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Structured Data - A foundation for data analysis in BI

Businesses should use structured data in their business intelligence solutions for a number of reasons. First and foremost, structured data allows for efficient searching and retrieval of information. This is in contrast to unstructured data, which is not organized in a specific manner and is therefore more difficult to search and access. By using structured data, businesses can quickly and easily access the information they need in order to make informed decisions.

Data comes in various forms. Most of the time companies gather data that is unstructured - this data is usually accessible on a website in the form of texts, videos and etc. It is often unorganized and difficult to search and analyze using traditional tools and techniques. On the other hand, structured data is anything entered into a pre-defined form, including the whole process for gathering it - employee records or tracking sales.

However, structured data is essential for companies that want to create business intelligence solutions. This type of data is best suited for the BI solution and its purpose, and it needs to be stored in databases before being connected to the BI tool. In the case of unstructured data, companies can use ETL tools to transform the data into a structured format.

Structured data — typically categorized as quantitative data — is highly organized and easily decipherable by machine learning algorithms. Developed by IBM in 1974, structured query language (SQL) is the programming language used to manage structured data. By using a relational (SQL) database, business users can quickly input, search and manipulate structured data - IBM

Another reason why businesses should use structured data in their business intelligence solutions is that it is highly standardized. This makes it easy to share and integrate with other systems, which can be beneficial for companies that need to make use of multiple sources of data. In contrast, unstructured data is often highly variable and may not be compatible with other systems. By organizing data into a consistent and standardized format, businesses can create more sophisticated and powerful business intelligence solutions that can provide valuable insights into the performance of the company.

In the past, analyzing data was primarily a statistical exercise. Companies would typically create structured data from stable data models using enterprise applications like CRM, ERP, and other financial software. The data would then be transformed using ETL tools and undergo a data quality control process, including normalization.

At the end of this process, this transformed data is stored in a data warehouse. A relational database adopts the idea of structured data. The basis is a relational data model.

Structured data usually resides in relational databases (RDBMS). Fields store length-delineated data like phone numbers, Social Security numbers, or ZIP codes. Records even contain text strings of variable length like names, making it a simple matter to search - datamation

In terms of how structured data looks, it is often found in databases, where it is organized into tables with rows and columns. Each row represents a single record, such as a customer or a product, and each column represents a specific piece of information, such as a customer's name or a product's price. This organization allows for efficient search and retrieval of information, as well as easy integration with other systems.

This overall process is generally performed on a regular cycle, usually on a daily or weekly basis.

Structured data is ideal for Machine Learning algorithms because of its organized format. This makes it easier for an algorithm to process the data. Additionally, structured data allows for a one-to-one comparison, which can be useful.

This type of data is also well-suited for business applications used by non-technical users because it is clear and easy to understand without requiring a deep understanding of the data types or their nature.

Another advantage of structured data is the variety of tools that can analyze it. For example, data can be visualized by PowerBI or other software. Structured data is also essential for creating dashboards and reports, which are key tools for business intelligence. These tools allow managers to quickly and easily view important metrics and trends, such as sales figures, customer satisfaction levels, and production output. By organizing data into a consistent and standardized format, business intelligence solutions can make it easier for managers to gain valuable insights into the performance of the company and identify areas for improvement.

A data dashboard is a tool many businesses use to track, analyze, and display data—usually to gain insight into the overall wellbeing of an organization, department, or specific process - Microsoft

I highly recommend using structured data for day-to-day business decisions. This data can also be easily manipulated in excel sheets using the Pivot table. Working with unstructured business data such as budgets might be very inconvenient in case of further analysis and usage.

Nowadays, data visualization is much more important due to the amount of data generated and visualization tools need structured data to work properly.

Overall, structured data is an essential component of any business intelligence solution. Its ability to facilitate efficient search and retrieval, as well as its standardized format, make it a valuable tool for organizing and analyzing data in a business setting. By leveraging the power of structured data, businesses can gain valuable insights and make more informed decisions.


Matěj Srna


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