Structured data is information that is organised into a specific format. It is used to describe and organise information in databases, spreadsheets, and other types of data storage systems. It has a wide range of applications including business intelligence, data mining and machine learning. Unlike unstructured data, it can be easily searched, sorted and analysed.
Structured Data and Privacy
A number of issues need to be considered for structured data and privacy including the processing of personal information. Structured data containing personal information such as names and addresses must be protected from any unauthorised access or use. Before collecting personal information, you need to obtain consent from the individuals. Furthermore, it is important to be transparent about using the obtained data.
There are several laws and regulations such as GDPR and CCPA which must be followed. This ensures that data is collected and used in a manner that is compliant with the privacy rights of individuals.
Connecting structured data
Connecting structured data is the process of linking or combining different sets of data from different sources to create a more complete or unified picture of the information. This is done by identifying common keys of attributes that is used to link the data, such as a unique identifier or a shared field. There are several methods to connect structured data, depending on the data format and the tools that are used. Some common methods include joining, merging and appending.
Joining links data from two different tables or datasets by matching the values of a common field. This can be a primary key or a foreign key. Merging combines data from two or more tables or datasets into a single table or dataset. However, it also requires careful consideration of data quality, data privacy, and data governance. Appending adds new rows of data to an existing table or dataset. Connecting data provides many benefits, marking it easier to analyse and understand large amounts of data, discovering new insights, and creating more accurate predictions.
Unstructured vs Structured data
Structured and unstructured data are two different types of data that have distinct characteristics and uses.
Structured data is organised in a specific format such as a database or spreadsheet. It is used to make data more easily searchable and readable by machines. This type of data is present in customer information in a CRM system, financial data in accounting software and product information in an e-commerce website.
Unstructured data is not organised in a specific format including text documents, images, videos, social media posts and emails. It can be difficult to search and analyse as it isn’t a predictable format.
The main difference between the two types of data is the way they are organised.
Structured data is useful for easily organising and analysing large amounts of information. The drawback is that it is a rigid format may not be suitable for all use cases. Unstructured data provides more context and insights, but is more difficult to analyse and understand.
Pros and cons of structured data
There are several advantages and disadvantages of using this data. Some advantages are the ease of access, it is machine-readable, flexibility and scalability. Structured data is organised in a specific format, making it easy to search, retrieve and analyse. It’s designed to be easily read and understood by machines. This makes it suitable for use in automated systems, such as databases and spreadsheets. Structured data can be easily integrated with other systems and can be used in a variety of applications. It can handle large amounts of data and can be easily scaled up to meet the needs of growing businesses.
Some disadvantages of structured data is that it is limited to a specific format, the maintenance, creativity limitations and privacy concerns. Structured Data is limited to a specific format, making it difficult to incorporate unstructured data, such as images and text. It requires regular maintenance and updates, which is time-consuming and costly. It can be rigid and may not provide enough flexibility for creative applications. It is machine-readable and makes it easy of hackers to access and use this data. This is a concern for the privacy of the data.
While structured data is useful for easily organising and analysing large amounts of information, the rigid format and potential privacy concerns need to be taken into account.
How can we use Structured data ?
How you use structured data depends on the specific goals and resources you have available.
It’s commonly used to create a database to store and manage large amounts of information. This is through a variety of database management systems including MySQL, PostgreSQL, and MongoDB. The data is used to perform data analysis to gain insights into your data in conjunction with tools such as Excel, R, and Python.
It can be used to create visualisation such as charts, graphs and maps which makes it easier to understand and communicate complex data. You can use it as input for machine learning algorithms, such as predictive modelling, classification and clustering.
Web scraping tools can be used to collect structured data from various websites and use it for different purposes such analytics and research. You can use it to improve the visibility of your website in search engines by providing additional information about the website’s content, such as the author, date, and keywords. This is known as Search Engine Optimisation (SEO).
To use structured data, you should have a good understanding of the data format and the tools that are used to work with it. Additionally, it’s important to be familiar with data privacy laws and regulations that may be applicable to your use case.
The best tools
There are many tools available, depending on your specific needs and the resources you have available.
Database management systems such as MySQL, PostgreSQL, and MongoDB are examples of popular database management systems that can be used to store and manage structured data.Data analysis tools including Excel, R, and Python are examples of popular data analysis tools that can be used to perform data analysis and gain insights into structured data.
The data visualisation tools Tableau, PowerBI, and Looker are examples of popular data visualisation tools. They are create visualisations, such as charts, graphs, and maps, to make it easier to understand and communicate complex data. Machine learning tools such as TensorFlow and PyTorch are examples of popular machine learning tools that can be used to implement machine learning algorithms on structured data.
Web scraping tools including Scrapy, Beautiful Soup, and Selenium are examples of popular web scraping tools that collect structured data from websites. Search Engine Optimisation tools such as Google Search Console, SEMrush, Ahrefs are examples of popular SEO tools that can be used to analyse the visibility and performance of your website in search engines and provide insights for improving it.
It’s important to note that these are just a few examples of the many structured data tools available, and the best tool for you will depend on your specific needs, your resources and your level of technical expertise.