Working with Structured and Unstructured Data
Data is at the core element of any business, and it comes in a wide range of formats ranging from relational databases to your last Facebook status update. All the different data formats can be sorted into structured and unstructured data.
Important factors to consider when differentiating between structured and unstructured data include:
●The end user of the data
●Data storage location
●Type of data
●Method of data storage
●Stage of data preparation
With these factors, understanding other terms like semi-structured data is easier, and so is mapping the future of data in cloud environments.
Structured data, also known as schema-on-write data, is data that has been predetermined and configured to a specific structure before storage. The relational database is the ideal example, formatted into clearly defined fields so that it is simple to query with SQL.
Benefits of Structured Data
●Suitable for business applications - Business users can use structured data with ease if they have a basic understanding of the subject matter. No in-depth knowledge of data types or their connections is required. The business user now has access to self-service data.
●Compatible with machine learning algorithms - The biggest advantage of structured data tools is how quickly and readily deep learning models can use it. Structured data's specificity and organization make it simple to manipulate and conduct queries on it.
●Easy access to more tools – Another advantage of structured data is that it has been around for a lot longer, since it has been the only option in past decades. As a result, more technologies have been utilized and examined for use in evaluating structured data, providing data managers with a wider range of product options to handle structured data.
Disadvantages of Structured Data
●Limited storage options – Data warehouses are typically where structured data is kept. These warehouses always have strict standards. Any change in requirements requires reorganization all of that data structure to fit the new criteria, which consumes a significant amount of energy and time. Utilizing a cloud-based data warehouse can partially reduce costs because it enhances scalability and eliminates the need for on-site equipment maintenance.
●Use is constrained by a specific goal – Structured data has several advantages, including the ability to define data on-write, but it's also true that data with a preset structure can only be utilized for that purpose. This limits the use cases and the system's flexibility.
Structured Data Examples
Structured data serves as the foundation for ATMs and inventory management systems. It may be created manually or automatically.
Weblog statistics and point-of-sale information like barcodes and quantity are typical instances of machine-generated structured data. Additionally, spreadsheets are a well-known example of human-generated structured data that's familiar to everyone who works with data.
Unstructured data (schema-on-read) is information that is kept in its original, unprocessed form until it is needed. Emails, posts on social media demonstrations, chat, IoT sensor data, and satellite data are just a few of the many file formats associated with this type of data.
Advantages of Unstructured Data
●Adaptability – Unstructured data is not defined until it is required since it's kept in its original format. Because the data's purpose is flexible, there is a bigger use case pool. It enables the preparation and analysis of only the necessary data. Because the data that may be saved is not limited by a certain format, the native format allows for a wider choice of file types in the database. As a result, an organization will have access to a wider pool of data.
●Data accumulation rates are faster – The data can be gathered fast and easy because there is no requirement for it to be predefined.
●Scalability - Unstructured data is frequently kept in cloud data lakes, which offer enormous capacity. Pay-as-you-use storage fees are another option available with cloud data lakes, which lowers expenses and facilitates simple scalability.
Disadvantages of Unstructured Data
●Specialized unstructured data tools are needed to manipulate unstructured data besides the necessary expertise. Standards set for application of structured data leaves a data administrator with few options for unstructured data products, some of which are still in the early stages of development.
●The biggest disadvantage is that it requires data science competence to prepare and evaluate. Due to the undefined/non-formatted nature of unstructured data, a typical business user cannot use it as is. Understanding the content or scope of the data as well as the data interrelationships is necessary for using unstructured data.
●Unstructured data is typically kept in data lakes, whereas structured data is frequently kept in data warehouses. Both have the potential to be used on the cloud, but organized data consumes less space for storage.
●The final modification can have a major effect. The typical business user can use structured data, but effective business insight from unstructured data requires competence in data science.
Unstructured Data Examples
Unstructured data is more distinctive and qualitative in nature.
It is highly suited for social media and market review websites that are in the business of evaluating the efficacy of marketing campaigns or spotting potential buying trends. Since it can be used to spot chat patterns or questionable email trends, it can also be highly helpful to organizations in monitoring for policy compliance.
Semi-structured data is unstructured data containing metadata that defines specific qualities. Compared to pure unstructured data, the metadata makes it possible to catalogue, search, and analyze the data more effectively. Semi-structured data can be thought of as the intermediary between structured and unstructured data.
A nice illustration of semi-structured versus structured data is a tab-delimited file with customer information against a database with CRM tables.
The Next Step for Your Data
Data integrity is essential if you want to maintain your data as a repository of authenticity, regardless of whether you choose to employ structured or unstructured data. The best way to create data integrity is to use well-established data governance procedures and well-defined data management methodologies. Selecting a partner with experience can help you improve the quality of all of your data.
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