Analytics can be approached from different perspectives. Usually analytics is seen as technological issue that includes data technologies, big data, computing algorithms and use of different software to compute data into different forms. Technical approach can be too complicated to most of people to understand even though analytics is seen nowadays more and more as an issue that concerns the whole organization and not only the technological people. This has evolved the need to bring analytics closer and more understandable to people that have not considered analytics as an important thing until now as analytics is beginning to have strategic significance in the business environment. This study acknowledges the need to understand analytics from different perspective and tries to find answers how this transformation can begin in organizations and what it takes to be analytic driven organization. The aim of the study is thus to understand how value is created with analytics in organizations and how the processes should be changed to become analytic driven organization. To understand these issues this thesis has two parts that together provide insights for the aim of the study. In the first part of the study a literature review is conducted to introduce relevant theories for the reader so that the matters that affect to the organization transformation regarding the topic can be understood as well as possible. The second part of the study is empirical study where series of semi-structured interviews were conducted for two different samples to gain more understanding of how the professionals of this topic see the approach and how different organizations perceive the change in their strategies and businesses. The results of the study challenge the traditional ways of developing analytics in organizations by presenting value based approach and implementation of analytics. Instead of starting to develop data capabilities as a priority organizations should start by finding a business problem that can be solved with analytics. After identifying it, a roadmap for the value chain of analytics should be developed, where the changes to capabilities and working ways are agreed. Implementing analytics is then an agile process where the whole value chain of analytics is being iterated from actions to decision to insight to modeling and data in a way that not only new technology is being added but the whole process is changed accordingly to support analytics and decision making.