Knowledge Base about the database industry: database software, database management systems, database application development, data warehouse, data mining, business intelligence, OLAP, database design, data modeling, data integration, database security.
Data Quality refers to the quality of data. Data are of high quality "if they are fit for their intended uses in operations, decision making and planning" (J.M. Juran). Alternatively, the data are deemed of high quality if they correctly represent the real-world construct to which they refer. These two views can often be in disagreement, even about the same set of data used for the same purpose.
Most data technologies have grown out of early desire to accurately send mail. Before the rise of the inexpensive server, massive mainframe computers were used to maintain name and address data so that the mail could properly arrive at its destination. The mainframes used business rules to correct common misspellings and typos 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 run customer against the national change of address registry (NCOA). This technology saved large companies millions of dollars compared to manually correcting customer data. Large companies saved on postage, as bills and direct marketing made its 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. Although most companies think of name and address when they think of data quality, data quality is recognized today as the act of improving all types of data, such as supply chain data, ERP data, transactional data, and more. 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; or 2) improving the understanding of vendor purchases to negotiate volume discounts; or 3) avoiding logistics costs in stocking and shipping parts across a large organization. While name and address data has a clear standard as defined by local postal authorities, other types of data have few recognized standards. There is a movement in the industry today to standardize certain non-address data. The non-profit group GS1 is among those groups that are spearheading this movement.
Global-BI is a premier Business Intelligence (BI) solution provider specializing in data warehousing, data preparation (cleansing, profiling, and ETL), and analytics solution development. We deliver high-quality and high-impact solutions fast and at extremely competitive prices.
Datanamic is a privately owned company based in Leiden, the Netherlands. Datanamic Solutions B.V. creates innovative database tools to assist the simplification and automation of database development. To learn more about us please visit our website at www.datanamic.com.
DB Data Generator is a simple, powerful data generator that allows developers to easily populate databases with thousands of rows of meaningful and syntactically correct test data for database testing purposes.
Data Quality Journal: news, conference and other resources.
