Data is everywhere. Data is undeniable the foundation to the Oil and Gas 4.0 in the petroleum industry. However, data is not equal to value. The true business lies in extracting and maximizing value out of the available and required data. Data analytical thinking is the key to identify and define the problems, extract values, and develop solutions. This is also the key to transform to a data-driven organization.

Being data driven involves a combination of people, process and technology. The structure has to be built based on a solid foundation of data driven culture, which requires both technical and soft skills updates. Being data-driven is a teamwork requiring the collaboration between domain experts and data scientists to maximize their strengths. Identifying and defining problems by domain experts is crucial. The traditional working approaches will no longer be sufficient in a data-driven culture. Becoming data sensitive with data analytical thinking will be a necessity especially during the transformation of the traditional petroleum industry.

The paper illustrates the theoretical concepts of data analytical thinking and discusses the real cases exploiting the dark data and enhancing data collection. The data-driven organization is based on massive data. Fundamentally, the data flows through the process of data collection, quality assurance, data access, data security, and analytics. Every organization is collecting an amount of data much more than ever before. Unfortunately, the collected data could become dark data without proper analytics and utilization. In order to shed light on the importance of data analytical thinking, the paper utilizes the most frequently gathered data, including field production flow testing data and daily field production operational data. Real cases of utilizing eight offshore fields’ Business Plan data are also illustrated. Customized automation data analytical processes with significant boost of working efficiency are also shared to highlight the importance of integrating domain experts and data scientists.

A data-driven organization makes effective decisions based on the values extracted from massive data. The smooth collaboration between domain experts and data scientists is fundamental to minimize the communication cost. Data analytical thinking bridges the gap and smoothens the problem solving process. The collaborative and data-analytical-thinking culture lays the foundation to the data-driven organization.

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