What is Semi-Structured Data?

The first thing that pops to mind when receiving a new batch of data to work on is quite often the question of structure. Even the management of structured data, when the aim is to do it efficiently and extract as much business value as possible, requires particular skills and dedication. And unstructured data management presents its own set of challenges. But between these two well know categories of data defined by its level of structuring is a third one, namely, semi-structured data. This type of data is also something that data managers and analysts have to skillfully take care of in business-relevant settings. Therefore, it is well worth going over the particularities of this kind of data structure.

Between order and chaos

The contrast between structured and unstructured data is quite easy to grasp even for those who do not directly deal with data management and analysis. Structured data, in crudest terms, is the data organized following some set of rigid rules of formatting. Thus, there is a high level of order in such data.

Unstructured data is the opposite of it, meaning a set of data that is not organized following the pre-defined rules. Commonly the distinction between structured and unstructured data is made when talking about the formatting rules of the relational model of data and the relational databases based on this model. Structured data conforms to these rules thus is easily managed by relational databases while unstructured data does not have the order required by such databases.

The middle ground between the order and the chaos represented by these two types of structures is the semi-structured data. This type of data structure does not meet the requirements of the relational model, thus cannot without adjustment be integrated into relational databases. However, it is also not entirely without order, as it does have some organization and formatting.

Thus, semi-structured data partly resembles both structured and unstructured data and thus is in some cases considered a subtype of the one or the other. The structural features of the semi-structured data may differ from case to case as different semantic markers may enforce various irregular hierarchies or relations within such type of data. This variety of ways in which data can have traces of structure also means that a lot of data that software users meet daily is semi-structured.

Semi-structured data in business

Traditionally most of the data analyzed in business tends to be structured. That is, it is presented in tabular forms, spreadsheets, and similar ways that give the information a clear order and structure.

However, lately, businesses increasingly recognize that well-structured relational data is no longer enough for high-quality data-driven decision-making. Thus, they turn to semi-structured data and have many good reasons to do so.

Firstly, most of the information available is either unstructured or semi-structured. Thus, looking only at the data that follows strict rules of ordering means looking at a tiny part of information that is actually out there. Semi-structured data has traditionally been thought of as unstructured until an empowering realization that lack of rigidness does not mean total absence of structure. This realization enabled looking at a much larger portion of data for the development of business insights.

Secondly, semi-structured data allows integrating various data sources together without making adjustments to the data structuring. Relational databases require a lot of data to be restructured in order to be integrated provided that is possible at all. There is no such need when dealing with semi-structured data. This increases the efficiency of data ingestion while also saving money and other resources that would need to be spent on integrating data into relational databases.

Furthermore, working with semi-structured data allows flexible representations of the information. Such data is very scalable as data can be added and changed over time.

Finally, semi-structured data are easy to work with as there are no worries of particular actions ruining a rigid structure. Errors by human users or simple bugs in the software might undo a lot of work very quickly and then additional time and effort have to be spent just to restore the previous state and structure. There are no such dangers with semi-structured data as various ways in which particular pieces of data are structured have no significant effect on differently ordered information within the same database.

Some challenges to be addressed

Semi-structured data, of course, has not only advantages but also particular challenges as compared to rigidly structured information. The main of such challenges is being able to determine the relationship between differently presented data points. Additionally, queries in semi-structured databases cannot be processed efficiently, which means that it takes much longer to access the needed information.

These and similar challenges should be answered through the constant development of data management and analysis technology. This is precisely what semi-structured data analysts are looking forward to.

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