BIG DATA ANALYTICS AND THE ADOPTION OF NEW PRACTICES IN ORGANIZATIONS
Lemmetty, Sonja (2017-08-15)
BIG DATA ANALYTICS AND THE ADOPTION OF NEW PRACTICES IN ORGANIZATIONS
Lemmetty, Sonja
(15.08.2017)
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Turun yliopisto. Turun kauppakorkeakoulu
Kuvaus
siirretty Doriasta
Tiivistelmä
The objective of this research is to study big data and big data analytics and to understand how new practices emerge in organizations. In addition this thesis focuses on explaining infrastructure relevant to big data analytics. The main research questions of this study were: (1) What is big data analytics? (2) What practices do companies use for analysing big data? (3) What kind of infrastructure is used for big data analytics? (4) How do companies adapt new practices for analysing big data?
This study follows a qualitative research approach. Empirical data is gathered through seven semi-structured interviews from high level professionals working in the area of big data analytics. The theoretical background is constructed based upon the research question. Academic articles, researches and books are used to study the theory of big data and big data analytics. The literature review explains the workflow used for analysing big data and introduces the reader to platforms and infrastructure relevant for big data analytics. Furthermore, this study introduces three theoretical frameworks that focus on the evolvement of new practices in organization. The first theoretical framework explains mimetic isomorphism. The second study focuses on the practice driven multilevel institutional change and the third emphasizes learning and reflection as a source of change in organization.
The findings of this study emphasize the definition of 4 V’s: volume, variety, velocity and veracity for defining big data. For the infrastructure of big data analytics, a hybrid model is proposed. This means that by integrating two systems there becomes an option to do data analytics on traditional structured as well as unstructured data. In addition to the definition of big data and big data infrastructure, this study explains practices related to big data analytics and suggests a model comprised of five stages for extracting insight from big data. The findings also showed a strong emphasis on the importance of having advanced skills to capture valuable information from large and complex data sets. Other suggestions drawn from the findings relate to the theme around practices in organizations. Based on the findings, new practices seem to evolve due to companies following relevant channels, being part of big data communities and participating in different seminars. Finally, the empirical findings revealed two new practises: “show and tell” and “code review”, which can help employees to learn new techniques and technologies that grow their skill set.
This study follows a qualitative research approach. Empirical data is gathered through seven semi-structured interviews from high level professionals working in the area of big data analytics. The theoretical background is constructed based upon the research question. Academic articles, researches and books are used to study the theory of big data and big data analytics. The literature review explains the workflow used for analysing big data and introduces the reader to platforms and infrastructure relevant for big data analytics. Furthermore, this study introduces three theoretical frameworks that focus on the evolvement of new practices in organization. The first theoretical framework explains mimetic isomorphism. The second study focuses on the practice driven multilevel institutional change and the third emphasizes learning and reflection as a source of change in organization.
The findings of this study emphasize the definition of 4 V’s: volume, variety, velocity and veracity for defining big data. For the infrastructure of big data analytics, a hybrid model is proposed. This means that by integrating two systems there becomes an option to do data analytics on traditional structured as well as unstructured data. In addition to the definition of big data and big data infrastructure, this study explains practices related to big data analytics and suggests a model comprised of five stages for extracting insight from big data. The findings also showed a strong emphasis on the importance of having advanced skills to capture valuable information from large and complex data sets. Other suggestions drawn from the findings relate to the theme around practices in organizations. Based on the findings, new practices seem to evolve due to companies following relevant channels, being part of big data communities and participating in different seminars. Finally, the empirical findings revealed two new practises: “show and tell” and “code review”, which can help employees to learn new techniques and technologies that grow their skill set.