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State of qualitative or quantitative pieces of information
Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Moreover, data is deemed of high quality if it correctly represents the real world construct to which it refers. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. When this is the case, data governance is used to form agreed upon definitions and standards for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality.

Before the rise of the inexpensive computer data storage, massive mainframe computers were used to maintain name and address data for delivery services. This was so that mail could be properly routed to its destination. The mainframes used business rules to correct common misspellings and typographical errors in name and address data, as well as to track customers who had moved, died, gone to prison, married, divorced, or experienced other life changing events. Government agencies began to make postal data available to a few service companies to cross reference customer data with the National Change of Address registry (NCOA). This technology saved large companies millions of dollars in comparison to manual correction of customer data. Large companies saved on postage, as bills and direct marketing materials made their way to the intended customer more accurately. Initially sold as a service, data quality moved inside the walls of corporations, as low cost and powerful server technology became available. Companies with an emphasis on marketing often focused their quality efforts on name and address information, but data quality is recognized as an important property of all types of data. Principles of data quality can be applied to supply chain data, transactional data, and nearly every other category of data found. For example, making supply chain data conform to a certain standard has value to an organization by: 1) avoiding overstocking of similar but slightly different stock; 2) avoiding false stock out; 3) improving the understanding of vendor purchases to negotiate volume discounts; and 4) avoiding logistics costs in stocking and shipping parts across a large organization. For companies with significant research efforts, data quality can include developing protocols for research methods, reducing measurement error, bounds checking of data, cross tabulation, modeling and outlier detection, verifying data integrity, etc.

Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing activities (e. g. removing outliers, missing data interpolation) to improve the data quality. These activities can be undertaken as part of data warehousing or as part of the database administration of an existing piece of application software.

The use of mobile devices in health, or mHealth, creates new challenges to health data security and privacy, in ways that directly affect data quality. Mobile phones and tablets are used for collection, reporting, and analysis of data in near real time. However, these mobile devices are commonly used for personal activities, as well, leaving them more vulnerable to security risks that could lead to data breaches. Without proper security safeguards, this personal use could jeopardize the quality, security, and confidentiality of health data.

Data quality has become a major focus of public health programs in recent years, especially as demand for accountability increases. Work towards ambitious goals related to the fight against diseases such as AIDS, Tuberculosis, and Malaria must be predicated on strong Monitoring and Evaluation systems that produce quality data related to program implementation. These programs, and program auditors, increasingly seek tools to standardize and streamline the process of determining the quality of data, verify the quality of reported data, and assess the underlying data management and reporting systems for indicators. An example is WHO and MEASURE Evaluation's Data Quality Review Tool WHO, the Global Fund, GAVI, and MEASURE Evaluation have collaborated to produce a harmonized approach to data quality assurance across different diseases and programs.

There are a number of scientific works devoted to the analysis of the data quality in open data sources, such as Wikipedia, Wikidata, DBpedia and other. In the case of Wikipedia, quality analysis may relate to the whole article Modeling of quality there is carried out by means of various methods. Some of them use machine learning algorithms, including Random Forest, Support Vector Machine, and others. Methods for assessing data quality in Wikidata, DBpedia and other LOD sources differ.

The Electronic Commerce Code Management Association (ECCMA) is a member based, international not for profit association committed to improving data quality through the implementation of international standards. ECCMA is the current project leader for the development of ISO 8000 and ISO 22745, which are the international standards for data quality and the exchange of material and service master data, respectively. ECCMA provides a platform for collaboration amongst subject experts on data quality and data governance around the world to build and maintain global, open standard dictionaries that are used to unambiguously label information. The existence of these dictionaries of labels allows information to be passed from one computer system to another without losing meaning.