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Strong Leaders, Strong Results: Our Path to Data Analysis, Literacy, and Governance
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Data-as-a-Service
Applicable Industries
- Oil & Gas
Applicable Functions
- Discrete Manufacturing
- Business Operation
Use Cases
- Predictive Maintenance
- Manufacturing System Automation
Services
- Data Science Services
- System Integration
The Challenge
Gazprom Neft, one of the top three oil-producing companies in Russia, was facing challenges in making data-based decisions due to the lack of a robust Business Intelligence (BI) unit. The company was already a year into their journey with Qlik, a data analytics platform, when the BI Department Head joined in 2013. The task was to further develop the internal Qlik-based processes. However, the allocation of resources was a major challenge, especially server resources. The company was also not always on schedule in the purchasing of new equipment. Change management was another challenge, particularly overcoming internal resistance without clear signaling from company leadership.
About The Customer
Gazprom Neft Group, celebrating its 25th anniversary in 2020, is one of the top three oil-producing companies in Russia. The company is involved in every stage of oil-based products, from hydrocarbon exploration and the production of high-quality oil and gas to the retail sale of a wide variety of petroleum products. Gazprom Neft has operations in many areas of the world, including South America and Africa. With more than 70 subsidiaries related to production, refining, and direct sales, it is one of the largest energy operations in the world. In 2019, the company's net profit was over $5 billion USD and it currently employs over 50,000 people.
The Solution
Despite the challenges, the company had full support from top management, especially the CFO, who was interested in the potential benefits of a robust BI unit. This support led to a revolution of sorts, leading to a transformation into a data-driven culture. The company took its time to develop expertise and structures for data analysis, data literacy, and data governance. As the company's data analysis competence grew, so did the team. By the end of 2014, the BI office had one developer and two analysts, all of whom were required to know structured query language (SQL). A year later, mathematical statistics and languages like Python were added to the required skills. As the functionality developed, so too did the various categories of staff expertise. The company also worked closely with a number of universities, offering internships to students from a variety of backgrounds, many of whom eventually joined the organization as full-time employees.
Operational Impact
Quantitative Benefit
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