Description
Big data analytics as source of competitive advantages is pivoting the automotive sector. Today’s original equipment manufacturers, however, are confronted by several barriers to the successful adoption of big data analytics. First, the cross-disciplinary nature of big data analytics requires (1) sufficient and skilled resources, (2) the collaboration of different business departments, supported by (3) appropriate organizational structures, (4) a data-driven culture, and (5) a defined business value, and (6) access to relevant data pools to achieve commitment and relevance within the organization. Drawing on the results of a revelatory case study as well as on the socio-technical systems theory I have identified six barriers to the adoption of big data analytics. From an academic perspective, these barriers contribute to the current body of knowledge of the adoption of big data analytics. For practice, they provide guidance for firms in the automotive sector as well as other traditionally goods-dominant industries which barriers need to be tackled to leverage the business value of big data analytics.
Recommended Citation
Dremel, Christian, "Barriers to the Adoption of Big Data Analytics in the Automotive Sector" (2017). AMCIS 2017 Proceedings. 11.
https://aisel.aisnet.org/amcis2017/AdoptionIT/Presentations/11
Barriers to the Adoption of Big Data Analytics in the Automotive Sector
Big data analytics as source of competitive advantages is pivoting the automotive sector. Today’s original equipment manufacturers, however, are confronted by several barriers to the successful adoption of big data analytics. First, the cross-disciplinary nature of big data analytics requires (1) sufficient and skilled resources, (2) the collaboration of different business departments, supported by (3) appropriate organizational structures, (4) a data-driven culture, and (5) a defined business value, and (6) access to relevant data pools to achieve commitment and relevance within the organization. Drawing on the results of a revelatory case study as well as on the socio-technical systems theory I have identified six barriers to the adoption of big data analytics. From an academic perspective, these barriers contribute to the current body of knowledge of the adoption of big data analytics. For practice, they provide guidance for firms in the automotive sector as well as other traditionally goods-dominant industries which barriers need to be tackled to leverage the business value of big data analytics.