<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<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">emjume</journal-id><journal-title-group><journal-title xml:lang="ru">Экономика и управление</journal-title><trans-title-group xml:lang="en"><trans-title>Economics and Management</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1998-1627</issn><issn pub-type="epub">3033-7984</issn><publisher><publisher-name>СПбУТУиЭ</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.35854/1998-1627-2025-4-538-552</article-id><article-id custom-type="elpub" pub-id-type="custom">emjume-2490</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>НАУЧНЫЕ ИССЛЕДОВАНИЯ МОЛОДЫХ УЧЕНЫХ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>SCIENTIFIC RESEARCH OF YOUNG SCIENTISTS</subject></subj-group></article-categories><title-group><article-title>Качество данных в процессах материально-технического обеспечения и адаптация методологии CRISP-DM</article-title><trans-title-group xml:lang="en"><trans-title>Data quality in logistic and maintenance support processes and adaptation of the CRISP-DM methodology</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>Понкратов-Вайсман</surname><given-names>Б. Д.</given-names></name><name name-style="western" xml:lang="en"><surname>Ponkratov-Vaysman</surname><given-names>B. D.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Борис Денисович Понкратов-Вайсман, аспирант</p><p>119991, Москва, Ленинские горы, д. 1</p></bio><bio xml:lang="en"><p>Boris D. Ponkratov-Vaysman, postgraduate student </p><p>1 Leninskie Gory, Moscow 119991</p></bio><email xlink:type="simple">jukea1@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Московский государственный университет имени М.  В. Ломоносова<country>Россия</country></aff><aff xml:lang="en">Lomonosov Moscow State University<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>22</day><month>05</month><year>2025</year></pub-date><volume>31</volume><issue>4</issue><fpage>538</fpage><lpage>552</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Понкратов-Вайсман Б.Д., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Понкратов-Вайсман Б.Д.</copyright-holder><copyright-holder xml:lang="en">Ponkratov-Vaysman B.D.</copyright-holder><license 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://emjume.elpub.ru/jour/article/view/2490">https://emjume.elpub.ru/jour/article/view/2490</self-uri><abstract><sec><title>Цель</title><p>Цель. Модификация методологии межотраслевого процесса интеллектуального анализа данных CRISP-DM для области материально-технического обеспечения (далее — МТО) на основе концепции «данные как стратегический актив» в аспекте обеспечения проектов цифровой трансформации релевантными данными и повышенными показателями результативности.</p></sec><sec><title>Задачи</title><p>Задачи. Исследовать эволюцию концепции данных как стратегического актива и определить их значимость в условиях цифровой трансформации; оценить влияние качества данных на эффективность производственных процессов через призму функционирования информационных систем в области МТО; спроецировать и адаптировать этапы методологии CRISP-DM для применения в процессах МТО; разработать дополнительный профильный этап методологии CRISP-DM «Адаптация к управлению данными в области МТО» для ее применения в процессах МТО.</p></sec><sec><title>Методология</title><p>Методология. Исследование базируется на комплексном применении научных методов познания. Системный подход использован для целостного анализа процессов цифровой трансформации и места данных в них. Сравнительный анализ проведен при оценке практик управления данными. Метод моделирования обеспечил научное обоснование проецирования этапов и модификации методологии CRISP-DM для процессов МТО. Статистические методы применены при обработке и интерпретации количественных данных из отраслевых отчетов, а кейс-метод позволил извлечь практические выводы из опыта внедрений информационных систем и практик управления данными. Информационную базу настоящей статьи составили научные публикации российских ученых в области цифровой трансформации, аналитические отчеты и исследования международных консалтинговых компаний (McKinsey, Deloitte, Ernst &amp; Young, Gartner), материалы ведущих российских ИТ-компаний.