steer a business in the right direction or, at the minimum, offer insights for planning future campaigns, organizing the introduction of new products, or conducting various experiments.
Organizations have benefited from the constant influx of data by making fact-based decisions that have led to growth. But every one of those decisions follows with the type of data that is being collected and moved.
You may have heard the terms structured and unstructured data, but you might be wondering what they mean. The distinction between structured and unstructured data has important implications for storing, processing and analysing data – particularly in large volumes. In this blog post, we cover what structured and unstructured data are, along with the main differences between the two.
Structured vs Unstructured Data: In a nutshell
You might be curious about the types of data being addressed given all the buzz about how organizations use it. The first thing to understand is that not all data is created equal. Although most data is unstructured, some of it is structured. Both structured and unstructured data live in many types of databases and are sourced, gathered, and scaled in diverse ways.
Structured data is well-organized, factual, and direct information. It usually takes the shape of letters and numbers that are easily inserted into table rows and columns. Unstructured data, on the other hand, exists in a wide variety of formats and without any pre-established organization. It can be anything from images and text files like PDFs to audio and video files.
What is Structured Data?
In simple words, structured data is a data type that can fit in a predefined format. It complies with a data model that has a clearly defined structure and follows a consistent order. Structured data is simple for a person or computer program to access and utilize.
Typically, structured data is kept in databases or other places with clear schemas. it is presented in a tabular manner with connections between the various rows and columns. SQL databases or Excel files are typical instances of structured data. Each of them has a set of organized, sortable columns and rows. And to manage structured data kept in databases, SQL (Structured Query language) is frequently utilized.
Pros of Structured Data
- Structured data is easier to access. Its use and existence predate that of unstructured data. Because of this, there are more tools available on the market for you to access, control, and alter it. Additionally, SQL may be used to call structured data, which further increases its accessibility.
- Structured data is simple to use with current technologies. Machine learning algorithms can understand and use structured data because of its clearly defined architecture. By using such technology, querying becomes simpler.
- Structured data is conducive to people. To understand and handle structured data, one does not necessarily need to have a thorough understanding of how it acts or performs. As a result, it is simpler for decision-makers to acquire, understand, and utilize data for corporate operations.
Cons of Structured Data
- It is essential to use the structured data in a specific way because of its predefined structure. The adaptability and versatility of structured data are therefore constrained.
- A predefined schema must be included in the storage because structured data must be stored in a specific manner. When the need arises to modify the data requirements, these data warehouses demand a lot of management resources.
Tools for Structured Data
Structured data has been here longer than unstructured or semi-structured data. Because of this, there are more tools available to manage structured data. A few of them are:
- MySQL
- SQLite
- OLAP
- PostgreSQL
What is Unstructured Data?
Unstructured data, in simple words, is undefined data that lacks a predetermined format. It refers to information that is not organized or has a predetermined data model. Although unstructured data can sometimes include facts like dates, numbers, and facts, it usually consists mostly of text. When compared to data stored in organized databases, this causes anomalies and ambiguities that make it challenging to comprehend using conventional tools. No-SQL databases, audio files, and video files are typical instances of unstructured data.
The capacity to store and analyze unstructured data has significantly improved in recent years, thanks to the introduction of a number of new tools and technologies that can store certain kinds of unstructured data. For example, MongoDB is designed to store documents efficiently. As an alternative illustration, Apache Giraph is designed for storing associations between nodes.
Pros of Unstructured Data
- Unstructured data is kept in the form that it was originally produced in. Because of its nascent nature, it may be modified to work with many other file formats, enhancing its adaptability and versatility. Instead of calling the whole stack, data scientists can only get the data they need to work with.
- Unstructured data doesn’t require any special processing before being stored. It is easier to gather and store as a result. It may be saved as soon as it is found or produced.
- Unstructured data is kept in data lakes, which are sizable spaces containing data storage and often operate on a pay-per-use model. As a result, businesses may store their data more affordably because they no longer need to operate internal data servers.
