Data quality

Data quality refers to the condition of data based on factors such as accuracy, completeness, consistency, reliability, and whether it is fit for its intended purpose. 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". Data is deemed of high quality if it correctly represents the real-world construct to which it refers. 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, businesses may adopt recognised international standards for data quality (See #International Standards for Data Quality below). Data governance can also be used to form agreed upon definitions and standards, including international standards, for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality.

Data quality refers to the condition of data based on factors such as accuracy, completeness, consistency, reliability, and whether it is fit for its intended purpose. 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". Data is deemed of high quality if it correctly represents the real-world construct to which it refers. 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, businesses may adopt recognised international standards for data quality (See #International Standards for Data Quality below). Data governance can also be used to form agreed upon definitions and standards, including international standards, for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality. ==Dimensions of data quality== Drilling down further, those expectations, specifications, and requirements are stated in terms of characteristics or dimensions of the data, such as: accessibility or availability accuracy or correctness comparability completeness or comprehensiveness consistency, coherence, or clarity credibility, reliability, or reputation flexibility plausibility relevance, pertinence, or usefulness timeliness or latency uniqueness validity or reasonableness A systematic scoping review of the literature suggests that data quality dimensions and methods with real world data are not consistent in the literature, and as a result quality assessments are challenging due to the complex and heterogeneous nature of these data. ==Data quality control== Data quality control is the process of controlling the usage of data for an application or a process. This process is performed both before and after a Data Quality Assurance (QA) process, which consists of discovery of data inconsistency and correction. Before: Restricts inputs After QA process the following statistics are gathered to guide the Quality Control (QC) process: Severity of inconsistency Incompleteness Accuracy Precision Missing / Unknown The Data QC process uses the information from the QA process to decide to use the data for analysis or in an application or business process. General example: if a Data QC process finds that the data contains too many errors or inconsistencies, then it prevents that data from being used for its intended process which could cause disruption. Specific example: providing invalid measurements from several sensors to the automatic pilot feature on an aircraft could cause it to crash. Thus, establishing a QC process provides data usage protection. ==Optimum use of data quality== Data Quality (DQ) is a niche area required for the integrity of the data management by covering gaps of data issues. This is one of the key functions that aid data governance by monitoring data to find exceptions undiscovered by current data management operations. Data Quality checks may be defined at attribute level to have full control on its remediation steps. DQ checks and business rules may easily overlap if an organization is not attentive of its DQ scope. Business teams should understand the DQ scope thoroughly in order to avoid overlap. Data quality checks are redundant if business logic covers the same functionality and fulfills the same purpose as DQ. The DQ scope of an organization should be defined in DQ strategy and well implemented. Some data quality checks may be translated into business rules after repeated instances of exceptions in the past. Below are a few areas of data flows that may need perennial DQ checks: Completeness and precision DQ checks on all data may be performed at the point of entry for each mandatory attribute from each source system. Few attribute values are created way after the initial creation of the transaction; in such cases, administering these checks becomes tricky and should be done immediately after the defined event of that attribute's source and the transaction's other core attribute conditions are met. All data having attributes referring to Reference Data in the organization may be validated against the set of well-defined valid values of Reference Data to discover new or discrepant values through the validity DQ check. Results may be used to update Reference Data administered under Master Data Management (MDM). All data sourced from a third party to organization's internal teams may undergo accuracy (DQ) check against the third party data. These DQ check results are valuable when administered on data that made multiple hops after the point of entry of that data but before that data becomes authorized or stored for enterprise intelligence. All data columns that refer to Master Data may be validated for its consistency check. A DQ check administered on the data at the point of entry discovers new data for the MDM process, but a DQ check administered after the point of entry discovers the failure (not exceptions) of consistency. As data transforms, multiple timestamps and the positions of that timestamps are captured and may be compared against each other and its leeway to validate its value, decay, operational significance against a defined SLA (service level agreement). This timeliness DQ check can be utilized to decrease data value decay rate and optimize the policies of data movement timeline. In an organization complex logic is usually segregated into simpler logic across multiple processes. Reasonableness DQ checks on such complex logic yielding to a logical result within a specific range of values or static interrelationships (aggregated business rules) may be validated to discover complicated but crucial business processes and outliers of the data, its drift from BAU (business as usual) expectations, and may provide possible exceptions eventually resulting into data issues. This check may be a simple generic aggregation rule engulfed by large chunk of data or it can be a complicated logic on a group of attributes of a transaction pertaining to the core business of the organization. This DQ check requires high degree of business knowledge and acumen. Discovery of reasonableness issues may aid for policy and strategy changes by either business or data governance or both. Conformity checks and integrity checks need not covered in all business needs, it's strictly under the database architecture's discretion. There are many places in the data movement where DQ checks may not be required. For instance, DQ check for completeness and precision on not–null columns is redundant for the data sourced from database. Similarly, data should be validated for its accuracy with respect to time when the data is stitched across disparate sources. However, that is a business rule and should not be in the DQ scope. Regretfully, from a software development perspective, DQ is often seen as a nonfunctional requirement. And as such, key data quality checks/processes are not factored into the final software solution. Within healthcare , wearable technologies or Body Area Networks, generate large volumes of data. The level of detail required to ensure data quality is extremely high and is often underestimated. This is also true for the vast majority of mHealth apps, EHRs and other health related software solutions. However, some open source tools exist that examine data quality. The primary reason for this, stems from the extra cost involved is added a higher degree of rigor within the software architecture. ==Professional associations== ==See also== ISO 8000 international standard for data quality

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