</p></sec><sec><title>Результаты</title><p>Результаты. Выявлена эволюционная трансформация данных в стратегический актив предприятия, раскрыта их роль как ключевого фактора конкурентоспособности в условиях цифровой трансформации. Определены основные составляющие понятия «качество данных», а также показано влияние качества данных на информационные системы и связанные производственные процессы области МТО. Рассмотрен вопрос о применимости методологии СRISP-DM, а также предложена адаптированная и расширенная интерпретация этапов для процессов МТО. Модифицирована методология CRISP-DM в аспекте введения дополнительного этапа «Адаптация к управлению данными в области МТО», ориентированного на повышение устойчивости и адаптивности цепочек поставок на базе анализа данных.</p></sec></abstract><trans-abstract xml:lang="en"><sec><title>Aim</title><p>Aim. The work aimed to modify the methodology of the inter-industry process of CRISP-DM data mining for the field of logistics and maintenance support (hereinafter referred to as LMS) based on the concept of data as a strategic asset in terms of providing digital transformation projects with relevant data and improved performance indicators.</p></sec><sec><title>Objectives</title><p>Objectives. The work seeks to study the evolution of the concept of data as a strategic asset and determine their significance in the context of digital transformation; to assess the impact of data quality on the efficiency of production processes in terms of the functioning of information systems in the field of logistics and maintenance support; to project and adapt the stages of the CRISP-DM methodology for application in logistics and maintenance support processes; to develop an additional specialized stage of the CRISP-DM methodology “Adaptation to data management in the field of logistics and maintenance support” for its application in logistics and maintenance processes.</p></sec><sec><title>Methods</title><p>Methods. The study is based on the integrated application of scientific methods of cognition. A systems approach was used for a holistic analysis of digital transformation processes and the place of data in them. A comparative analysis was conducted when assessing data management practices. The modeling method provided a scientific justification for projecting the stages and modifying the CRISP-DM methodology for the logistics and maintenance processes. Statistical methods were used to process and interpret quantitative data from industry reports, and the case method was applied to draw practical conclusions from the experience of implementing the information systems and data management practices.</p><p>This article information base was compiled of scientific publications of Russian scientists in the field of digital transformation, as well as analytical reports and studies of international consulting companies (McKinsey, Deloitte, Ernst &amp; Young, Gartner), and materials from leading Russian IT companies.</p></sec><sec><title>Results</title><p>Results. The work revealed the data evolutionary transformation into a strategic asset of an enterprise, and identified its role as a key factor in competitiveness in the context of digital transformation. The main components of the concept of “data quality” were determined, and the impact of data quality on information systems and related production processes in the field of logistics and maintenance support was demonstrated. The applicability of the CRISP-DM methodology was considered, as well as an adapted and expanded interpretation of the stages for logistics and maintenance processes was proposed. The CRISP-DM methodology was modified in terms of introducing an additional stage “Adaptation to data management in the field of logistics and maintenance support”, aimed at increasing the sustainability and adaptability of supply chains based on data analysis.</p></sec><sec><title>Conclusions</title><p>Conclusions. Data is a strategic asset that affects significantly the efficiency of logistics and maintenance processes. The standard CRISP-DM methodology requires adaptation to take into account the specifics of logistics. The proposed modification of the methodology in the context of expanding the functionality of the current stages and introducing an additional stage “Adaptation to data management in the field of logistics and maintenance support” ensures the availability and processing of data relevant to the industry. This increases the value of data management projects in relation to business goals.