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 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">Russian Journal of Management</journal-id>
   <journal-title-group>
    <journal-title xml:lang="en">Russian Journal of Management</journal-title>
    <trans-title-group xml:lang="ru">
     <trans-title>Russian Journal of Management</trans-title>
    </trans-title-group>
   </journal-title-group>
   <issn publication-format="print">2409-6024</issn>
   <issn publication-format="online">2500-1469</issn>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="publisher-id">105479</article-id>
   <article-id pub-id-type="doi">10.29039/2500-1469-2025-13-10-110-118</article-id>
   <article-categories>
    <subj-group subj-group-type="toc-heading" xml:lang="ru">
     <subject>Финансовый менеджмент</subject>
    </subj-group>
    <subj-group subj-group-type="toc-heading" xml:lang="en">
     <subject>Financial management</subject>
    </subj-group>
    <subj-group>
     <subject>Финансовый менеджмент</subject>
    </subj-group>
   </article-categories>
   <title-group>
    <article-title xml:lang="en">DATA QUALITY ISSUES AND THEIR IMPACT ON BUSINESS ANALYTICS RESULTS</article-title>
    <trans-title-group xml:lang="ru">
     <trans-title>ПРОБЛЕМЫ КАЧЕСТВА ДАННЫХ И ИХ ВЛИЯНИЕ НА РЕЗУЛЬТАТЫ БИЗНЕС-АНАЛИТИКИ</trans-title>
    </trans-title-group>
   </title-group>
   <contrib-group content-type="authors">
    <contrib contrib-type="author">
     <name-alternatives>
      <name xml:lang="ru">
       <surname>Петрова</surname>
       <given-names>Ольга Александровна</given-names>
      </name>
      <name xml:lang="en">
       <surname>Petrova</surname>
       <given-names>Ol'ga Aleksandrovna</given-names>
      </name>
     </name-alternatives>
     <email>OAPetrova@fa.ru</email>
     <bio xml:lang="ru">
      <p>кандидат экономических наук;</p>
     </bio>
     <bio xml:lang="en">
      <p>candidate of economic sciences;</p>
     </bio>
     <xref ref-type="aff" rid="aff-1"/>
    </contrib>
   </contrib-group>
   <aff-alternatives id="aff-1">
    <aff>
     <institution xml:lang="ru">Финансовый Университет при Правительстве Российской Федерации</institution>
     <city>Москва</city>
     <country>Россия</country>
    </aff>
    <aff>
     <institution xml:lang="en">Financial University under the Government of the Russian Federation</institution>
     <city>Moscow</city>
     <country>Russian Federation</country>
    </aff>
   </aff-alternatives>
   <pub-date publication-format="print" date-type="pub" iso-8601-date="2025-11-15T13:34:49+03:00">
    <day>15</day>
    <month>11</month>
    <year>2025</year>
   </pub-date>
   <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-11-15T13:34:49+03:00">
    <day>15</day>
    <month>11</month>
    <year>2025</year>
   </pub-date>
   <volume>13</volume>
   <issue>10</issue>
   <fpage>110</fpage>
   <lpage>118</lpage>
   <history>
    <date date-type="received" iso-8601-date="2025-10-16T00:00:00+03:00">
     <day>16</day>
     <month>10</month>
     <year>2025</year>
    </date>
   </history>
   <self-uri xlink:href="https://riorpub.com/en/nauka/article/105479/view">https://riorpub.com/en/nauka/article/105479/view</self-uri>
   <abstract xml:lang="ru">
    <p>В данной статье исследуется теоретическое осмысление качества данных в контексте бизнес-аналитики, основанное на междисциплинарном подходе, интегрирующем управление данными, информационную экономику и системный анализ. Качество данных определяется их пригодностью для конкретных бизнес-процессов через критерии точности, полноты, непротиворечивости, актуальности, достоверности и согласованности. Анализируются проблемы качества, включая технические, организационные и процессуальные аспекты, а также предлагаются комплексные меры, такие как внедрение фреймворков управления данными, использование машинного обучения для валидации и трансформация корпоративной культуры, подчеркивая необходимость синергии между технологиями, процессами и человеческим фактором для минимизации негативного воздействия на бизнес-результаты.</p>
   </abstract>
   <trans-abstract xml:lang="en">
    <p>This article explores the theoretical understanding of data quality in the context of business analytics, based on an interdisciplinary approach integrating data management, information economics, and systems analysis. Data quality is defined by its suitability for specific business processes through criteria of accuracy, completeness, consistency, currency, reliability, and coherence. Quality issues are analyzed, including technical, organizational, and procedural aspects, and comprehensive measures are proposed, such as the implementation of data management frameworks, the use of machine learning for validation, and corporate cultural transformation, emphasizing the need for synergy between technology, processes, and the human factor to minimize the negative impact on business results.</p>
   </trans-abstract>
   <kwd-group xml:lang="ru">
    <kwd>качество данных</kwd>
    <kwd>бизнес-аналитика</kwd>
    <kwd>управление данными</kwd>
    <kwd>DAMA-DMBOK</kwd>
    <kwd>ISO 8000</kwd>
    <kwd>машинное обучение</kwd>
    <kwd>корпоративная культура</kwd>
    <kwd>метрики качества</kwd>
   </kwd-group>
   <kwd-group xml:lang="en">
    <kwd>Data quality</kwd>
    <kwd>business analytics</kwd>
    <kwd>data management</kwd>
    <kwd>DAMA-DMBOK</kwd>
    <kwd>ISO 8000</kwd>
    <kwd>machine learning</kwd>
    <kwd>corporate culture</kwd>
    <kwd>quality metrics</kwd>
   </kwd-group>
  </article-meta>
 </front>
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