The data-driving management in oil and gas industry: examples and opportunities

Background. Decision-making process in the oil and gas industry, traditionally extremely expensive, should be based on the point of maximizing the business value. Forecasting the effectiveness of investments of any business unit in oil and gas should be based on a data-driven management approach. The purpose of this article — to study methods and best practices of applying a data — driven approach to decision-making and analyze the possibility of scaling methods of best practices in the processes in oil and gas company.

Materials and methods. Research a various case with data-driven management shows that using data-driven approach allows solving several tasks at once:

to make a fast and quality decisions based on data that can always be checked, and the result can be analyzed;
to reduce the costs by eliminating inefficient steps and increase the flexibility of the process;
to form the correct attitude to data (data culture) and prepare for the implementation of the technologies of Industry 4.0.
Analyze cases revealed two common and important things: engineering of business processes from the key performance indicators and the technological development.

Results. In article discusses the topic of applying a data-driven decision-making approach in oil and gas companies using several examples of Gazprom Neft. These examples shows that better effect from the using of data-driven management is achieved by consistently modeling business processes for achieving maximum values; highlighting and fixing key business performance indicators and creating a digital monitoring of these indicators, which allows you to the achievement of goals.

Conclusions. In the conclusion of the article there are recommendation about using data-driven management approach for various processes of an oil and gas company.

data-driven management, hierarchy of metrics, North star metric, the technology management, oil and gas industry

1. Индустрия 4.0 в цифрах и фактах. [Электронный ресурс.] https://trends.rbc.ru/trends/industry/5e740c5b9a79470c22dd13e7

2. Technology vision 2021. Leaders wanted. [Электронный ресурс.] https://www.accenture.com/us-en/insights/technology/_acnmedia/Thought-Leadership-Assets/PDF-3/Accenture-Tech-Vision-2021-Full-Report.pdf

3. Канеман Д. Думай медленно … решай быстро. Часть II. Методы эвристики и искажения. М.: АСТ, 2014. — С. 124.

4. Андерсон К. Аналитическая культура. От сбора данных до бизнес-результатов. М.: Манн, Иванов и Фербер, 2017.

5. van Gasteren W. What is a “North Star Metric”? (+ 8 steps how you can discover your NSM). [Электронный ресурс.] https://growwithward.com/north-star-metric/

6. Серегина Е. Иерархия метрик VS Пирамида метрик. А также что такое North Star Metrics (метрики Всевластия) — учим матчасть. [Электронный ресурс.] https://medium.com/@elenest/иерархия-метрик-vs-пирамида-метрик-8eeda61fdefe

7. OSDU (Open Subsurface Data Universe). [Электронный ресурс.] https://osduforum.org

8. Databricks. [Электронный ресурс.] https://databricks.com