03647nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001500153072001500168072002300183072001400206100007300220245015100293250001800444264006900462300006400531336002600595337002600621338003600647347002400683490005000707505030600757520154401063650001402607650003502621650002402656650002502680650004602705650002702751650001402778650002002792650001402812650002502826650001802851650003202869710003402901773002602935776003602961776003602997830005003033856004603083978-3-031-71871-7DE-He21320251030113250.0cr nn 008mamaa241029s2024    sz |    s    |||| 0|eng d  a97830317187177 a10.1007/978-3-031-71871-72doi 4aQA76.9.B45 7aUN2bicssc 7aCOM0210002bisacsh 7aUN2thema1 aYu, Haiyan.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aData Quality Management in the Data Ageh[electronic resource] :bExcellence in Data Quality for Enhanced Digital Economic Growth /cby Haiyan Yu.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIX, 90 p. 26 illus., 17 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Service Science,x2731-37510 aIntroduction of data quality management -- Quality management in Data Science -- Pillars of data quality management -- Tools of data quality management -- Experimental designs for data quality control -- High-quality data collection in data markets -- Ghost data in data quality management -- Summary.  aThis book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets. 0aBig data. 0aBusiness information services. 0aService industries. 0aDatabase management. 0aArtificial intelligencexData processing. 0aQuantitative research.14aBig Data.24aIT in Business.24aServices.24aDatabase Management.24aData Science.24aData Analysis and Big Data.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303171870008iPrinted edition:z9783031718724 0aSpringerBriefs in Service Science,x2731-375140uhttps://doi.org/10.1007/978-3-031-71871-7