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>качество данных</kwd><kwd>материально-техническое обеспечение</kwd><kwd>СRISP-DM</kwd><kwd>цифровая трансформация</kwd><kwd>данные как стратегический актив</kwd><kwd>интеллектуальный анализ данных</kwd><kwd>управление цепочками поставок</kwd></kwd-group><kwd-group xml:lang="en"><kwd>data quality</kwd><kwd>logistics and maintenance support</kwd><kwd>CRISP-DM</kwd><kwd>digital transformation</kwd><kwd>data as a strategic asset</kwd><kwd>intelligent data analysis</kwd><kwd>supply chain management</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">Paramita (Guha) Ghosh The evolution of data as an asset // DataVersity. December 9. 2020. URL: https://www.dataversity.net/the-evolution-of-data-as-an-asset/ (дата обращения: 19.11.2024).</mixed-citation><mixed-citation xml:lang="en">Paramita (Guha) Ghosh. The evolution of data as an asset. DataVersity. Dec. 09, 2020. URL: https://www.dataversity.net/the-evolution-of-data-as-an-asset/ (accessed on 19.11.2024).</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Xu T., Shi H., Shi Y., You J. From data to data asset: Conceptual evolution and strategic imperatives in the digital economy era // Asia Pacific Journal of Innovation and Entrepreneurship. 2024. Vol. 18. No. 1. P. 2–20. https://doi.org/10.1108/APJIE-10-2023-0195</mixed-citation><mixed-citation xml:lang="en">Xu T., Shi H., Shi Y., You J. From data to data asset: Conceptual evolution and strategic imperatives in the digital economy era. Asia Pacific Journal of Innovation and Entrepreneurship. 2024;18(1):2-20. https://doi.org/10.1108/APJIE-10-2023-0195</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Petzold B., Roggendorf M., Rowshankish K., Sporleder C. Designing data governance that delivers value // McKinsey Digital. June 26. 2020. URL: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/designing-data-governance-that-delivers-value (дата обращения: 19.11.2024).</mixed-citation><mixed-citation xml:lang="en">Petzold B., Roggendorf M., Rowshankish K., Sporleder C. Designing data governance that delivers value. McKinsey Digital. Jun. 26, 2020. URL: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/designing-data-governance-that-delivers-value (accessed on 19.11.2024).</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Data as a strategic asset // Deloitte. URL: https://www2.deloitte.com/us/en/pages/consulting/articles/data-strategic-asset.html (дата обращения: 19.11.2024).</mixed-citation><mixed-citation xml:lang="en">Data as a strategic asset. Deloitte. URL: https://www2.deloitte.com/us/en/pages/consulting/articles/data-strategic-asset.html (accessed on 19.11.2024).</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Лапидус Л. В. Что такое цифровая экономика и Индустрия 4.0? Принципы трансформации и перспективы для бизнеса // Перспективы развития электронного бизнеса и электронной коммерции: материалы IV межфакультетской науч.-практ. конф. молодых ученых (Москва, 13 декабря 2017 г.) / под ред. Л. В. Лапидус. М.: Экономический факультет МГУ имени М. В. Ломоносова, 2018. С. 4–15.</mixed-citation><mixed-citation xml:lang="en">Lapidus L.V. What is the digital economy and Industry 4.0? Principles of transformation and prospects for business. In: Prospects for the development of electronic business and electronic commerce. Proc. 4th Interfaculty sci.-pract. conf. of young scientists (Moscow, December 13, 2017). Moscow: Faculty of Economics, Lomonosov Moscow State University; 2018:4-15. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Цифровые технологии в логистике и управлении цепями поставок: аналитический обзор / под общ. и науч. ред. В. И. Сергеева. М.: ИД Высшей школы экономики, 2020. 192 с.</mixed-citation><mixed-citation xml:lang="en">Sergeev V.I., ed. Digital technologies in logistics and supply chain management: An analytical review. Moscow: HSE Publ.; 2020. 192 p. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Reinsel D., Gantz J., Rydning J. The digitization of the world: From edge to core. An IDC White Paper-#US44413318 // Framingham, MA: International Data Corporation (IDC), 2018. 28 p. URL: https://www.seagate.com/files/www-content/our-story/trends/files/idcseagate-dataage-whitepaper.pdf (дата обращения: 25.01.2024).</mixed-citation><mixed-citation xml:lang="en">Reinsel D., Gantz J., Rydning J. The digitization of the world: From edge to core. An IDC White Paper-#US44413318. Framingham, MA: International Data Corporation (IDC); 2018. 