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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">proneft</journal-id><journal-title-group><journal-title xml:lang="ru">PROНЕФТЬ. Профессионально о нефти</journal-title><trans-title-group xml:lang="en"><trans-title>PROneft. Professionally about Oil</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2587-7399</issn><issn pub-type="epub">2588-0055</issn><publisher><publisher-name>«Газпром нефть»</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.51890/2587-7399-2025-10-1-98-107</article-id><article-id custom-type="elpub" pub-id-type="custom">proneft-547</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЭКОНОМИКА, УПРАВЛЕНИЕ, ПРАВО</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ECONOMICS, MANAGEMENT, LAW</subject></subj-group></article-categories><title-group><article-title>Практика построения корпоративного хранилища данных  в нефтегазовом секторе</article-title><trans-title-group xml:lang="en"><trans-title>Practice of building a corporate data warehouse in the oil and gas sector</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Шеховцова</surname><given-names>И. Р.</given-names></name><name name-style="western" xml:lang="en"><surname>Shekhovtsova</surname><given-names>I. R.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ирина Романовна Шеховцова — ведущий специалист отдела автоматизированных систем управления </p><p>197022, г. Санкт-Петербург, наб. реки Большой Невки, д. 24, стр. 1</p></bio><bio xml:lang="en"><p>Irina R. Shekhovtsova — Lead Specialist of Department of Automated Control Systems</p><p>Building 1, 24 Bolshaia Nevka Embankment, 197022</p></bio><email xlink:type="simple">I.Shekhovtsova@gazprom-international.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Международная компания ООО «Газпром Интернэшнл Лимитед»</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Gazprom International Limited ILLC</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>07</day><month>04</month><year>2025</year></pub-date><volume>10</volume><issue>1</issue><fpage>98</fpage><lpage>107</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Шеховцова И.Р., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Шеховцова И.Р.</copyright-holder><copyright-holder xml:lang="en">Shekhovtsova I.R.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://proneft.elpub.ru/jour/article/view/547">https://proneft.elpub.ru/jour/article/view/547</self-uri><abstract><sec><title>Введение</title><p>Введение. Эффективное принятие управленческих решений во многом зависит от наличия оперативного доступа к актуальной информации о состоянии активов. Данные о таких бизнес-структурах, как правило, фрагментированы и взаимозависимы, что требует системного подхода к их консолидации и интерпретации.</p></sec><sec><title>Цель</title><p>Цель. В статье рассматривается опыт построения корпоративного хранилища данных на основе разрозненных данных операционных систем-источников для возможности комплексного анализа состояния активов.</p></sec><sec><title>Материалы и методы</title><p>Материалы и методы. Для достижения цели определен перечень ключевых показателей компании на разных уровнях управления, проанализированы достоинства и недостатки архитектурных подходов Уильяма Инмона и Ральфа Кимбола к построению корпоративного хранилища данных, исследованы трудности по устранению несоответствий в моделях систем-источников и обеспечению заданного уровня информационной безопасности в компонентах архитектуры.</p></sec><sec><title>Результаты</title><p>Результаты. Приведен реальный пример построения гибридной архитектуры корпоративного хранилища данных на основе подходов Уильяма Инмона и Ральфа Кимбола, настроены ETL-процессы по загрузке в хранилище и насыщению витрин данных, разработан модуль загрузки отсутствующих в системах данных, проведены мониторинг и анализ рисков архитектуры хранилища.</p></sec><sec><title>Заключение</title><p>Заключение. Построение корпоративного хранилища данных действительно сопряжено со множеством вызовов в области проработки архитектуры и настройки ETL-процессов. Реальные примеры интеграции, в том числе приведенный в настоящей статье, служат ценным ориентиром для понимания реализованных стратегий, показывают практическое применение интеграционных фреймворков, технологий и методологий, а также предлагают информацию о приобретенных уроках и ключевых факторах, способствовавших успешным проектам интеграции.</p></sec></abstract><trans-abstract xml:lang="en"><sec><title>Introduction</title><p>Introduction. Effective managerial decision-making largely depends on having timely access to current information about asset status. Data on such business structures are typically fragmented and interdependent, necessitating a systematic approach to their consolidation and interpretation.</p></sec><sec><title>Aim</title><p>Aim. The article examines the experience of building a corporate data warehouse based on disparate data from operational source systems to enable comprehensive analysis of asset status.</p></sec><sec><title>Materials and methods</title><p>Materials and methods. To achieve the goal, a list of key company performance indicators at diff erent management levels was determined, the advantages and disadvantages of the architectural approaches of William Inmon and Ralph Kimball to building a corporate data warehouse were analyzed, and the difficulties in eliminating inconsistencies in source system models and ensuring the required level of information security in architecture components were investigated..</p></sec><sec><title>Results</title><p>Results. A real example of constructing a hybrid architecture for a corporate data warehouse based on the approaches of William Inmon and Ralph Kimball is provided. ETL processes for loading data into the warehouse and populating data marts have been configured. A module for loading data absent in the systems has been developed. Monitoring and risk analysis of the constructed architecture have been conducted.</p></sec><sec><title>Conclusion</title><p>Conclusion. Building a corporate data warehouse indeed presents numerous challenges in architecture development and ETL process confi guration. Real-world integration examples, including the one provided in this article, serve as valuable benchmarks for understanding implemented strategies. They demonstrate the practical application of integration frameworks, technologies, and methodologies, and offer insights into lessons learned and key factors that contributed to successful integration projects. </p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>корпоративное хранилище данных</kwd><kwd>интеграция данных</kwd><kwd>витрины данных</kwd><kwd>ETL</kwd><kwd>дашборд</kwd><kwd>риск</kwd></kwd-group><kwd-group xml:lang="en"><kwd>corporate data warehouse</kwd><kwd>data integration</kwd><kwd>data marts</kwd><kwd>ETL</kwd><kwd>dashboard</kwd><kwd>risk</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Devlin B. Thirty Years of Data Warehousing // Business Intelligence Journal. — 2018. — Vol. 23. — №1. — P. 12–24.</mixed-citation><mixed-citation xml:lang="en">Devlin B. Thirty Years of Data Warehousing. 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