The Role of Design for Six Sigma in Successful Product Development

Soderborg, Nathan

doi:10.4271/2010-01-0711

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Knowledgeintheformofassessments,expertise,ratingsandrecommendationsistheprimary resourceforsuccessfulinvestmentbanks.Thisisduetoanenormousvarietyofinternalaswellas externaldata,whichneedtobeprocessedinordertocometointelligentbusinessdecisions (Soderborg, 2017). Atthesametime,theoverallbranchisunderhighpressurethroughnewdigitalbusinessmodels (e.g.bankingapps,blockchaintechnology,tonamesometends).Theintelligentapplicationofdata sciencetechniquesiscurrentlydiscussedasprimarywaytoorganizetheknowledgeresourcebestto strategicallydistinguishitselffromthecompetition (Tapscott&Tapscott,2016).Forexample,for consideringtheactualvalueofcompanies,manystructuredinformationsuchassales,andnumberof employees,locations,andproductsshouldbeconsidered.Atthesametimesemi-orevenun-structured informationsuchassocialmediareviews,buyingrecommendations,ortestportalsresultscouldbe used.Goingonestepfurther,weaksignalssuchasreviewsonemployerportalsorevenhardfacts likeproductionefficiencyinasmartfactoryarenowavailableforinformationprocessing (Viney& Phillips2015;Fahamietal.2015;Witkowska,1999).Whatstandsout:Thereareinnovativeideasfor datausageavailablethatbringnewusecaseswithit.Furthermore,rigidsystemsandsimpledatabase queriesneedtoberethoughtinfavorofflexibledatascienceknowledgemanagementtechniquesthat makesenseoflinkinginternalandexternaldataforgoodknowledge.…”

Section: Foundationsmentioning

confidence: 99%

Data Science Techniques in Knowledge-Intensive Business Processes

Lederer

Riedl

2020

International Journal of Data Analytics

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The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.

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Section: Foundationsmentioning

confidence: 99%