Common data terms

Raw data is a term used to describe data in its most basic digital format. Data is raw, individual facts that need to be processed.  When data is processed, combined with other data, organised, structured or presented in a given context, it is referred to as information. 

Data can be further broken down into structured and unstructured data. 

• Structured data resides in a consistent field structure and includes data in formats such as relational databases and spreadsheets. This data is often generated during business transactions and is stored in a business information system, e.g. student data, financial data, research data. 

• Unstructured data does not have a pre-defined data model or a consistent field structure that is easily readable by machines, and includes formats such as audio, video and unstructured text. Unstructured data may have structured elements, e.g. metadata associated with an email, xml document. 

Information is data with context. It includes, but is not limited to, physical (e.g. paper records) or digital files (e.g. email, voicemail, meeting minutes, video and audio recordings) in any format (e.g. PDF, .wav, .docx, .jpeg, etc.) and data recorded by University applications (often in a database of some form). 

Knowledge is synthesised information. This means that it is personal to us—our brains store information and use it to make judgements about the world. For example, if we know that the number of ibises has increased on campus, we might be less likely to eat our lunch outside in case an ibis steals our sandwich. 

Learn more about Information in the Library’s Information Essentials Training

Records are “information created, received and maintained as evidence and as an asset by an organisation or person, in pursuit of legal obligations or in the transaction of business” (Australian Standard AS ISO 15489-1-2017). In essence, records aren’t just collections of data—they comprise of the content, context, and structure necessary to provide sufficient evidence of a business activity.  As a collection, records are fundamental to our institutional memory and contribute directly to our understanding of UQ in the past, present and into the future. 

Learn more about Records at UQ at the Records Management and Advisory Services website

Data needs to be effectively governed in order to fully capitalise on its value. Data Governance commonly refers to the below terms in relation to data and information. 

• An Information Domain is a broad category or theme under which UQ information can be identified and managed.  
  Examples of Information Domains are: Teaching and Learning data, Finance data, Human Resources data etc. 

• A Information Entity is a specific group of information that is related to an Information Domain.  
  Examples of Information Entities are: ‘Digital Learning' data for Teaching and Learning domain, ‘Budget’ data for the Finance domain, ‘Salary’ data for the Human Resources domain, etc. 

• Datasets are a collection of related data elements that can be integrated and combined into one. Most commonly a Dataset corresponds to the contents of a single database table. 
  Examples of Datasets are: Blackboard Clickstream data, WiFi logs, SiNet HR datasets, etc. 

• Data Elements are the smallest named item of data that conveys meaningful information or condenses lengthy description into a short code. Data elements are called ‘data field’ in the structure of a database. 
  Examples of Data Elements are: course_id, timestamp, etc. 

• An Information Asset is a body of information, defined and managed as a single unit so it can be understood, shared, protected and exploited effectively. Information assets have recognisable and manageable value, risk, content and lifecycles. 
  *Note: this can be in the form of an information entity, collection of datasets, data elements, and so on.