Semistructured Data: Challenges and Solutions

In these times, the rapidly growing data stream around modern companies demands precisely tailored processing strategies. In addition to unstructured formats, semi-structured data can also become a challenge - especially if it is less structured than assumed at the beginning of a project. In this article, we look at the special features of this mixed form of data and show possible solutions for dealing with it.

What is semistructured data?

Semistructured data is data that lacks tabular order, but has a basic hierarchical structure due to certain markings. In principle, this enables categorization and further processing, but relational databases cannot be used due to the lack of structure. Semistructured data thus to a certain extent eludes the often binary category system that is often applied to data. First of all, defining the two most common forms of data can help to distinguish and better understand this hybrid form:

Structured Data is organized in a specific, consistent way and thus follows a continuous logic. Individual data parts are usually assigned to variables or input fields so that they can be ideally stored in databases and tables. This makes it particularly easy to navigate to specific information - for example, customer numbers, contract details or invoice content. In addition, structured data provides the ideal basis for AI-based further processing. Machine learning algorithms need this mathematical order in information in order to analyze it in the best possible way.

Unstructured Data on the other hand, has no order and does not even have to be in similar file formats. This makes data analysis and processing immensely difficult. Therefore, one usually first creates a necessary basic structure before a data-based gain of knowledge is possible.

Semistructured Data is already one step closer to this gain in knowledge. Through various metadata and tags, certain hierarchies can be built or semantic elements can be separated. In principle, this facilitates further processing, but for storage in typical, e.g. SQL-based databases a relational structure is necessary. In some cases, semistructured data is also understood as a subspecies of structured data, since it has at least a minimum of basic order due to the markings. However, considering this hybrid form as an independent data type can prevent irritations and makes it clear that special treatment is necessary when processing it.

Examples of semistructured data

The establishment of the Internet has given rise to many semistructured data formats, which has greatly changed IT, which until then had been very database-oriented. Correspondingly frequent sources are:

  • e-mails
  • Websites
  • Social media content
  • Word documents (with tags)
  • ZIP files
  • Binary files (e.g. .exe, .bin)

In addition, two particular data formats are popular precisely because they allow semi-structured data to be stored and have a corresponding versatility of use. However, there has been a significant redistribution of this popularity over the past twenty years.

One can see the shift from XML to JSON as well as the subsequent growing search interest in API


XML (Extendable Markup Language) is suitable for storing almost any data. As a markup language, it allows text to be structured and formatted in particular by providing it with appropriate tags. On the one hand, this facilitates machine processing; on the other hand, the format is human-readable. For these reasons, XML appears in a large number of business processes, but should be treated with caution depending on the degree to which it is structured.


The same applies to the open standard data format JSON (JavaScript Object Nation). It serves primarily as an exchange format for semistructured data from a wide variety of sources. The particularly flexible REST APIs usually serve as interfaces. Since JSON is purely text-based, it can thus be used to communicate easily between various servers, web browsers and enterprise applications. However, this easily distributes the somewhat deceptive mixed data form throughout the enterprise, which can lead to various problems. One of the most common misconceptions is that all JSON data structures are the same simply because they follow the same format.

In practice, the quality and structure of data varies greatly - depending on the individual applications or sources through which it was generated.

Challenges of the data mixed form

Semi-structured data has several advantages, especially due to its flexibility. For example, their order can be easily changed and they support users without SQL knowledge. However, companies pay a high price for this, which is expressed in the risk and error-proneness of this data typology. While structured data is one of the most important resources, holding SQL queries together and providing business intelligence tools with reliable information, semistructured data can disrupt this order in unpredictable ways. This is particularly evident in three challenges:

Data integration

Integrating Semistructured Data into a database-driven environment can be problematic due to the lack of relational structure. The same applies to attempts to insert it into tables. Traditionally built infrastructures in particular are hardly prepared for this unconventional data type. In addition, attempted mixing with structured data or different formats can lead to significant distortions.

Data quality

Semistructured data is often incomplete and inconsistent due to a lack of order. In addition, errors caused by manual input occur regularly. Cleaning up these weaknesses and extracting the valuable data content poses significant problems for companies.

semi-structured data quality
Many sources of semistructural data are prone to typos.