Cons of Unstructured Data
- Unstructured data needs expertise and experience to be understandable. It is initially devoid of any details or qualities. It is only a haphazard compilation of unprocessed data taken from the internet. Data scientists are thus required to handle and interpret this data.
- Unstructured data demands special tools. It is relatively new as compared to the structured data. Unstructured data cannot be used in its raw form. It requires processing through specialized data processors that separate it sufficiently to be usable.
Tools for Unstructured Data
Since unstructured data is more recent than structured data, there aren’t many tools available to manage it. This makes it difficult to manage unstructured data. However, you could find this procedure to be considerably simpler for you if you use the tools indicated below.
- Azure
- Amazon DynamoDB
- MongoDB
5 Critical Steps For Effective Data Cleaning
Data cleaning is a very important first step of building a data analytics strategy. Knowing how to clean your data can save you countless hours and even prevent you from making serious mistakes by selecting the wrong data to prepare your analysis, or worse, drawing the wrong conclusions. Learn the 5 critical steps for effective data cleaning.
Structured Data vs. Unstructured Data: Key Differences
It boils down to the sorts of data that can be used, the amount of data knowledge needed to use it, and the on-write versus on-read schema when deciding between structured and unstructured data.
Unstructured (qualitative) data offers a more in-depth insight into consumer behavior and intent than structured (quantitative) data.
Let’s examine a few of the main areas of distinction and their ramifications:
| Property | Structured Data | Unstructured Data |
| Sources | Common sources of structured data are spreadsheets, OLTP systems, online forms, networks, web servers, etc. | Common sources of unstructured data are email messages, instant messaging, media files, collaborative tools, and more. |
| Scalability | Scaling up or down might be a little challenging since structured data is stored on database schemas. | Unstructured data is more scalable since it is kept in its raw format without any processing. |
| Forms | Structured data is referred to as having a tabular format with clear relationships between the columns. | Unstructured data is accessible in rich media, geo-spatial and surveillance data, etc. |
| Format | Predefined format | No specific format, raw |
| Nature | Quantitative or mathematical | Uncategorized and Qualitative |
| Storage | Data warehouses | Data Lakes |
| Use Case | CRMs, online booking services, and accounting systems are some of the most common use cases for structured data. | Unstructured data has several applications, including data mining, chatbots, predictive analytics, etc. |
Role of Semi-Structured Data in Relation to Structured and Unstructured Data
Data analysts can identify information grouping and hierarchies by using the internal tags and marks that semi-structured data maintains to distinguish distinct data items. Databases can also be semi-structured, as can documents. Even though this form of data only accounts for 5–10% of the total amount of data, it has important commercial applications when combined with structured and unstructured data.
A typical example of a semi-structured data type is email. Although thread tracking, near-dedupe, and concept searching require more sophisticated analytic tools, email’s inbuilt metadata allows categorization and keyword searches without the need for any extra tools.
Even though email is a significant use case, most semi-structured development is focused on solving data transit problems. Web-based data sharing and transfer, including electronic data interchange (EDI), several social media platforms, document markup languages, and NoSQL databases, are becoming more and more popular use cases for exchanging sensor data.
Final Takeaway
Data and information are essential for the growth and sustenance of a business. It is also important for making informed business decisions and driving beneficial outcomes. How effective a business is depends on its capacity to obtain relevant data, evaluate it, and act in response to the findings. As important as it is, data doesn’t come in a single form or type. Some of it is structured whereas some of it is unstructured.
Structured data is easy to manage, but unstructured and semi-structured data are challenging to organize and extract. Every type of data is crucial to a business, and knowing how to handle it well helps organizations cut errors and increase productivity.
By selecting Zuci as a partner with expertise, you may raise the caliber of all of your data. Zuci offers a wide range of tools to help you obtain the data you need, ensure data integrity, and provide high-quality results without losing productivity. Visit Zuci’s data science and analytics services to learn more about our services for a strong data architecture that will serve you best.
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