Data Management in the Offshore Oil and Gas Sector

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Introduction

The relevance of the topic of data management is growing every year. Indeed, the need to organize processes to improve the efficiency of collecting, processing, storing, and using data as a valuable asset is already evident to almost all companies. Much has been said about the benefits of having well-structured data management processes for companies, and many have already begun to implement this initiative. At the same time, organizations often make similar mistakes that negatively affect the speed of implementation and the effectiveness of the data management processes they create (Ayyavariah and Gopi, 2017). The purpose of this paper is to discuss these mistakes and organizational issues in the process of implementing data management in the offshore oil and gas sector.

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The Importance of Data Management

Sometimes an organization starts implementing data management processes without understanding the ultimate goals and measurable indicators of their achievement. There is also no assessment of the organization’s current level of maturity in terms of data management and a strategy for developing the function. External factors may dictate the initiative. They include the trend toward digital transformation, and the introduction of data management processes in competing companies (“they have it, we also need it”), while internal prerequisites are absent or opaque to the company’s management (Strengholt, 2020). Thus, these are the attempts to implement what the company is not ready for. Moreover, the company may not need it at the moment. Therefore, the future of these initiatives is not clear. To avoid this, it is necessary to set clear and measurable business goals. For example, to reduce by 50% the labor costs for checking financial and risk reports by unifying business terms and creating a centralized data hub (Panneerselvam, 2018). These goals need to be understood, validated by business units and company management, and reflected in the data management strategy.

Data Management Process

Notably, in no case should one try to immediately improve all data quality in an organization or describe in a business glossary all the terms used in the company. Concerning data management, the Pareto principle can be formulated as follows: “20% of all data critically affects 80% of the company’s business processes” (Narang, 2018). The correct approach, in this case, is the following sequence of steps. First, it is necessary to identify the most critical processes from the point of view of the oil and gas business, determine the data that significantly affect the effectiveness of these processes, and fix the initial assessment of the operations’ effectiveness. Then, step by step, increase the level of quality, data availability, and descriptions of their metadata (Gupta and Cannon, 2020). Upon completion of the work, assess how much it was possible to achieve the stated business goals and move on to activities for the next business process, thus scaling up the activities for systematic work with data.

Crucial Aspects of Data Management

There is a certain stereotype that implementing expensive IT systems is enough to bring order to the data. That is, if one implements an industrial data warehouse, Big Data, or data management tools, then the information will be safe. However, this is not always the case because data management includes other crucial aspects. For instance, if incorrect data is received at the entry point into the IT system, then the output will be useless, even if all the algorithms have worked correctly inside the IT system (Hoffmann, 2017). It negatively affects the whole set of processes and makes data owners repeat the same processes twice. Therefore, there are a few priority areas of work on data management.

People are the most critical resource. Employees must develop a culture of working with data and be aware that data is a valuable asset by which the company can generate additional profit or reduce costs. The second area of work is the processes governing the collection, processing, storage, and data use (Myers, 2019). They also include the work to improve the data quality and maintain a glossary of company business terms, architectural standards, and the institution of data ownership. Technologies are also a priority: business glossary, data flow management tools, integration tools, data model design, quality management software, master data management systems, and reference information management systems (Gils, 2020). Undoubtedly, technology is necessary, but it is still secondary to people and process organization.

Data Ownership

It happens that heads of an offshore organization consider data management to be a technology initiative. Data is stored in IT systems, which means that IT specialists should be responsible for it. It is also a common concept that data owners are assigned to departments that use the data in their processes, for example, to prepare financial statements. That is, the data is owned by those who are most interested in their quality (Gessert, Wingerath, and Ritter, 2020). This approach is possible, but still not effective enough. Data can only be owned by the one who creates it, the one in whose process the data is born, and the one who can directly influence the efficiency of the data production process. The owner’s tasks are to regulate the processes of entering data into IT systems, data quality control processes, both automated and manual, and initiate the necessary improvements to IT systems.

RAID and Cloud-Based Systems

There are various ways to store and process different types of information. RAID is a data virtualization technology that combines multiple disks into a logical unit to improve the work with it. Its performance is a significant plus since it uses several disks at once. However, it has low reliability, and it is quite difficult to find several suitable drives with the required characteristics (Lemahieu, Broucke, and Baesens, 2018). Cloud storage is a much more convenient data management tool in this regard. The information is stored on remote storage and does not take up space in the user’s system. This increases its reliability since any user software issues will not result in data loss (Kaoudi, Manolescu, and Zampetakis, 2020). However, there is a difficulty in this data management method: for example, the need for constant access to the Internet. It is impossible to obtain and use the information without it.

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Conclusion

There is an opinion that data management in the offshore oil and gas sector is the ultimate project, a one-off exercise in cleaning and inventory data. However, it is not: the world is rapidly changing, data disappears and appears, information systems migrate, and data requirements change (Ladley, 2019). Therefore, after implementing the data management framework, work on it should be continued daily as part of the company’s operational activities. Continuous work to improve the collection, processing, storage, improvement, and ​responsible attitude towards data is becoming an integral part of the company’s life and culture. Only then can one safely talk about the success of the implementation of the data management initiative.

Reference List

Ayyavariah, M. and Gopi, A. (2017) Database management system. Traverse City: Horizon Books.

Gessert, F., Wingerath, W. and Ritter, N. (2020) Fast and scalable cloud data management. Cham: Springer Nature.

Gils, B. (2020) Data management: A gentle introduction: Balancing theory and practice. Amersfoort: Van Haren.

Gupta, U. and Cannon, S. (2020) A practitioner guide to data governance: A case-cased approach. Bingley: Emerald Group Publishing.

Hoffmann, J. P. (2017) Principles of data management and presentation. Oakland: University of California Press.

Kaoudi, Z., Manolescu, I. and Zampetakis, S. (2020) Cloud-based RDF data management. San Rafael: Morgan & Claypool Publishers.

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Ladley, J. (2019) Data governance: How to design, deploy, and sustain an effective data governance program. London: Academic Press.

Lemahieu, W., Broucke, S. and Baesens, B. (2018). Principles of database management: The practical guide to storing, managing and analyzing big and small data. Cambridge: Cambridge University Press.

Myers, R. (2019) Data management and statistical analysis techniques. Waltham Abbey Essex: Scientific e-Resources.

Narang, R. (2018) Database management systems. New Delhi: PHI Learning Pvt. Ltd.

Panneerselvam, R. (2018) Database management systems. New Delhi: PHI Learning Pvt. Ltd.

Strengholt, P. (2020) Data management at scale. Sebastopol: O’Reilly Media, Inc.

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