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Ultima descărcare din IBN: 2024-05-14 17:35 |
SM ISO690:2012 GAGAUZ, Valeriu. Big data in marketing: opportunities and risks. In: Tendințe contemporane ale dezvoltării științei: viziuni ale tinerilor cercetători, 1-3 iulie 2021, Chişinău. Chișinău, Republica Moldova: Complexul Editorial, INCE, 2021, p. 177. ISBN 978-9975-3486-4-5. |
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Tendințe contemporane ale dezvoltării științei: viziuni ale tinerilor cercetători 2021 | ||||||
Conferința "Tendințe contemporane ale dezvoltării științei: viziuni ale tinerilor cercetători" Chişinău, Moldova, 1-3 iulie 2021 | ||||||
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JEL: M31 | ||||||
Pag. 177-177 | ||||||
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Rezumat | ||||||
Nowadays, marketing is becoming more and more personalized, and the collection, processing and analytics of big data facilitates that. This paper examines the advantages and disadvantages of using big data in marketing, as well as the level of their use in Moldova. The research method is a qualitative study. The research results show that the main suppliers of big data in Moldova are search engines. They have access to primary data, sufficient technological base for their processing, as well as creating new services for analytics and algorithms for developing conclusions and trends. The company's clients do not collect big data. Simple data is rarely processed using learning algorithms or neural networks (AI). Basically, big data (secondary) is available only that which is collected by the search and analytical systems like Google, Yandex. Clients order analytics of simple (homogeneous, formalized) data, which the company's specialists process in MS Power Bi. Machine learning technologies, artificial intelligence, and big data are currently less in demand in Moldova than abroad. Banks of Moldova possess large amounts of data, but due to the structure of their storage, security issues are not processed by neural networks and machine learning. Simpals uses machine learning to analyze, evaluate and moderate comments on their web platform Point.md and for advertisement texts on 999.md. For these purposes, they use third-party services like Google Query. Orange and Moldcell collect, store and analyze data, for which they use technologies such as HDFS, MapReduce, YARN, Flume, Scoop, Oozie, Spark. |
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Cuvinte-cheie big data, data analytics, data-driven marketing, большие данные, аналитика данных, Маркетинг |
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