refers to the condition of a set of values of Qualitative data
or quantitative variables. 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
Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as data volume 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. Data cleansing
may be required in order to ensure data quality.
This list is taken from the online book "Data Quality: High-impact Strategies".
See also the glossary of data quality terms.
Degree of excellence exhibited by the data in relation to the portrayal of the actual scenario.
The state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use.
[ Government of British Columbia]
The totality of features and characteristics of data that bears on its ability to satisfy a given purpose; the sum of the degrees of excellence for factors related to data.
[ Reference-quality Water Sample Data: Notes on Acquisition, Record Keeping, and Evaluation]
The processes and technologies involved in ensuring the conformance of data values to business requirements and acceptance criteria.
Complete, standards based, consistent, accurate and time stamped.
If the ISO 9000:2015 definition of quality is applied, data quality can be defined as the degree to which a set of characteristics of data fulfills requirements. Examples of characteristics are: completeness, validity, accuracy, consistency, availability and timeliness. Requirements are defined as the need or expectation that is stated, generally implied or obligatory.
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.
There are a number of theoretical frameworks for understanding data quality. A systems-theoretical approach influenced by American pragmatism expands the definition of data quality to include information quality, and emphasizes the inclusiveness of the fundamental dimensions of accuracy and precision on the basis of the theory of science (Ivanov, 1972). One framework, dubbed "Zero Defect Data" (Hansen, 1991) adapts the principles of statistical process control to data quality. Another framework seeks to integrate the product perspective (conformance to specifications) and the Customer service
perspective (meeting consumers' expectations) (Kahn et al. 2002). Another framework is based in semiotics
to evaluate the quality of the form, meaning and use of the data (Price and Shanks, 2004). One highly theoretical approach analyzes the ontological
nature of information systems to define data quality rigorously (Wand and Wang, 1996).
A considerable amount of data quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. These dimensions commonly include accuracy, correctness, currency, completeness and relevance. Nearly 200 such terms have been identified and there is little agreement in their nature (are these concepts, goals or criteria?), their definitions or measures (Wang et al., 1993). Software engineers may recognize this as a similar problem to "ilities".
MIT has a Total Data Quality Management program, led by Professor Richard Wang, which produces a large number of publications and hosts a significant international conference in this field (International Conference on Information Quality, ICIQ). This program grew out of the work done by Hansen on the "Zero Defect Data" framework (Hansen, 1991).
In practice, data quality is a concern for professionals involved with a wide range of information systems, ranging from data warehousing and business intelligence to customer relationship management and supply chain management. One industry study estimated the total cost to the U.S. economy of data quality problems at over U.S. $600 billion per annum (Eckerson, 2002). Incorrect data – which includes invalid and outdated information – can originate from different data sources – through data entry, or data migration and conversion projects.
In 2002, the USPS and PricewaterhouseCoopers released a report stating that 23.6 percent of all U.S. mail sent is incorrectly addressed.
One reason contact data becomes stale very quickly in the average database – more than 45 million Americans change their address every year.
In fact, the problem is such a concern that companies are beginning to set up a data governance team whose sole role in the corporation is to be responsible for data quality. In some organizations, this data governance function has been established as part of a larger Regulatory Compliance function - a recognition of the importance of Data/Information Quality to organizations.
Problems with data quality don't only arise from incorrect data; inconsistent data is a problem as well. Eliminating shadow system and centralizing data in a warehouse is one of the initiatives a company can take to ensure data consistency.
Enterprises, scientists, and researchers are starting to participate within data curation communities to improve the quality of their common data.
[E. Curry, A. Freitas, and S. O'Riáin, "The Role of Community-Driven Data Curation for Enterprises", in Linking Enterprise Data, D. Wood, Ed. Boston, Mass.: Springer US, 2010, pp. 25-47.]
