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[{"key": "dc.contributor.advisor", "value": "Holtkamp, Phillipp", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Strengell, Tiina", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2017-04-20T10:46:19Z", "language": "", "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2017-04-20T10:46:19Z", "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:1695336", "language": null, "element": "identifier", "qualifier": "other", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/53636", "language": "", "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4ss\u00e4 pro gradu \u2013 tutkimuksessa tarkoituksena on selvitt\u00e4\u00e4 mit\u00e4 kompenssivaatimuksia data analytiikassa on ja mitk\u00e4 kompetenssit ovat t\u00e4rkeimpi\u00e4 asiakkaalle luodun arvon kannalta. Tutkimuksessa on hy\u00f6dynnetty mixed method \u2013 l\u00e4hestymistapaa, jossa yhdistet\u00e4\u00e4n m\u00e4\u00e4rillisen ja laadullisen tutkimuksen elementtej\u00e4. Empiirinen tutkimus pohjautuu kirjallisuuskatsauksen tuloksiin. Empiirinen tutkimus toteutettiin s\u00e4hk\u00f6isin kyselylomakkein, jotka sis\u00e4lsiv\u00e4t sek\u00e4 strukturoituja ett\u00e4 avoimia kysymyksi\u00e4. Tutkimukseen osallistui 18 henkil\u00f6\u00e4 kolmesta eri yrityksest\u00e4. Strukturoitujen kysymysten vastaukset valmisteltiin kuvailevien tilastomenetelmien avulla ja analysoitiin verraten l\u00f6yd\u00f6ksi\u00e4 kirjallisuuskatsauksen tuloksiin. Avointen vastausten analysointi tehtiin Korossyn Competence-Performance -teorian pohjalta. Data-analyytikoilta vaadittavat kompetenssit olivat kirjallisuudessa ja tutkimuksen empiirisess\u00e4 aineistossa p\u00e4\u00e4osin samankaltaisia. Suurin poikkeus t\u00e4h\u00e4n oli ohjelmointitaidot ja koneoppiminen. Niiden t\u00e4rkeytt\u00e4 painotettiin kirjallisuudessa. Empiirisess\u00e4 aineistossa keskim\u00e4\u00e4r\u00e4inen osaaminen ei ollut erityisen korkea ja vaihteli melko paljon. Siit\u00e4 huolimatta osallistujat eiv\u00e4t kokeneet sit\u00e4 ongelmaksi. Muu tekninen osaaminen vaihteli melko paljon, lukuun ottamatta muutamaa teknist\u00e4 taitoa, jotka toistuivat kaikissa aineistoissa. Tulosten perustella asiakkaan arvon luonnin kannalta t\u00e4rkeimm\u00e4t kompetenssit ovat data-arkkitehtuurin suunnittelu, tiet\u00e4mys datan ja liiketoiminnan yhdist\u00e4misest\u00e4 sek\u00e4 yrityksen strateginen suunnittelu. N\u00e4m\u00e4 kompetenssit tulivat esiin sek\u00e4 yksil\u00f6iden ett\u00e4 yrityksen kompetensseina. Yksil\u00f6t tarvitsevat edell\u00e4 mainittua osaamista, mutta my\u00f6s yrityksen panos on v\u00e4ltt\u00e4m\u00e4t\u00f6n. Yleisesti ottaen, henkil\u00f6kohtaisia piirteit\u00e4 ja liiketoiminnallista osaamista arvostettiin teknist\u00e4 osaamista enemm\u00e4n.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "The purpose of this master\u2019s thesis is to determine the competency requirements of data analytics and identify the most beneficial competencies that create customer value. This study is implemented using a mixed-method approach, which combines qualitative and quantitative methods. A literature review is at the base of the empirical research. The empirical study is done using questionnaires, in which both structured and unstructured questions are used. A total of 18 participants from three different companies are involved in the study. Descriptive statistics and graphs are used to summarize the answers to the structured questions. Descriptions are compared with the literature review findings. Analysis of open questions is carried out using the Competence Performance Theory of Korossy. Competency requirements of data analysts found in empirical research had much in common with the requirements described in the literature. The major exceptions to this are programming skills and machine learning. Their importance is emphasized in the literature. In the empirical data, the knowledge varies quite a lot. Nevertheless, respondents do not see it as a problem. Aside from a few technical skills, which are repeated in all datasets, other skills also vary. The results justify the main competencies needed for the creation of customer value, which are data architecture design, business planning, and knowledge of the company's strategic planning. These competencies were raised as competencies both for individuals and the organization. Individuals need the skills mentioned above, but the company's contribution is also essential. Generally speaking, personal traits and business skills were appreciated more than technical skills.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted using Plone Publishing form by Tiina Strengell (tjstreng) on 2017-04-20 10:46:18.556319. 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 2017-04-20T10:46:19Z\r\nNo. of bitstreams: 2\r\nURN:NBN:fi:jyu-201704202027.pdf: 1968781 bytes, checksum: aaae601209a1282b764bc5657032369b (MD5)\r\nlicense.html: 4837 bytes, checksum: 6be2910bd3f605d5d431e58983378cf0 (MD5)", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2017-04-20T10:46:19Z (GMT). No. of bitstreams: 2\r\nURN:NBN:fi:jyu-201704202027.pdf: 1968781 bytes, checksum: aaae601209a1282b764bc5657032369b (MD5)\r\nlicense.html: 4837 bytes, checksum: 6be2910bd3f605d5d431e58983378cf0 (MD5)\r\n Previous issue date: 2017", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "1 verkkoaineisto (97 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": "eng", "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": "competency requirements", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "organizational performance", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "analytics", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Competitiveness from data and analytics : required competency in organization", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.title.alternative", "value": "Required competency in organization", "language": null, "element": "title", "qualifier": "alternative", "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201704202027", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.date.updated", "value": "2017-04-20T10:46:20Z", "language": "", "element": "date", "qualifier": "updated", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": "fi", "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": 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