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[{"key": "dc.contributor.advisor", "value": "Luoma, Eetu", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Kaisto, Heini", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2017-06-07T18:06:04Z", "language": "", "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2017-06-07T18:06:04Z", "language": "", "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2017", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.other", "value": "oai:jykdok.linneanet.fi:1703249", "language": null, "element": "identifier", "qualifier": "other", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/54363", "language": "", "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Massadata on suuresti puhuttava lis\u00e4arvon luomisen l\u00e4hde niin tieteellisille yhteis\u00f6ille kuin yrityksillekin. Massadatan k\u00e4site el\u00e4\u00e4 kehittyv\u00e4n teknologian mukana, mutta tavallisimmillaan sit\u00e4 voi kuvata suurena m\u00e4\u00e4r\u00e4n\u00e4 dataa, joka on monimuotoista ja kasvaa suurella nopeudella. Massadata mahdollistaa paljon liiketoiminnassa hy\u00f6dynnett\u00e4v\u00e4ss\u00e4 analytiikassa, kuten liiketoimintatiedon hallinnan ja analytiikan prosesseissa sek\u00e4 asiakasanalytiikassa. Asiakasanalytiikassa massadata mahdollistaa syv\u00e4llisemm\u00e4n asiakasymm\u00e4rryksen saavuttamisen datasta ja sen hy\u00f6dynt\u00e4misen asiakassuhdehallinnasta aina merkityksellisemm\u00e4n asiakassegmentoinnin tekemiseen. Tutkimuksessa k\u00e4sitell\u00e4\u00e4n ensin massadataa sen erilaisten m\u00e4\u00e4ritelmien kautta sek\u00e4 sen tunnettuja etuja ja hy\u00f6tyj\u00e4, jonka j\u00e4lkeen n\u00e4it\u00e4 k\u00e4sitell\u00e4\u00e4n suhteessa massadatan hy\u00f6dynt\u00e4miseen ja mahdollisuuksiin liiketoimintatiedon hallinnassa ja analytiikassa. Tutkielmassa k\u00e4sitell\u00e4\u00e4n my\u00f6s asiakasanalytiikkaa omana kokonaisuutenaan kattaen sen m\u00e4\u00e4ritelm\u00e4n, asiakassegmentoinnin suhde asiakasanalytiikkaan sek\u00e4 parhaat keinot ja ongelmakohdat asiakassegmentoinnin toteuttamisessa. Seuraavaksi tutkimuksessa esitell\u00e4\u00e4n toteutettu empiirinen tutkimus, jonka tavoitteena oli m\u00e4\u00e4ritt\u00e4\u00e4, onko tapausyrityksell\u00e4 kirjallisuudesta l\u00f6ydettyjen m\u00e4\u00e4ritelmien mukaan massadataa ja jos on, l\u00f6ytyyk\u00f6 yrityksen tavasta hy\u00f6dynt\u00e4\u00e4 massadataa yhtym\u00e4kohtia tai merkitt\u00e4vi\u00e4 poikkeuksia kirjallisuudessa esitettyihin tapoihin ja kirjallisuuden pohjalta laadittuun viitekehykseen. Tutkimus toteutettiin teemahaastatteluna, johon haastateltaviksi valittiin tapausyrityksen ty\u00f6ntekij\u00f6it\u00e4, jotka ovat tekemisiss\u00e4 asiakasdatan tai -analytiikan kanssa. Tutkimuksen tuloksina havaittiin, ettei tapausyrityksess\u00e4 ole massadataa, mutta se voisi hy\u00f6ty\u00e4 joistain massadatalle ominaisista osaratkaisuista.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Big Data is a widely-discussed topic on creating additional value for both scientific associations and companies. The definition of Big Data changes along with the technical environment, but usually it\u2019s described as a high volume of data that has variety and accumulates quickly. Big Data enables a lot in analytics processes in Business Intelligence and Analysis, as well as in customer analytics. In customer analytics, Big Data makes it possible to gain a deeper understanding of the customers, and use it to better the customer relationship functions as well as using it to make more meaningful customer groups in customer segmentation process. The study first presents the definitions for Big Data and the problems and benefits with it. After this the study dedicates a whole chapter to customer analytics \u2013 its definition, how it\u2019s related to customer segmentation and the bestknown practices and problems in customer segmentation. Next the framework for the empirical research is presented. The study was done as an interview survey where employees of the case company that dealt customer data or analyzed it were chosen for it. The research\u2019s aim was to find out whether or not the case study actually has Big Data and if so, does the way the case company uses Big Data for its customer analytics have similarities or remarkable differences to the ways presented in literature and the framework built in the study based on the literature. The results of the research were that the company did not have big data, but could benefit even in their current status from using some of the solutions used for Big Data.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted using Plone Publishing form by Heini Kaisto (heelkais) on 2017-06-07 18:06:03.524234. Form: Pro gradu -lomake (https://kirjasto.jyu.fi/julkaisut/julkaisulomakkeet/pro-gradu-lomake). JyX data: [jyx_publishing-allowed (fi) =True]", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2017-06-07T18:06:03Z\r\nNo. of bitstreams: 2\r\nURN:NBN:fi:jyu-201706072732.pdf: 888265 bytes, checksum: d0f16cb06a46f315a5bd88a91ab60f75 (MD5)\r\nlicense.html: 4820 bytes, checksum: 99a12a426f55aaa02023db44bbcf2d6f (MD5)", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2017-06-07T18:06:04Z (GMT). No. of bitstreams: 2\r\nURN:NBN:fi:jyu-201706072732.pdf: 888265 bytes, checksum: d0f16cb06a46f315a5bd88a91ab60f75 (MD5)\r\nlicense.html: 4820 bytes, checksum: 99a12a426f55aaa02023db44bbcf2d6f (MD5)\r\n Previous issue date: 2017", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "1 verkkoaineisto (82 sivua)", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "Massadata", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", 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