Data is found in various forms and needs to be processed to gain proper insights. Companies that have huge infrastructure and data storage invest in IT automation with AI. However, they also need to understand the importance of categorizing and using structured or unstructured data.
What is Structured Data?
Any data available in specific formats like Excel sheets or Google sheets is known as structured data. It can be mapped easily and written in rows and columns. Structured data has specific parameters. Companies that are implementing AIOps digital transformation solutions use structured data for mining and analytics.
Pros of Structured Data
- Easy to Use
Employees can use structured data with any level of technological skills. If the user has a basic understanding of data, they can use structured data for various purposes. Users can access and build services related to structured data without having to deal with any complexity. Companies that implement machine learning can also use structured data as it is very convenient for such purposes. Since structured data is organized in nature, one can easily use it for accurate and quick analysis.
- Can be Stored Conveniently
Enterprises can conveniently store structured data in data warehouses or data lakes. These data warehouses are designed to conserve space. They are also optimized for enterprises and ensure quick access to structured data. Professionals can have a clear idea about where and how the structured data is being stored and can therefore access it when required.
- Access to Several Analytical Tools
Businesses often try to quantify data, and that is possible if that data is structured. The practice of quantifying structured data has been prevalent for a long time, and therefore, there are many tools available. Analytical tools have been developed particularly for structured data. There are also several tools available for the convenient processing of structured data.
Cons of Structured Data
- Limited Flexibility
While structured data can be accessed easily, it has its limitations. It is because the data is predefined and is, therefore, only suitable for certain processes. It is not flexible enough for different use cases and cannot be repurposed in any way.
- Difficult to Modify
Since structured data is often stored in data lakes, one has to adhere to the limits and rules of storage in such spaces. If the data requires any modifications, the process will be extensive and often costly. One can only do the modifications if there are enough resources available, and once complete, they take a long time to update. This reduces the scalability of structured data and has often led businesses to use Cloud Enablement Services to create cloud-native storage.
What is Unstructured Data?
Companies deal with various kinds of data and often use IT infrastructure managed services. The most common type of data is unstructured. Unstructured data is not predefined and has no limitations or parameters. It is stored in its natural form.
Pros of Unstructured Data
- Can be Used Without Any Limits
Unstructured data is extremely versatile and can be used without any specific limits. Professionals and data analysts do not need to generate unstructured data from any area. Professionals can obtain it through free-form texts, social media platforms, audio, or video content. This makes the applications and use cases of unstructured data limitless.
- Ideal for Obtaining Transformational Insights
Companies often require transformational insights that are actionable. It is easier to get them from analyzing unstructured data. Businesses deal with a large volume of unstructured data daily. Therefore, such data is easily accessible for analysis. While the process of analyzing unstructured data may be challenging, the insights generated are beneficial. These transformational insights can allow businesses to create competitive services.
- Affordable Storage
Like structured data, one can also store unstructured data in data warehouses. But since one does not need to store any unstructured data in data lakes, storage is much cheaper. Due to the affordability of data warehouses, enterprises can house huge volumes of unstructured data.
Cons of Unstructured Data
- Difficult to Analyze
While it is easy to generate unstructured data, it is not easy to analyze. The average user cannot access or analyze the data. Companies need to have data scientists with advanced skills to work on unstructured data. They need to find the data in its raw form and then evaluate and analyze it to extract valuable and actionable insights.
- Few Accessible Tools
Since businesses have been using structured data for quantification for a long time, few tools have been developed for unstructured data. Professionals cannot use traditional data analytics tools on unstructured data. The tools need to involve machine learning, artificial intelligence, and natural language processing.
- Various Formats Available
Unstructured data is available in different formats. It is difficult to analyze multiple platforms and extract anything of value. The process requires special skills and is time-consuming.
Difference Between Structured and Unstructured Data
Following are some major differences between structured and unstructured data.
- Structured data is quantitative, while unstructured data is qualitative.
- Structured data involves alphanumeric characters, but unstructured data has non-character digital representation.
- One can integrate structured data in simple SQL methods, but unstructured requires specific tools.
Structured and Unstructured Data: Impact on ITOps and Business Needs
Business enterprises are incorporating AIOps artificial intelligence for IT operations. An essential part of these operations includes big data analytics and data mining. In this case, the availability of both structured and unstructured data is important. While structured data is essential for data mining, unstructured data is important for data analytics, especially because it is available in a large volume and is versatile. Unstructured data is also used to achieve marketing intelligence. Quick scanning of available datasets and unstructured data generated from customer behaviour can help businesses figure out what will boost their services. Structured data, on the other hand, has a few practical uses. These include enhancing AMP, ensuring website visibility on Google Knowledge and Graphs, improving click-through rates.
In recent times, companies have been moving towards hyper-automation. In ITOps, hyper-automation can assist in dependency mapping and auto-discovery. It can also make data more accessible to businesses by converting available unstructured data to structured data.
Both structured and unstructured data can help businesses. It is important to know when to use what data and how to analyze current and historical information. Businesses can benefit from leveraging both types of data and establishing services that will generate more revenue.