| 000 | 03716nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-38992-9 | ||
| 003 | DE-He213 | ||
| 005 | 20220801222145.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 160524s2016 sz | s |||| 0|eng d | ||
| 020 |
_a9783319389929 _9978-3-319-38992-9 |
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| 024 | 7 |
_a10.1007/978-3-319-38992-9 _2doi |
|
| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aTEC009000 _2bisacsh |
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| 072 | 7 |
_aUYQ _2thema |
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| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aCorea, Francesco. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _960007 |
|
| 245 | 1 | 0 |
_aBig Data Analytics: A Management Perspective _h[electronic resource] / _cby Francesco Corea. |
| 250 | _a1st ed. 2016. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
|
| 300 |
_aXIII, 48 p. 7 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aStudies in Big Data, _x2197-6511 ; _v21 |
|
| 505 | 0 | _aIntroduction -- What Data Science Means to the Business -- Key Data Challenges to Strategic Business Decisions -- A Chimera Called Data Scientist: Why they don’t Exist (but they will in the Future) -- Future Data Trends -- Where are we Going? The Path Toward an Artificial Intelligence -- Conclusions. | |
| 520 | _aThis book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.). The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively. | ||
| 650 | 0 |
_aComputational intelligence. _97716 |
|
| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 0 |
_aQuantitative research. _94633 |
|
| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aData Analysis and Big Data. _960008 |
| 710 | 2 |
_aSpringerLink (Online service) _960009 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319389912 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319389936 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319817866 |
| 830 | 0 |
_aStudies in Big Data, _x2197-6511 ; _v21 _960010 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-38992-9 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c80460 _d80460 |
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