"Data quality is acceptable and meets the business need for which it is intended"
Data produced and reported must be fit for purpose. That is, of sufficient accuracy and integrity proportional to its use and cost of collection and maintenance.
Data is used in all areas of decision-making, operations, planning and performance management in order that the organisation achieves its objectives.
Data is increasingly being used externally by customers, consumers and citizens to inform their personal decisions, and by stakeholders to assess the organisation’s aggregate performance of the organisation. This reinforces the need to ensure that the quality of data held is sufficient to meet diverse needs.
Significant human and system resource is consumed in the collection, manipulation and dissemination of data whether of high quality or not, so it is essential that the most effective use of public funds is achieved through appropriately directed attention to data quality and the procedures to realise quality.
Data should be sufficiently accurate for its intended purpose, representing clearly and in sufficient detail the activity which it represents.
Standards to monitor data quality
Data cleansing, data cleaning, or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant, etc. parts of the data and then replacing, modifying, or deleting this dirty data.
Disciplines and standards will need to be put in place and monitored so that accuracy and integrity of data is assured
Collection and manipulation of data should be reliable and should reflect consistent processes where needed between departments, to allow for meaningful comparison where appropriate.
Data collected should be complete and captured once ‘right first time’ such that it can be aggregated, analysed and manipulated for decision making purposes
Data should be timely, so that its usefulness for decision making can be maximised.
10 data management principles