Question.4167 - Review the Case Study in Chapter 1: Do You Trust Your Data?After reviewing the case, answer the following questions. Be sure to use outside resources and your textbook to validate your responses. Why do you believe that data can be inaccurate? What can a business do to ensure data is correct? Explain how bad data will impact information, business intelligence, and knowledge. Have you ever made a decision based on bad data? If so, please share how you could have verified the data quality. Argue for or against the following statement "It is better to make a business decision with bad data than with no data". This assignment should be written using APA format and should include a title page, headings, conclusion, and references.Don't forget to submit your assignment for grading.
Answer Below:
Introduction xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx This xxxxxxxxxx will xxxxxxxx the xxxx study xxxxxxxxxxx how xxxx can xx trusted xx how xxx association xx bad xxxx may xxxxxx the xxxxxxx knowledge xx nexus xxxx this xxxx case xxxxx from xxxxxxx One xxxxxxx on xxx data xxxxxx fact-based xxxxxxxxx especially xxxx the xxxx of xxxxxxx where xxx needs xx rely xx the xxxx in xxxxx to xxxxx the xxxxxxxx decision xxxxx with xxxx evaluation xx bad xxxx and xxxxxxxxxxxxxxx properties xx bad xxxx across xxx assignment xxx do xxx Believe xxxx Can xx Inaccurate xxxx can xx inaccurate xxx to xxxxxxx factors xxxxxxxxx human xxxxx incomplete xxxx outdated xxxxxxxxxxx data xxxxxxxxxxx errors xxx lack xx standardization xx nexus xxxx this xxxxxxxx in xxxx entry xxx occur xxxxxxx various xxxxxx such xx typos xxxxxxxxx or xxxxxxxxx formulas xxx instance xx seen xx the xxxxxx Olympics xxx University xx Toledo xxxxx Baltzan xxx case xxxxx highlights x typo xx the xxxxxxxxxxx formula xxxx led xx an xxxxxxxxxxxxxx of xxxxxxxxxx with xxxxxxxxxxxx revenue xx approximately xxxxxxx USD xx the xxxxx hand xxxxxxx values xx incomplete xxxxxxxx can xxxx affect xxx results xxx may xxxx to xxxxxxxxx conclusions xx evident xx a xxxxxxx negative xxxx on x dividend xxxxxx costing xxx financial xxxxxxx a xxxx of xxxxxxx Baltzan xxxxxxxxx using xxx data xx inconsistent xxxx collection xxxxxxx may xxxxxx discrepancies xxxxxxx to xxxxxxxxxx or xxx data xxxx can x Business xx to xxxxxx Data xx Correct xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx In xxxxx to xxxxxx that xxx data xxxxxxxxx is xxxxxxx a xxxxxxxx needs xx focus xx data xxxxxxxxxx quality xxxxxxx processes xxxxxxxx employees xxxxxxxxx tools xxxxxxxxxxxxxxx and xxxxxxx and xxxxxxx control xxxxxx et xx Similarly xxxxxxxxxxxx validation xxxxx at xxx point xx entry xxx focus xx catching xxxxxx early xxxxx regular xxxxxx data xxxxxxxxx and xxxxxxxxx checks xxx help xx maintaining xxxxxxxx in xxxx collection xx the xxxxx hand xxxxxxxx the xxxxxxxxx with xxxxxx data xxxxxxxxxx and xxxxx with xxxxxxxxxxx utilization xx data xxxxxxxxxx software xxx focus xx error xxxxxxxxx features xxxxxxx it xxxxx be xxxx essential xx maintain xxxxxxxxxx versions xx the xxxxxxxxx data xxx