Data management in the broadest sense is understood as a set of technologies, processes and standards. Along with data quality issues, their security, compliance with corporate policies and regulations, consistency, the correctness of reference books, metadata, and master data, this often includes areas related to one or another applied data processing.
However, today, when many enterprises and organisations have embarked on the path of digital transformation when the range of data has expanded enormously, and its volumes have grown, when corporate data is located in the clouds and often does not belong to the company, but is bought or rented when data becomes almost the main asset of individual organisations, data governance requirements are changing, and existing practices are being revised.
Today, data is increasingly called new oil, and in some companies, it is positioned as one of the main assets. The boom around data is associated with new processing methods – machine learning and artificial intelligence.
Rigorous cloud data governance considerations should be included in all contracts with cloud service providers. Things to include:
- Data ownership – organisations, should retain exclusive rights of all data held in a cloud provider’s solution. Their staff, systems, or affiliates entered in all media forms, e.g. online, backup and archive, etc.
- Any other standard intellectual property clauses (as are relevant to the service).
- Data location (the countries where the data can be held should be explicitly stated in contracts – this should be based on the outcome of the cloud risk assessment and any associated privacy impact assessment).
- Privacy legislation compliance.
- Applying appropriate retention policies to stored data based on its classification means the cloud service provider’s solution must not hinder following any records act such as GDPR.
- A transparent process is documenting the responsibilities of each party concerning extracting and destroying data at the end of the contract.
- Provision for a cloud service provider being taken over/bought out by another organisation. This should include ensuring the ownership, access rights, and protection of any data the organisation owns cannot be lost when there is a change of cloud service provider ownership.
Effective data governance framework should include:
Establish data governance
- Define and document data governance model
- Empower data governance with executive sponsorship
- Organise and operationalise data governance functions
- Nominate, empower and manage data stakeholders, data stewards and data owners
Manage data governance
- Plan and sponsor data management projects and services
- Review and approve new data initiatives conformance with the information management framework
- Monitor and ensure regulatory compliance
- Resolve data conflicts
- Approve common data definitions
- Train and communicate the information management framework and value of data assets
Control & develop data governance
- Maintain organisation & sponsorship
- Manage business change
- Manage return on investment
- Estimate and manage data asset value and associated costs
- Maintain process and data quality metrics
Manage data and master data lifecycle
- Create and store master data (data warehouse and data lake)
- Update master data
- Move transactional data or master data
- Use transactional data or master data
- Monitor, measure and manage data quality
- Manage data authorisations
- Support decision-making processes with accurate data
- Retire and archive transactional data or master data