Abstract
The digital revolution and the communication platforms provided by the web 2.0 virtual space era, such as social media, social networks, other tools and channels, create new opportunities for better marketing decisions based on user-generated data analysis. Every day customers of social media and other virtual tools are creating huge amounts of their actions caused data, and business lack management tools for the support of this process, which could create knowledge in the area of customer profiles and preferences deeper cognition. Growing numbers of social media users indicate the popularity of these communication tools among the information society, but science today lacks a deeper knowledge of social media generated data and other algorithms for this data usage. Therefore, the purpose of the article is defined as the development of the conceptual model of big data generated by social media usage in business. The formation of the conceptual model is based on the analysis of big data assumptions and application possibilities, social media classification peculiarities and different channel specifics, identification of big data analysis methods and analysis of large data applications generated by social media. The conceptual model creates preconditions for deeper knowledge of user-generated big data in nowadays widely used communication platforms, as well as creation of the decision support tool for marketing specialists in order to use big data from social media in deeper customer profile and preferences cognition. Methods employed in this research are: literature and other references analysis, synthesis and logical analysis of information, comparison of information, systemization and visualization.
Highlights
Apibendrinant galima teigti, jog didieji duomenys – tai didelės įvairovės, sparčiai augantys duomenų kiekiai, kurie reikalauja pažangių saugojimo ir apdorojimo technologijų bei kuriuos analizuojant galima gauti vertingų įžvalgų
Conceptual framework of big data generated by social media use process)
Critical analysis of Big Data challenges and analytical methods
Summary
Interneto sparta bei išmaniųjų įrenginių paplitimas – visa tai lemia nuolatinį duomenų kiekių augimą. –– Sharma (2015) taip pat pabrėžia, jog tai duomenų rinkiniai, kurie yra tokie dideli ir komplikuoti, kad juos tampa sunku apdoroti naudojant tradicines duomenų apdorojimo programas ir įrankius. Apibendrinant galima teigti, jog didieji duomenys – tai didelės įvairovės, sparčiai augantys duomenų kiekiai, kurie reikalauja pažangių saugojimo ir apdorojimo technologijų bei kuriuos analizuojant galima gauti vertingų įžvalgų. Kad iš šių duomenų būtų išgaunama vertė, yra svarbu, jog šie duomenys tenkintų teisingumo, pagrįstumo ir matomumo sąlygas. The elements of big data characteristics models) (sudaryta autorių, remiantis Alharthi et al, 2017; Gandomi ir Haider, 2015; Khan et al, 2014; Rai, Meshram ir Gunasekaran, 2018; Russom ir Org, 2011; Sharma, 2015; Tanwar et al, 2015; Yin ir Kaynak, 2015) Figure 2. The elements of big data characteristics models (created by authors, based on Alharthi et al, 2017; Gandomi & Haider, 2015; Khan et al, 2014; Rai, Meshram, & Gunasekaran, 2018; Russom & Org, 2011; Sharma, 2015; Tanwar et al, 2015; Yin & Kaynak, 2015)
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