cross-verification xx the xxxx term xxxxxxx How xxx Data xxxx Impact xxxxxxxxxxx Business xxxxxxxxxxxx and xxxxxxxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xx consideration xx the xxx data xx affects xxx information xxxxxxxxx from xxx raw xxxx leading xx misleading xxxxxxxxx reports xxx dashboards xx the xxxxx hand xxxxxxxx intelligence xxxxxx mainly xx the xxxxxxx in xxxxx to xxxxxxxx insights xxxx the xxxxxx however xx the xxxx has xxxx flawed xxx overall xxxxxxxx will xx unreliable xxxxxxx to xxxx strategic xxxxxxxxx Gade xxxxxxxxx knowledge xxx been xxxxx on xxxxxxxx and xxxxxxxx derived xxxx information xxxxx bad xxxx undermines xxx foundation xx organizational xxxxxxxx and xxxxxxxxx planning xxxx You xxxx Made x Decision xxxxx on xxx Data xx various xxxxxxxxx bad xxxx decisions xxx be xxxxxxx when x manager xxx overestimate xxxxx availability xxxxx on xxxxxxxx shift xxxx This xxxxxxxx has xxxxxxxx several xxxxx leading xx understaffing xxxxxx peak xxxxx In xxxxx to xxxxxxx this xxxxxxxxx data xxxxxxxx through xxxxxxx updates xxx cross-checking xxx attendance xxxxxxx would xxxx been xxxxxxxxx Argue xxx or xxxxxxx the xxxxxxxxx ldquo xx is xxxxxx to xxxx a xxxxxxxx Decision xxxx Bad xxxx Than xxxx No xxxx rdquo xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx In xxxxxxxxxx with xxxx et xx decision-making xxxx bad xxxx can xx very xxxxxxx since xx can xxxxxxxx an xxxxxxxx of xxxxxxxxxx compounding xxxxxx financial xxxx and xxxxxxxxxx damage xxx instance xxxxxxxxx based xx bad xxxx insights xxx trigger x chain xx poor xxxxxxxxx moves xxxxx bad xxxx can xxxxxx the xxxxxxxx of xxxxxxxxx leading xx misguided xxxxxx Similarly xxxxxxxxx data xx business xxxxxxxxx can xxxxxx stakeholder xxxxx damaging xxxxx credibility xxx resulting xx massive xxxxxxxxx loss xx the xxxxxxxx no xxxx encourages xxxxxxx and xx this xxxxxxxx prompting xxxxxxx investigation xxx more xxxxxxxxx thinking xxxxxx than xxxxx decisions xxxxxxxxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxx nbsp xxxxx it xxx be xxxxxxxxx that xxxx accuracy xx critical xxx effective xxxxxxxxxxxxxxx where xxxxxxxxxx need xx prioritize xxxx quality xxxxxxxxxx through xxxxxxxxxx standardization xxx employee xxxxxxxx in xxxxx to xxxxx costly xxxxxxxx In xxxx scenario xxx data xx referred xx in xxx Case xxxxx in xxxxxxx One xxx lead xx poor xxxxxxxxx financial xxxxxx and xxxxxx to xxx organizational xxxxxxxxxx while xxxxxxxx data xxxxxxxxxxx provides x foundation xxx sound xxxxxxxxx choices xxxx nbsp xxxxxxxxxxxxxxxxx P xxxxxxxx driven xxxxxxxxxx McGraw-Hill xxxx K x Data xxxxxxx Metrics xxx the xxxxxx Enterprise x Data xxxxxxxxx Perspective xx Journal xx Artificial xxxxxxxxxxxx Ngcobo x Bhengu x Mudau x Thango x amp xxxxxx M xxxxxxxxxx data xxxxxxxxxx Types xxxxxxx and xxxxxxxxx applications xx enhance xxxxxxxx performance-a xxxxxxxxxx review xxxxxxxxxx Review xxxxxxxxx Peng x Ye x amp xx J xxxxx the xxxxx of xxxxxxxxx Simulation xx Influence xxxxxxxxx of xxxxxxxxx Bias xx To-Collude xxxxxxxx Making xxxxxxxxx
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