What is unstructured data? – Different types of data

Differences between structured, semi-structured and unstructured data

Unstructured data is information that exists in a non-normalized and non-identifiable data structure. Classically, this includes text, image, audio and video files that are not stored in databases. Especially in the environment of Big Data, unstructured data is of enormous importance.

Different forms of data

Digital data can be sorted into three different categories. These are mainly distinguished by different degrees of structuring:

Unstructured data

The file type is known, but the content is completely independent in itself. They are not located in defined databases and are therefore very difficult to analyze. In addition, the majority of data available in companies is unstructured

  • Examples include enterprise digital assets:
    • Presentations
    • Videos
    • Images
    • Texts
    • etc.

Semistructured data

A certain basic structure is present, but the content itself is unstructured. These files contain certain information, such as metadata, but are still not easy to process because most of the information is unstructured. They are therefore to be classified between the structured and the unstructured data.

  • An example is e-mails: the subject, recipient and sender are defined, but the rest of the data is undefined.

Structured data

Structured data always has a predefined format in rows and columns (e.g. CRM systems). Thus, on the one hand, they are easy to find/process with the help of an SQL database, but, if they are built on a relational model, they also avoid duplication of information (data redundancy).

Examples of structured data are, for example, barcodes, log statistics or customer databases. Excel tables also contain manually created, structured data.

Knowledge content of unstructured data

Knowledge stored in unstructured data is initially “richer” than knowledge stored in structured form. This is due to the fact that often much more can be inferred from the context (e.g. emotions and context of an e-mail) than is possible with structured data. With structured data, the detailed context is often lost. Unstructured data is therefore much more difficult to interpret and often a case for Data Scientists.

One confusion that is often made is between Big Data and unstructured data. Big Data is not necessarily unstructured, but can also be in structured form (e.g. streaming data at Netflix). At the same time, there is unstructured data that does not belong to Big Data, such as individual media assets like images or video.

Challenges and solutions

The problem with unstructured data is that computers find it very difficult to assign, analyze and further process this data. The most relevant information in companies is usually available in unstructured form. In order to be able to process this automatically, methods from the field of artificial intelligence, such as Natural Language Processing or Deep Learning, are used. The aim is to extract information with the help of these technologies and make it comprehensible for software. This software can then process the information in various ways, such as an enterprise search engine.

The big challenge is to analyze and process large amounts of data from different sources and file formats in real time. This is not possible with today’s solutions. Instead, scalable solutions that can also process the growing data volumes of the future are needed.

Example:

Contracts are the results of various negotiations, protocols and amendments. If you only look at the result, then it is difficult to find out which things were relevant for the contracting parties and which influenced the conclusion of the contract. In the past, this could be done by employees evaluating relevant documents, such as letters of complaint or problems with supplier deliveries. It was possible to rely on the intuition and knowledge of the employee. Today, however, the amount of data overwhelms employees and companies because so much knowledge is stored, but it is simply no longer efficiently accessible to employees.

Today, generating and storing data is no longer a problem. Every tool stores data and makes it readily available. Companies today need scalable solutions that are able to efficiently process corresponding information and digitize it.

The future of unstructured data

The proportion of unstructured data will continue to increase in the future due to social media, voice assistants and other data producers. This makes it even more important for companies today to develop a good strategy for dealing with unstructured data, as this is essential for the future success of the company. This should not only be based on unstructured text files, but also include other, rapidly growing file formats, such as images, audio and video files. Furthermore, this should not be neglected, as companies always produce information in different media (flyers, podcasts, explainer videos, etc…).

Conclusion

The majority of knowledge within a company is stored in unstructured form. Companies must position themselves for the future in such a way that they make knowledge accessible to employees and use corresponding scalable methods. Many promising technologies are being developed quickly and promisingly, especially by startups, and success stories abound. Those who manage to break new ground at an early stage and trust and understand new technologies will not only be able to maintain their competitive advantage, but even expand it.

Bastian Maiworm ist der Gründer des Enterprise-Search-Tech-Startups ambeRoad. Als Jungunternehmer spricht er über die neuesten Entwicklungen im Bereich Startup und Enterprise Search. Bei ambeRoad war er maßgeblich an der Entwicklung der Vertriebsstrategie beteiligt und kennt aufgrund seiner Erfahrung die Probleme, die sich bei Kooperationen zwischen Konzernen und Startups ergeben, sehr genau. Dies nutzt er, um die Digitalisierung und Zusammenarbeit zwischen Startups und Konzernen weiter voranzutreiben und zu optimieren.

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