This thesis investigates the relationship between technology, specifically machine learning and business value. By incorporating fundamentals of today such as a socio technical perspective and the fourth industrial revolution, commonly referred to as Industry 4.0 and this thesis will explain if machine learning can create business value in the organization as well as what organizational aspects a company needs to obtain in order to create business value from its data. 

This was achieved through a qualitative study by conducting semi-structured interviews with a technology industry leader, two experts on the subject and complemented with closely evaluated sources gathered from articles, journals, published literature, websites and case studies. 

The analysis of this thesis is based on an analytical framework that concludes five organizational aspects. It shows a clear relationship between the five organizational aspects the analysis is based on in regards to data and internal structure that needs close consideration in order to create business value from data as well as the possibilities of implementing and utilizing machine learning. By having a socio technical perspective in mind the analysis shows and confirms the theories this thesis is based on as well as it gives a nuanced perspective on an industry that primarily focuses on technical factors. 

The researchers conclude that apart from the five organizational aspects the analysis is based on, a sixth aspects needs to be incorporated as well. By conducting a study on a large technology industry leader as well as two additional experts on the subject, the researchers conclude that machine learning can benefit large parts of the organization as well as the workforce employed.