28 p. URL: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagatedataage-whitepaper.pdf (accessed on 25.01.2024).</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Лапидус Л. В. Синергетические эффекты как результат реализации Data Strategy и стратегия цифровой трансформации // Экономика железных дорог. 2022. № 11. С. 26–39.</mixed-citation><mixed-citation xml:lang="en">Lapidus L.V. Synergetic effects as a result of implementing data management and digital transformation strategies. Ekonomika zheleznykh dorog = Railway Economy. 2022;(11): 26-39. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Трофимов В. В., Трофимова Л. А. О концепции управления на основе данных в условиях цифровой трансформации // Петербургский экономический журнал. 2021. № 4. С. 149–155. https://doi.org/10.24412/2307-5368-2021-4-149-155</mixed-citation><mixed-citation xml:lang="en">Trofimov V.V., Trofimova L.A. On the concept of data-driven management under the conditions of digital transformation. Peterburgskii ekonomicheskii zhurnal = Saint-Petersburg Economic Journal. 2021;(4):149-155. (In Russ.). https://doi.org/10.24412/2307-5368-2021-4-149-155</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Аналитика в тренде: Data-driven-подход и современные методы решения бизнес-задач // Forbes. 2023. 12 декабря. URL: https://www.forbes.ru/spetsproekt/501823-analitika-v-trendedata-driven-podhod-i-sovremennye-metody-resenia-biznes-zadac?erid=4CQwVszH9pWuokYkYu7 (дата обращения: 01.12.2024).</mixed-citation><mixed-citation xml:lang="en">Analytics in trend: Data-driven approach and modern methods of solving business problems. Forbes. Dec. 12, 2023. URL: https://www.forbes.ru/spetsproekt/501823-analitika-v-trendedata-driven-podhod-i-sovremennye-metody-resenia-biznes-zadac?erid=4CQwVszH9pWuokYkYu7 (accessed on 01.12.2024). (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Data quality is the state of the data, reflected in its accuracy, completeness, reliability, relevance, and timelines // Starburst. URL: https://www.starburst.io/data-glossary/ (дата обращения: 01.12.2024).</mixed-citation><mixed-citation xml:lang="en">Data quality is the state of the data, reflected in its accuracy, completeness, reliability, relevance, and timelines. Starburst. URL: https://www.starburst.io/data-glossary/ (accessed on 01.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Data quality: The foundation of effective business analytics // IABAC. October 12. 2023. URL: https://iabac.org/blog/data-quality-the-foundation-of-effective-business-analytics (дата обращения: 12.12.2024).</mixed-citation><mixed-citation xml:lang="en">Data quality: The foundation of effective business analytics. IABAC. Oct. 12, 2023. URL: https://iabac.org/blog/data-quality-the-foundation-of-effective-business-analytics (accessed on 12.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Скворцов Н. DAMA-DMBOK2: трудности перевода // Открытые системы. 2020. 9 апреля. URL: https://www.osp.ru/os/2020/02/13055423 (дата обращения: 12.12.2024).</mixed-citation><mixed-citation xml:lang="en">Skvortsov N. DAMA-DMBOK2: Lost in translation. Otkrytye sistemy. Apr. 09, 2020. URL: https://www.osp.ru/os/2020/02/13055423 (accessed on 12.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Лапидус Л. В. Системные эффекты от имплементации Data Strategy в стратегию цифровой трансформации на транспорте // Экономика железных дорог. 2022. № 8. С. 17–29.</mixed-citation><mixed-citation xml:lang="en">Lapidus L.V. Systemic effects of the implementation of data strategy in the strategy of digital transformation in transport. Ekonomika zheleznykh dorog = Railway Economy. 2022;(8):17-29. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">10 Examples of how Big Data in logistics can transform the supply chain // Datapine. URL: https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain/ (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">10 Examples of how Big Data in logistics can transform the supply chain. Datapine. URL: https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain/ (accessed on 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Kendall G. Why UPS drivers don’t turn left and you probably shouldn’t either // The Conversation. January 20. 