Batch Processing, Real-Time Integration, and Data Virtualization
Data integration in business intelligence refers to combining data from different sources or systems within an organization to provide a unified view for analysis and decision-making purposes. This integration typically involves merging data from disparate databases, applications, and platforms into a coherent data repository or warehouse. Data consolidation combines data from various sources, such as transactional databases, ERP systems, CRM platforms, spreadsheets, and external sources like social media or market research data. Data transformation involves converting and standardizing data formats, structures, and semantics to ensure consistency and compatibility across different sources. It may also include data cleansing, normalization, and enrichment to improve data quality. Data synchronization ensures that data across different systems is kept up-to-date and synchronized in real-time or at regular intervals to provide users with timely and accurate information. Data governance involves implementing policies, processes, and controls throughout the integration process to ensure data quality, security, and compliance with regulatory requirements. Data modeling involves designing a logical and efficient data model that represents the integrated data in a way that supports meaningful analysis and reporting. This may involve creating dimensional models for data warehouses or data marts. By combining data from disparate sources and providing a unified view of information, data integration in business intelligence enables organizations to gain valuable insights, make informed decisions, and improve overall operational efficiency and effectiveness.
Source: William McKnight, https://www.youtube.com/watch?v=W3S4y2qAPQs
This work is licensed under a Creative Commons Attribution 3.0 License.