3 min readMar 26, 2024


Data Quality Dimensions - Part VII: Structuredness

Data Management Series

Barry Boehm defines structuredness as ‘the degree to which a system or component possesses a definite pattern of organization of its interdependent parts’ [1], which transposed to data refers to the ‘pattern of organization’ that can be observed in data, mainly the format in which the data are stored at macro-level (file or any other type of digital containment) or micro-level (tags, groupings, sentences, paragraphs, tables, etc.), emerging thus several levels of structure of different type.

From the various sources in which data are stored — databases, Excel files and other types of data sheets, text files, emails, documentation, meeting minutes, charts, images, intranet or extranet web sites, can be derived multiple structures coexisting in the same document, some of them quite difficult to perceive. From the structuredness point of view data can be categorized as structured, semi-structured and unstructured.

In general, the term structured data refers to structures that can be easily perceived or known, that raises no doubt on structure’s delimitations. Unstructured data refers to textual data and media content (video, sound, images), in which the structural patterns even if exist they are hard to discover or not predefined, while semi-structured data refers to islands of structured data stored floating with unstructured data, or vice versa.

From this perspective, according to [3], database and file systems, data exchange formats are example of semi-structured data, though from a programmers’ perspective the databases are highly structured, and same for XML files. As also remarked by [2] the terms of structured data and unstructured data are often used ambiguously by different interest groups, in different contexts — web searching, data mining, semantics, etc.

Data structuredness is important especially when is considered the processing of data with the help of machines, the correct parsing of data being highly dependent on the knowledge about the data structure, either defined beforehand or deducted. The more structured the data and the more evident and standardized the structure, the easier should be to process the data. Merrill Lynch estimates that 85% of the data in an organization are in unstructured form, most probably this number referring to semi-structured data too. To make such data available in a structured format is required an important volume of manual work combined eventually with reliable data/text mining techniques, a fact that reduces considerably the value of such data.

Text, relational, multidimensional, object, graph or XML-based DBMS are in theory the most easily to process, map and integrate though that might not be so simple as it looks given the different architectures vendors come with, the fact that the structures evolve over time. To bridge the structure and architectural differences, many vendors make it possible to access data over standard interfaces (e.g. ODBC), though there are also systems that provide only proprietary interfaces, making data difficult to obtain in an automated manner. There are also other types of technical issues related mainly to the different data types and data formats, though such issues can be easily overcome.

In the context of Data Quality, the structuredness dimension refers to the degree the structure in which the data are stored matches the expectations, the syntactic set of rules defining it, being considered across the whole set of records. Even a minor inadvertence in the structure of a record could lead to processing errors and unexpected behavior. The simplest example is a delimited text file — if any of the character sets used to delimit the structure of the file is available in the data itself, then there are high chances that the file will be parsed incorrectly, or the parsing will fail unless the issues are corrected.

Originally published at Written: Jan-2010, Last Reviewed: Mar-2024

[1] Barry W Boehm et al (1978) “Characteristics of software quality”
[2] The Register (2006) “Structured data is boring and useless”, by D Nortfolk (link)
[3] Peter Wood (?) “Semi-structured Data”




IT professional/blogger with more than 24 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management