Data security

There is also some risk in terms of cybersecurity and compliance. Protection mechanisms such as firewalls work best for structured data because they behave statically and facilitate role-based access restrictions. Semistructured data, on the other hand, can take unpredictable forms and be riddled with insecure links. This makes it difficult to keep track of data and comply with regulations such as the GDPR or CCPA.

This is how data processing succeeds

In the meantime, however, semistructured data is no longer a new phenomenon and modern information technology can counter it with sophisticated solutions:

AI-based analytics: Machine learning algorithms are able to analyze semistructured data in order to extract and order relevant parts of the data. A particularly large field is natural language processing, which is the basis of most semistructured formats. By means of Natural Language Processing (NLP) for example, text is broken down into semantic units that can be mathematically coded and thus automatically captured. Similar work is done by Natural Language Understanding (NLU), except that this technology enables deeper semantic analysis by searching for keywords.

Another approach is provided by AI-based Optical Character Recognition (OCR). It focuses on visual recognition of individual letters matched with training data using neural networks. Last but not least, AI classifiers that use probabilistic principles such as Naive Bayes: to classify objects into categories, can be usea to analyze semistructured data. A typical example is email spam filters.

NoSQL databases: In contrast to relational databases, these are specially designed to accommodate semistructured data. No fixed schema is required and a wide variety of data formats can be processed. In addition, they allow high availability and scalability, which enables data processing in real time.

Data Lakes: This refers to particularly efficient storage environments that can hold immense amounts of structured, unstructured and semistructured data. Here, too, no rigid schema is necessary; it is more a matter of a buffer that saves the data until it is put into the appropriate form, for example, using processing tools.

Data Governance Tools: Tools are available to categorize, track and manage data policies. These make it possible both to increase data quality and to ensure greater security when dealing with semistructured data.

semi-structured cybersecurity

Process Semistructured Data with Konfuzio

As a data-centric IDP software, Konfuzio combines above processing techniques and some of the most sophisticated AI methods to ensure the most holistic and reliable processing of data. It focuses particularly on the automated processing of documents, which often contain semi-structured or even unstructured data.

Document automation

With the help of Konfuzio's Document AI, various documents of any structure can be read automatically. In particular, optical-semantic AI is used, which combines OCR, NLP and computer vision. Due to the different approaches of these individual technologies, Konfuzio accurately captures even heterogeneous and complex documents and extracts all relevant data. This data can then be further used in structured formats and, for example, fed into the company's own ERP or CRM system. Semistructured data is thus transformed from a dangerous disruptive variable into a valuable resource that can be used to make informed decisions.

Full data control

Konfuzio ensures compliance with security standards at all times and guarantees this through regular updates when the platform is implemented via the cloud. This ensures seamless availability and API access via any browser. Data Lakes can also be connected in this way, for example, to enable flexible storage of data. When using Konfuzio, this data does not leave the European legal area at any time. For more data control, the platform can also be operated on-premise via its own servers.

Outlook: Large Language Models as a New Breakthrough

LLMs are a particularly current and promising solution approach. They are understood to be large language models that have undergone pre-training with immense amounts of text. LLMs can be fine-tuned for individual tasks - for example, to process semistructured data. To this end, a team of researchers from Stanford and Cornell has developed a Method in order to significantly increase the inference quality. The special feature: In contrast to other attempts, the strategy promises a cost reduction of 110 times!

At the heart of it all is an elaborate code synthesis tool that identifies and applies a suitable schema for heterogeneous documents. To do this, it only analyzes fragments of the respective document using an LLM. Its high degree of flexibility prevents simplifying assumptions and thus typical errors during data extraction. Also because the concept can be modified in principle, it could become the most important strategy for dealing with semi-structured data in the future.


Semistructured data poses problems for companies because of its unpredictability. Classic databases lack relational order and the existing degree of structuredness in the form of tags can vary greatly. This complicates data integration, reduces data quality and can lead to security problems. Modern solution approaches focus particularly on the flexible use of artificial intelligence. Techniques such as OCR or NLP can be used to extract relevant data from semistructured formats and process it further. This approach finds its full potential in the Konfuzio software environment in combination with versatile technologies - with maximum data security.

Tim Filzinger Avatar

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