Enterprise Data Architecture for Intelligent Business Operations
Keywords:
Enterprise Data Architecture, Intelligent Business Operations, Data Governance, Data Integration, Business Intelligence, Data Quality, Artificial Intelligence, Data Analytics, DataOps, MLOps, Operational Efficiency.Abstract
Enterprise Data Architecture (EDA) has become a necessity when making intelligent decisions within business enterprises today. The research aims to determine how the enterprise data architecture improves data integration, governance, analysis, automation, and decision-making in organisations. An inductive qualitative methodology has been employed using secondary data sources such as academic and industry journals and studies carried out in organisations before 2023. As observed from the results obtained, there was an improvement in data-driven decision-making adoption from 38% in 2018 to 50% in 2020 among the organisations surveyed. In banking, the highest adoption rate was 65%, while that for retail and industrial manufacturing was 43%. It is clear that there was a difference in the digital maturity level between the industries. As found out, organisations with mature enterprise data architecture have been able to improve productivity by 19%, efficiency by 16%, and reduce operational costs by 15% through automation and analytics. Customer responsiveness was improved through reduced complaints by 19% and churn by 22%. But still, there are obstacles in implementing such an approach as a result of the use of old systems, insufficient metadata, governance, and low organisational data maturity. Only 16% of the firms managed to become data masters, which means that many companies have failed to reach a high level of development in the sphere of enterprise data. The conclusion drawn from the research is that intelligent business requires effective data activation, governance, cloud-based infrastructure, DataOps, MLOps, and AI-based analytics.
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