Data is an incredibly valuable resource for businesses, enabling organizations to effectively operate and to make business improvements. In order to exploit this value most effectively, businesses must invest in good data management.
The aim of data management is to allow businesses to oversee and control their data resources, enabling organizations to optimize the use of their data in a manner which enhances the value of data while minimizing risk (e.g. legal and compliance risks) to the business.
What does data management include?
Data management aspects include the following
- Assigning responsibilities for data sets.
- Validating the quality of data.
- Identifying relationships within data sets.
- Recognizing and resolving faults within the data.
- Optimizing database systems (including database administration).
- Managing the backup and recovery of data, and its archiving and destruction.
It is a multi-disciplinary area, which requires structured input from a variety of teams across an organization such as IT, information security, records management, privacy, legal, compliance and business area representatives.
An important element of good data management is data governance. Data governance consists of the establishment of appropriate plans and policies at the “people level” to ensure effective data management within applicable legal and regulatory frameworks.
A key aspect of good data management, and a fundamental tenet of data governance, is accountability. Each major dataset should be formally managed by a named individual, who is responsible for the care and quality of the data which is assigned to them. These “data stewards” should ensure that such policies and procedures are followed in respect of their datasets, including around classification, validation and quality control of data.
Data ethics are linked to data governance. Data ethics moves focus from simply considering whether data use is legally compliant to whether data use is ethically defensible and what the wider societal consequences might be for the proposed data use. The use of data ethics by a business can be important in safeguarding business reputation, and can also have wider legal implications depending on the context (as ethical and legal issues are often inextricably linked).
Principles of data ethics, such as fairness (e.g. taking steps to reduce or eliminate inherent bias within data, especially in the context of AI machine learning) and transparency, are increasingly being built into data governance strategies. This can help establish defensible processes for the handling and sharing of data, particularly where the data use impacts individuals or society, or the data is the basis for businesses decision-making.
For more information on ethics and decision-making, see Artificial Intelligence and Ethics.