The market is going some way to providing data quality assurance. A number of vendors make tools for analyzing and repairing poor quality data in situ, service providers can clean the data on a contract basis and consultants can advise on fixing processes or systems to avoid data quality problems in the first place. Most data quality tools offer a series of tools for improving data, which may include some or all of the following:
Data profiling - initially assessing the data to understand its quality challenges
Data standardization - a business rules engine that ensures that data conforms to quality rules
Geocoding - for name and address data. Corrects data to U.S. and Worldwide postal standards
Record linkage - a way to compare data so that similar, but slightly different records can be aligned. Matching may use "fuzzy logic" to find duplicates in the data. It often recognizes that "Bob" and "Robert" may be the same individual. It might be able to manage "householding", or finding links between spouses at the same address, for example. Finally, it often can build a "best of breed" record, taking the best components from multiple data sources and building a single super-record.
Monitoring - keeping track of data quality over time and reporting variations in the quality of data. Software can also auto-correct the variations based on pre-defined business rules.
Batch and Real time - Once the data is initially cleansed (batch), companies often want to build the processes into enterprise applications to keep it clean.
There are several well-known authors and self-styled experts, with Larry English perhaps the most popular guru. In addition, IQ International - the International Association for Information and Data Quality was established in 2004 to provide a focal point for professionals and researchers in this field.
ISO 8000 is an international standard for data quality.
Data quality assurance
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.
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.
After QA process the following statistics are gathered to guide the Quality Control (QC) process:
Severity of inconsistency
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.
[O'Donoghue, John, and John Herbert. "Data management within mHealth environments: Patient sensors, mobile devices, and databases". Journal of Data and Information Quality ( JDIQ) 4.1 (2012): 5.] 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.
Health data security and privacy
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. mHealth is an increasingly important strategy for delivery of health services in low- and middle-income countries.
[MEASURE Evaluation. (2017) Improving data quality in mobile community-based health information systems: Guidelines for design and implementation
/ref> 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.] [Wambugu, S. & Villella, C. (2016). mHealth for health information systems in low- and middle-income countries: Challenges and opportunities in data quality, privacy, and security
Data quality in public health
/ref> 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. [The World Health Organization (WHO). (2009). Monitoring and evaluation of health systems strengthening. Geneva, Switzerland: WHO. Retrieved from http://www.who.int/healthinfo/HSS_MandE_framework_Nov_2009.pdf]
Open data quality
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
or its separate parts (such as infobox ). Modeling of quality there is carried out by means of various methods. Some of them use data mining algorithms, including Random Forest , Support Vector Machine and other. There are also scientific works using synthetic measure for assessing quality of Wikipedia articles in different languages.
Methods for assessing data quality in Wikidata, DBpedia and other LOD sources differ
- IQ International—the International Association for Information and Data Quality
- IQ International is a not-for-profit, vendor neutral, professional association formed in 2004, dedicated to building the information and data quality profession.
Dai, Wei, Kenji Yoshigoe, and William Parsley. "Improving Data Quality Through Deep Learning and Statistical Models." Information Technology-New Generations. Springer, Cham, 2018. 515-522.
Baamann, Katharina, "Data Quality Aspects of Revenue Assurance", Article
Eckerson, W. (2002) "Data Warehousing Special Report: Data quality and the bottom line", Article
Kristo Ivanov (1972) "Quality-control of information: On the concept of accuracy of information in data banks and in management information systems". The University of Stockholm and The Royal Institute of Technology. Doctoral dissertation.
Hansen, M. (1991) Zero Defect Data, MIT. Masters thesis 
Kahn, B., Strong, D., Wang, R. (2002) "Information Quality Benchmarks: Product and Service Performance," Communications of the ACM, April 2002. pp. 184–192. Article
Price, R. and Shanks, G. (2004) A Semiotic Information Quality Framework, Proc. IFIP International Conference on Decision Support Systems (DSS2004): Decision Support in an Uncertain and Complex World, Prato. Article
Redman, T. C. (2008) Data Driven: Profiting From Our Most Important Business Asset
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