000			01979cam a2200373Ka 4500
001			394134
005			20211207111742.0
006			m o d
007			cr |n|||||||||
008			180928s2013 vtu ob 001 0 ENG d
020			_a9780123977540
020			_z9781283933186
020			_z1283933187
020			_z9780123970336
020			_z0123970334
035			_a394134
035			_a(OCoLC)823722256
100	1	0	_aSebastian-Coleman, Laura
245	1	0	_aMeasuring data quality for ongoing improvement : _ba data quality assessment framework / _cLaura Sebastian-Coleman.
260			_aBurlington : _bElsevier Science, _c2013.
300			_a1 online resource
504			_aIncludes bibliographical references and index.
520	3		_aConcepts and definitions -- DQAF concepts and measurement types -- Data assessment scenarios -- Applying the DQAF to data requirements -- A strategic approach to data quality -- The DQAF in dept
520			_aThe Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT.
650		0	_aData structures (Computer science)
650		0	_aDatabases _xQuality control
650		0	_aDatabase management.
650		7	_aREFERENCE _xResearch. _2bisacsh
650		7	_aData structures (Computer science) _2fast
650		7	_aDatabase management _2fast
650		7	_aDatabases _xQuality control _2fast
856	4		_uhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=485118
942			_cEBOOK _n0
999			_c61835 _d61835