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Didžiųjų duomenų naudojimas klientui pažinti / Model of the big data use for customer cognition

Abstract

In a customer-oriented market, understanding customer behavior is an important determinant of the success of an organization. An organization that strives to survive and succeed can not ignore increasing amounts of data – big data. Big data is complex data arrays that are difficult to process using traditional data processing applications. Optimal analysis of such data enables organizations for better understanding of its customers, improve the decision-making process and increase its competitive advantage. It is important for the organization to understand how to use big data, which processing tools and models to apply. This article analyzes the concepts and evolution of big data, the risks of exploitation, mining methods and applied models. Applied methods: systematic, logical analysis of information sources, comparison of information, systemization.


Santrauka


Į klientus orientuotoje rinkoje klientų elgsenos supratimas yra svarbus veiksnys, lemiantis organizacijos sėkmę. Organizacija, siekianti išlikti ir sėkmingai egzistuoti, negali ignoruoti nuolat didėjančių duomenų kiekių – didžiųjų duomenų. Didieji duomenys – sudėtingi duomenų masyvai, kuriuos sunku apdoroti naudojant tradicines duomenų apdorojimo programas. Optimaliai išanalizuoti tokie duomenys suteikia galimybę geriau pažinti klientus, tobulinti sprendimų priėmimo procesą, didinti konkurencinį pranašumą. Organizacijai svarbu suprasti, kaip panaudoti didžiuosius duomenis, kokias apdorojimo priemones ir modelius taikyti. Šiame straipsnyje analizuojamos didžiųjų duomenų koncepcijos ir raida, naudojimo rizikos, gavybos būdai ir taikomi modeliai. Taikomi šie metodai: mokslinių šaltinių sisteminė, loginė analizė, informacijos sugretinimas, sisteminimas.


Reikšminiai žodžiai: didieji duomenys, kliento pažinimas, didžiųjų duomenų analizė, naudojimo rizikos, duomenų tyryba, duomenų valdymas.

Keyword : Big data, customer cognition, big data analytics, risks of exploitation, big data mining, big data management

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Jul 5, 2018
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