![]() Data Standards are a set of well-defined rules by which data are described, recorded, and shared in order to ensure common understanding among data users and to improve data quality, including data integrity, consistency, format and meaning. Adopt common data standards across datasetsĪll data within all datasets must conform to common data standards which are based on industry best practice. Linking data needs to be done in a consistent, reliable, and ethical way, whilst safeguarding privacy. It will enable more granular analysis to help explore and better understand the most complex issues facing society, but it needs to be done responsibly. The ability to link datasets together will help us to draw better insights from the data we hold. Using the same dataset for multiple purposes maximises the value of data by reducing burden on data providers and suppliers and preventing them from being asked for the same information multiple times. Maximise the value of data through multiple uses of the same dataset and enable data re-use through the implementation of a strategic data store which can be accessed by multiple users in a controlled way. Reuse and linkage Use data multiple times to maximise value As a producer of Official Statistics, ONS must follow certain protocols to control the external release and sharing of data and statistics, as set out in the Code of Practice for Statistics. Publish data and analysis via approved routesĭata and analysis should be made available to third or external parties in a controlled manner, via approved routes. This consideration of the ethics of a project should take place at the research design phase and should be regularly reviewed as the research develops. When undertaking research and/or producing statistics we must consider not just what we could do, but also what we should do to ensure that we collect and use data in ethically appropriate ways which are for the public good. This includes the Statistics and Registration Service Act, the Digital Economy Act, the Census Act, the General Data Protection Regulation, the Data Protection Act 2018 and any other relevant legislation. Data management Use transparent and legally compliant data practicesĭata practices should be transparent and comply with all relevant laws, policies, regulations, and standards of good practice regarding the acquisition, storing, processing, sharing, and disposal of all data. Quality assurance should take place across the entire data lifecycle and should be proportionate to the importance of the data. Achieving high data quality helps to ensure effective decisions can be made using the data. Actively manage, review and improve data qualityĭata quality is defined as whether datasets are fit for their intended purpose. Metadata is information about the characteristics of data and what happens to it from when it is collected or acquired, across its whole lifecycle through to archiving. ![]() All data must have metadataĪll datasets, whether collected via survey, acquired from an external supplier, or derived from processing, should be accompanied by metadata. It should then be disposed of or archived appropriately. Audit all subsequent data changesĭata should be backed up appropriately and retained only where necessary and for the minimum period required in line with policies, guidance, and any agreements in place with data suppliers or providers. ![]() Keep a copy of data in its 'as received' state and keep an audit trail of all subsequent changes, managed with clear version control practices so that it can always be rolled back to 'as-received' state, or to be accessed at the lowest level of granularity (dependent on access permissions). Keep an original copy of data as they are received. Follow a controlled and consistent data ingest processĭata can arrive via any of the agreed approved corporate routes and follow clear and consistent ingest processes for loading into an approved data storage solution data will be managed in accordance with agreed and authorised process. ![]() Always research ethically and learn about the needs of all kinds of users as this will help you to develop inclusively. The users' needs must inform the design at every stage to ensure you build the right thing and collect data that are fit for purpose. When designing and developing a data collection service, product or tool always start by learning about the respondent needs of the users (i.e., the people) who will be providing the data. Assets Follow best practice for data collection
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