What is structured data?
Structured data is when data is in a standardized format, has a well-defined structure, complies to a data model, follows a persistent order, and is easily accessed by humans and programs. This data type is generally stored in a database.
While structured data only accounts for around 20 percent of data world-wide, it is the current foundation of big data. This is because it is so easy to access, use, and the outcomes of using it are far more accurate.
Why does a business need structured data?
The biggest source of information a business has about its customers, processes, and staff is data. This data could take on many forms—feedback from customers, Tweets, financial information, stock flow, almost anything. However, a large proportion of data is completely non-quantifiable. You cannot measure feelings, reasons for behavior, or a video clip. So, structured data is required because you can draw inferences and information from it more easily than unstructured data.
If a business is planning on growing or moving into a new product segment, then structured data is required. This data is easily used in machine learning and artificial intelligence, and it results in accurate predictions about what will yield the biggest increase in business size, or which new product will sell best.
Structured data is also useful to staff: customer details, sales information, stock levels, day-to-day information that needs to be accessible, easy to manage, and provides relevant information.
Characteristics of structured data
Good structured data will have a range of characteristics, regardless of how the data is stored or what the information is about. Structured data:
- Has an identifiable structure that conforms to a data model
- Is presented in rows and columns, such as in a database
- Is organized so that the definition, format and meaning of the data is explicitly understood
- Is in fixed fields in a file or record
- Has similar groups of data clustered together in classes
- Data points in the same group have the same attributes
- Information is easy to access and query for humans and other programs
- Elements are able to be addressed, enabling efficient analysis and processing
The sources of this data vary, depending on the organization. There is computer or machine generated data that is created by a machine without any need for human intervention. This includes things like sensor data, web logs, point-of-sale details, and financial information. This all is automatically captured by machines.
Human generated data is, obviously, supplied by humans. This includes input data from survey responses, click-stream data that records all the actions a human takes on a website, or a move-by-move breakdown of actions taken in an online game.
Alternatives to structured data
This data is not in a relational database, does not conform to a data model, but has some elements of structure. While it is not as rigid as structured data, it does have some elements that are similar.
This data cannot be stored in rows and columns or databases. This data contains metadata and tags which helps it to be grouped appropriately and describes the way it is stored. Semi-structured data is organized hierarchically, although the entities within that group may not have the same properties or attributes. It is difficult to automate and manage and is hard for programs to access.
Semi-structured data includes XML language data, emails, zipped files, web files, and binary executables.
This unstructured data does not conform to any other model and has no easily identifiable structure. There is no organization to it and it cannot be stored in any logical way. Unstructured data does not fit into any database structure, has no rules or format, and it cannot be easily used by programs.
This data type includes videos, reports, surveys, Word documents, images, and memos.
Advantages of structured data
Structured data has a range of advantages. If an organization intends to use data for business predictions or analytics, then it must be structured.
Easy storage and access
Because structured data has a well defined architecture, it’s easy to find the data when needed. Human or computer, the relevant database is quick and easy to locate.
Data mining is simple
If data is required for artificial intelligence or machine learning, it is easy to apply. Knowledge can be easily extracted from the data, even using manual calculations.
Ease of updating and deleting
If the data is well structured, updating and deleting data becomes a simple task.
Because the data fits into a pre-set architecture, it is easy to add more. In terms of streamed data or data that is constantly being refreshed, it will automatically be added in the correct place.
Better business intelligence
Data mining is a far simpler exercise when the data is structured. This means that any predictions made or business intelligence assumptions drawn from it are more likely to be correct and accurate. Machine learning algorithms easily crawl the data, making for simple data queries and manipulation.
Data security is easy
Structured data is stored in a data warehouse, which generally will have layers of security. While nothing is ever 100 percent safe, the security of structured data is simple to implement and follows standard industry best practices.
Easy searches for information
Because structured data can be indexed on text string and attributes, this makes search operations simple. The nature of the data is easily understood, with meanings and relationships behind the data being easily accepted.
Disadvantages of structured data
Data warehouses or relationship databases where structured data is stored have set structures that are not flexible. If, for whatever reason, the requirements of the data change, it is likely all of the structured data will need to be updated.
Limited use cases
Because all the data has been collected in a certain way for a certain use, that is how it will be used. As a result, structured data has less flexibility.
The future of structured data
While structured data is currently 20 percent of an organization’s data type, that percent is dropping. The huge increase in unstructured and semi-structured data, which is growing at a rapid pace, is decreasing the share of data. Currently, structured data is still valuable with an increasing emphasis on predictions for business. Because structured data is far more accessible than unstructured data, it is currently valuable for businesses.
Only 0.5 percent of unstructured data is used and analyzed, but it is a valuable source of information. As the industry turns towards deciphering and quantifying unstructured data, the reliance on structured data will fall. Semi-structured data is being increasingly transferred to JSON format, which is parsable for machines. This means other data forms, which are less rigid in structure, will become the source of more data analysis.
While the focus has been on turning un or semi-structured data into structured data, the emphasis now is on having the data available for machines without the extra, expensive, and time-consuming step of turning it into structured data.
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