2017. URL: https://theconversation.com/why-ups-driversdont-turn-left-and-you-probably-shouldnt-either-71432 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Kendall G. Why UPS drivers don’t turn left and you probably shouldn’t either. The Conversation. Jan. 20, 2017. URL: https://theconversation.com/why-ups-drivers-dont-turn-left-and-youprobably-shouldnt-either-71432 (accessed on 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">UPS extends use of Google Cloud data analytics technology // UPS Stories. March 25. 2022. URL: https://about.ups.com/us/en/our-stories/innovation-driven/ups-and-google-cloud.html (дата обращения: 21.12.2024).</mixed-citation><mixed-citation xml:lang="en">UPS extends use of Google Cloud data analytics technology. UPS Stories. Mar. 25, 2022. URL: https://about.ups.com/us/en/our-stories/innovation-driven/ups-and-google-cloud. html (accessed on 21.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Davenport T. H., Bean R. Action and inaction on data, analytics, and AI // MIT Sloan Management Review. January 19. 2023. URL: https://sloanreview.mit.edu/article/actionand-inaction-on-data-analytics-and-ai/#article-authors (дата обращения: 21.12.2024).</mixed-citation><mixed-citation xml:lang="en">Davenport T.H., Bean R. Action and inaction on data, analytics, and AI. MIT Sloan Management Review. Jan. 19, 2023. URL: https://sloanreview.mit.edu/article/action-andinaction-on-data-analytics-and-ai/#article-authors (accessed on 21.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Moore S. Gartner top 10 data and analytics trends for 2019 // Gartner. November 5. 2019. URL: https://www.gartner.com/smarterwithgartner/gartner-top-10-data-analytics-trends (дата обращения: 25.12.2024).</mixed-citation><mixed-citation xml:lang="en">Moore S. Gartner top 10 data and analytics trends for 2019. Gartner. Nov. 05, 2019. URL: https://www.gartner.com/smarterwithgartner/gartner-top-10-data-analytics-trends (accessed on 25.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Decoding key trends from Gartner’s hype cycle for data management — 2021 report // Indium. February 21. 2022. URL: https://www.indiumsoftware.com/blog/decoding-keytrends-from-gartners-hype-cycle-for-data-management/ (дата обращения: 25.12.2024).</mixed-citation><mixed-citation xml:lang="en">Decoding key trends from Gartner’s hype cycle for data management — 2021 report. Indium. Feb. 21, 2022. URL: https://www.indiumsoftware.com/blog/decoding-key-trends-fromgartners-hype-cycle-for-data-management/ (accessed on 25.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Pallotta C. 5 insights from Gartner’s hype cycle for data management: 2022 report // СhaosSearch. September 1. 2022. URL: https://www.chaossearch.io/blog/data-managementhype-cycle-report-gartner (дата обращения: 12.01.2025).</mixed-citation><mixed-citation xml:lang="en">Pallotta C. 5 Insights from Gartner’s hype cycle for data management: 2022 report. СhaosSearch. Sep. 01, 2022. URL: https://www.chaossearch.io/blog/data-management-hypecycle-report-gartner (accessed on 12.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Discover the future of data management: 2023 Gartner hype cycle for data management // Denodo. URL: https://www.denodo.com/en/document/analyst-report/gartner-hype-cycledata-management-2023 (дата обращения: 12.01.2025).</mixed-citation><mixed-citation xml:lang="en">Discover the future of data management: 2023 Gartner hype cycle for data management. Denodo. URL: https://www.denodo.com/en/document/analyst-report/gartner-hype-cycledata-management-2023 (accessed on 12.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Gezgin E., Huang X., Samal P., Silva I. Digital transformation: Raising supply-chain performance to new levels // McKinsey. November 17. 2017. URL: https://www.mckinsey.com/capabilities/operations/our-insights/digital-transformation-raising-supply-chain-performance-to-new-levels (дата обращения: 12.01.2025).</mixed-citation><mixed-citation xml:lang="en">Gezgin E., Huang X., Samal P., Silva I. Digital transformation: Raising supply-chain performance to new levels. McKinsey. Nov. 17, 2017. URL: https://www.mckinsey.com/capabilities/operations/our-insights/digital-transformation-raising-supply-chain-performance-to-new-levels (accessed on 12.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Redesigning master data management framework to save up to $98 million in procurement spend // Genpact. URL: https://www.genpact.com/insight/harnessing-master-data-managementto-save-up-to-98m-by-improving-spend-visibility-and-compliance (дата обращения: 12.01.2025).