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 assignment xxxx evaluate xxx case xxxxx considering xxx data xxx be xxxxxxx or xxx the xxxxxxxxxxx of xxx data xxx affect xxx overall xxxxxxxxx In xxxxx with xxxx this xxxx study xxxx Chapter xxx focuses xx how xxxx drives xxxxxxxxxx decisions xxxxxxxxxx with xxx role xx manager xxxxx one xxxxx to xxxx on xxx data xx order xx drive xxx business xxxxxxxx Hence xxxx this xxxxxxxxxx of xxx data xxx decision-making xxxxxxxxxx in xxx data xxxxxx the xxxxxxxxxx Why xx you xxxxxxx Data xxx be xxxxxxxxxx Data xxx be xxxxxxxxxx due xx several xxxxxxx including xxxxx error xxxxxxxxxx data xxxxxxxx information xxxx integration xxxxxx and xxxx of xxxxxxxxxxxxxxx In xxxxx with xxxx mistakes xx data xxxxx can xxxxx through xxxxxxx points xxxx as xxxxx omissions xx incorrect xxxxxxxx for xxxxxxxx as xxxx in xxx London xxxxxxxx and xxxxxxxxxx of xxxxxx cases xxxxxxx The xxxx study xxxxxxxxxx a xxxx in xxx spreadsheet xxxxxxx that xxx to xx overestimation xx enrollment xxxx overinflated xxxxxxx of xxxxxxxxxxxxx million xxx On xxx other xxxx missing xxxxxx or xxxxxxxxxx datasets xxx even xxxxxx the xxxxxxx and xxx lead xx incorrect xxxxxxxxxxx as xxxxxxx in x missing xxxxxxxx sign xx a xxxxxxxx report xxxxxxx the xxxxxxxxx company x loss xx billion xxxxxxx Similarly xxxxx old xxxx or xxxxxxxxxxxx data xxxxxxxxxx methods xxx create xxxxxxxxxxxxx leading xx inaccurate xx bad xxxx What xxx a xxxxxxxx Do xx Ensure xxxx is xxxxxxx 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 In xxxxxxxxxxxxx of xxx bad xxxx it xxxxxxx the xxxxxxxxxxx generated xxxx the xxx data xxxxxxx to xxxxxxxxxx summaries xxxxxxx and xxxxxxxxxx On xxx other xxxx Business xxxxxxxxxxxx relies xxxxxx on xxx process xx order xx generate xxxxxxxx into xxx report xxxxxxx if xxx data xxx been xxxxxx the xxxxxxx insights xxxx be xxxxxxxxxx leading xx poor xxxxxxxxx decisions xxxx Similarly xxxxxxxxx has xxxx built xx patterns xxx insights xxxxxxx from xxxxxxxxxxx where xxx data xxxxxxxxxx the xxxxxxxxxx of xxxxxxxxxxxxxx learning xxx strategic xxxxxxxx Have xxx Ever xxxx a xxxxxxxx Based xx Bad xxxx In xxxxxxx instances xxx data xxxxxxxxx can xx evident xxxx a xxxxxxx may xxxxxxxxxxxx staff xxxxxxxxxxxx based xx outdated xxxxx data xxxx incident xxx happened xxxxxxx times xxxxxxx to xxxxxxxxxxxxx during xxxx hours xx order xx prevent xxxx verifying xxxx accuracy xxxxxxx regular xxxxxxx and xxxxxxxxxxxxxx the xxxxxxxxxx records xxxxx have xxxx essential xxxxx for xx Against xxx Statement xx is xxxxxx to xxxx a xxxxxxxx Decision xxxx Bad xxxx Than xxxx No 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 Hence xx can xx concluded xxxx data xxxxxxxx is xxxxxxxx for xxxxxxxxx decision-making xxxxx businesses xxxx to xxxxxxxxxx data xxxxxxx management xxxxxxx validation xxxxxxxxxxxxxxx and xxxxxxxx training xx order xx avoid xxxxxx mistakes xx this xxxxxxxx bad xxxx as xxxxxxxx to xx the xxxx Study xx Chapter xxx may xxxx to xxxx decisions xxxxxxxxx losses xxx damage xx the xxxxxxxxxxxxxx reputation xxxxx accurate xxxx essentially xxxxxxxx a xxxxxxxxxx for xxxxx strategic xxxxxxx ReferencesBaltzan x Business xxxxxx technology xxxxxxxxxxx Gade x R xxxx Quality xxxxxxx for xxx Modern xxxxxxxxxx A xxxx Analytics xxxxxxxxxxx MZ xxxxxxx of xxxxxxxxxx Intelligence xxxxxx K xxxxxx S xxxxx A xxxxxx B xxxxxx M xxxxxxxxxx data xxxxxxxxxx Types xxxxxxx and xxxxxxxxx applications xx enhance xxxxxxxx performance-a xxxxxxxxxx review xxxxxxxxxx Review xxxxxxxxx Peng x Ye x Li x Break xxx Cycle xx Collusion xxxxxxxxxx to xxxxxxxxx Mechanism xx Cognitive xxxx on xxxxxxxxxx Decision xxxxxx Buildings
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