Change search
ReferencesLink to record
Permanent link

Direct link
POTENTIALEN AV BIG DATA I FRONT END OF INNOVATION
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
2016 (Swedish)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
The Potential of Big Data in the Front End of Innovation (English)
Abstract [en]

The aim of this study was to explore the potential of Big Data in the Front End of Innovation and develop theory through conceptual work. Specifically research questions were aimed at the potential of Big Data in opportunity identification, opportunity analysis, idea generation and idea selection. When conducting the study, it was found that some vagueness and opposing opinions are present regarding the use of the term Big Data and its definition. The findings of this study focus on what Big Data offers organizations increased abilities in analyzing external environments including competitors, markets and users to facilitate the opportunity identification and ideation processes. As well as the implementation of analytics in opportunity analysis and idea selection as an additional perspective to existing processes.

Abstract [sv]

Målet med detta examensarbete var att utforska potentialen hos Big Data i det som ofta kallas Front End of Innovation, dvs. De tidiga stadiet i innovationsarbetet. Det övergripande målet bröts ned i fyra mer specifika frågor rörande hur Big Data kan användas i identifiering av nya möjlighet, analys av möjligheter, idégenerering och val av idéer. Arbetet inleds med en utforskning av begreppet Big Data i syfte att bygga förståelse och bättre uppfattning om vad begreppet innefattar. Från detta fastställdes att begreppet har implikationer för mjukvara, hårdvara, analys av datamängder samt för ledningsfrågor. Intervjuer med experter inom innovationsledning respektive dataanalys nyanserade begreppet ytterligare. Experternas åsikter la större vikt vid vilken typ av data som samlades in, datans källa, relevansen hos en given analysmetod och hur organisationer implementerade resultaten av analysen. Resultaten i arbetet indikerar att Big Data-analys kan bidra med nya metoder för att öka användarförståelse, samt för datadrivet beslutsfattande vilka, två processer som kan ha positiv påverkan i Front End Innovation.

Place, publisher, year, edition, pages
2016. , 71 p.
Series
, MMK 2016:143 MPI 17
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-192493OAI: oai:DiVA.org:kth-192493DiVA: diva2:968974
Supervisors
Examiners
Available from: 2016-09-13 Created: 2016-09-13 Last updated: 2016-09-13Bibliographically approved

Open Access in DiVA

fulltext(1486 kB)16 downloads
File information
File name FULLTEXT01.pdfFile size 1486 kBChecksum SHA-512
87c35a0c9819dc11cf47dc8a486ccc02151ac5a516cb38c0ca680f3f6d38d7b0914a3845b5a44f5ff21b5e436bb281eebf59b0d6fffaf75b38b3459bd2c6cb32
Type fulltextMimetype application/pdf

By organisation
Machine Design (Dept.)
Mechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 16 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 8 hits
ReferencesLink to record
Permanent link

Direct link