</mixed-citation><mixed-citation xml:lang="en">Redesigning master data management framework to save up to $98 million in procurement spend. Genpact. URL: https://www.genpact.com/insight/harnessing-master-data-managementto-save-up-to-98m-by-improving-spend-visibility-and-compliance (accessed on 12.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Что такое data mining и для чего он применяется? // Kaspersky. URL: https://www.kaspersky.ru/resource-center/definitions/data-mining (дата обращения: 16.01.2025).</mixed-citation><mixed-citation xml:lang="en">What is data mining and what is it used for? Kaspersky. URL: https://www.kaspersky.ru/resource-center/definitions/data-mining (accessed on 16.01.2025). (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Data mining concepts // Microsoft Build. October 31. 2023. URL: https://learn.microsoft.com/en-us/analysis-services/data-mining/data-mining-concepts?view=asallproducts-allversions (дата обращения: 16.01.2025).</mixed-citation><mixed-citation xml:lang="en">Data mining concepts. Microsoft Build. Oct. 31, 2023. URL: https://learn.microsoft.com/en-us/analysis-services/data-mining/data-mining-concepts?view=asallproducts-allversions (accessed on 16.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">What is data mining? // RightData. URL: https://medium.com/rightdata/what-is-datamining-70978aedf079 (дата обращения: 21.01.2025).</mixed-citation><mixed-citation xml:lang="en">What is data mining? RightData. URL: https://medium.com/rightdata/what-is-data-mining70978aedf079 (accessed on 21.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">How can data mining enhance supply chain management? // Quantzig. February 10. 2025. URL: https://www.quantzig.com/blog/data-mining-supply-chain-management/ (дата обращения: 21.01.2025).</mixed-citation><mixed-citation xml:lang="en">How can data mining enhance supply chain management? Quantzig. Feb. 10, 2025. URL: https://www.quantzig.com/blog/data-mining-supply-chain-management/ (accessed on 21.01.2025).</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Data Mining — интеллектуальный или глубинный анализ данных // Web-Creator. URL: https://web-creator.ru/articles/data-mining (дата обращения: 28.01.2025).</mixed-citation><mixed-citation xml:lang="en">Data Mining — intellectual or deep data analysis. Web-Creator. URL: https://web-creator.ru/articles/data-mining (accessed on 28.01.2025). (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">CRISP-DM is still the most popular framework for executing data science projects // Data Science PM. November 18. 2024. URL: https://www.datascience-pm.com/crisp-dm-stillmost-popular/ (дата обращения: 10.02.2025).</mixed-citation><mixed-citation xml:lang="en">CRISP-DM is still the most popular framework for executing data science projects. Data Science PM. Nov. 18, 2024. URL: https://www.datascience-pm.com/crisp-dm-still-mostpopular/ (accessed on 10.02.2025).</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">CRISP-DM. Часть 1. Как использование CRISP-DM может улучшить ваши проекты по машинному обучению? // SCMAX. URL: https://scmax.ru/articles/44259/ (дата обращения: 10.02.2025).</mixed-citation><mixed-citation xml:lang="en">CRISP-DM. Part 1. How can using CRISP-DM improve your machine learning projects? SCMAX. URL: https://scmax.ru/articles/44259/ (accessed on 10.02.2025). (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">What is CRISP DM? // Data Science PM. December 9. 2024. URL: https://www.datasciencepm.com/crisp-dm-2/ (дата обращения: 24.02.2025).</mixed-citation><mixed-citation xml:lang="en">What is CRISP DM? Data Science PM. Dec. 09, 2024. URL: https://www.datascience-pm.com/crisp-dm-2/ (accessed on 24.02.2025).</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">CRISP-DM: проверенная методология для Data-Scientist-ов // Хабр. May 17. 2017. URL: https://habr.com/ru/companies/lanit/articles/328858/ (дата обращения: 24.02.2025).</mixed-citation><mixed-citation xml:lang="en">CRISP-DM: A proven methodology for Data Scientists. Habr. May 17, 2017. URL: https://habr.com/ru/companies/lanit/articles/328858/ (accessed on 24.02.2025).</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">CRISP-DM // Machine Learning.ru. URL: http://www.machinelearning.ru/wiki/index.php?title=Crisp-dm (дата обращения: 24.02.2025).</mixed-citation><mixed-citation xml:lang="en">CRISP-DM. Machine Learning.ru. URL: http://www.machinelearning.ru/wiki/index.php?title=Crisp-dm (accessed on 24.02.2025). (In Russ.).</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
