What is data management?

Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively throughout the entire information lifecycle—from ‘create and capture’ through to ‘retain and archive’ and ‘dispose and destroy’. 

The aim of data management is to help people and organisations optimise the use of data—within the bounds of policy and regulation—to maximise its use and benefit.  

Data management encompasses many concepts, including:  

  1. Data governance, which is the planning of all aspects of data management. This commonly includes ensuring availability, usability, consistency, integrity, and security of data managed by an organisation. 

  1. Data architecture, or the overall structure of an organisation's data and how it fits into a broader enterprise architecture. 

  1. Data modelling and design, which covers data analytics and the design, building, testing, and maintenance of analytics systems. 

  1. Data storage and operations, which is concerned with the physical hardware used to store and manage data. 

  1. Data security, which encompasses all elements of protecting data and ensuring only authorised users have access. 

  1. Data integration and interoperability, which includes everything to do with the transformation of data into a structured form (i.e., in an organised database) and the work necessary to maintain it. 

  1. Documents and content, which includes all forms of unstructured data and the work necessary to make it accessible to, and integrated with, structured databases. 

  1. Reference and master data, or the process of managing data in such a way that redundancy and other mistakes are reduced by standardising data values. 

  1. Data warehousing and business intelligence, which involves the management and application of data for analytics and business decision making. 

  1. Metadata, which involves all elements of creating, collecting, organising, and managing metadata (data that references other data, like headers, etc.). 

  1. Data quality, which involves the practices of monitoring data and data sources to ensure quality information is being delivered, integrity is being maintained, and poor quality data is being filtered out (DAMA International).

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Data management vs information management

Just as we covered the distinction between data and information, it is important to note the difference between data management and information management.  

Information management refers to an organisational program or system that manages the processes that controls the structure, processing, delivery and usage of information. Data management is a subset of information management whereby data is managed as a valuable resource, and comprises all disciplines related to managing data as a valuable, organisational resource. It focuses on the process of creating, obtaining, transforming, sharing, protecting, documenting and preserving data.  

Research data management 

Research data management involves the organisation, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data underpinning publications in a repository for long-term archiving and access). 

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Why data management is important

At its core, data management enables an organisation to optimise the use of their data. It has numerous benefits, including: 

  • Allow UQ to better utilise, understand and manage its vast quantities of data. 

  • Help ensure that UQ’s data is accurate, available and accessible. 

  • Minimising the risks and costs of regulatory non-compliance and security breaches. 

  • Faster application and system development. 

  • Improved decision making and reporting. 

  • More consistency across all enterprise processes. 

At a corporate level, data management also lays the groundwork for data analytics. Data increasingly is seen as a corporate asset that can be used to make more-informed business decisions, improve marketing campaigns, optimise business operations and reduce costs. Good data management is essential in producing reliable and valuable insights.  

Furthermore, UQ is a research institution. Research requires the collection of thousands of pieces of data, produced in multiple forms, and collected and stored over years. That collection needs to be secure and accessible at every stage of the research process. At a research level, effective research data management is needed to support producing quality research outcomes as well as help with compliance, legal and/or funding requirements.  

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Data management at UQ

At UQ, there are various areas which are responsible for leading and governing data management. These include (but are not limited to): 

  • The Data Strategy and Governance team, responsible for establishing and maturing the University’s data management capabilities. This includes developing practices and processes concerning the formal governance and management of UQ’s data assets. 

  • Data Services and Analytics teams which provide various data platforms at UQ to enable business intelligence and data analytics.  

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UQ staff and researcher responsibilities

All staff play a role in supporting effective data management practices. See data roles and responsibilities to learn more.  

Furthermore, as UQ is a research institution, managing data in a way which results in trusted, reusable, interoperable, findable and accessible data is essential and a joint responsibility between researchers and UQ. 

Learn more about managing research data at UQ

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