In today’s competitive business landscape, organizations are increasingly relying on Business Intelligence (BI) systems to optimize resource utilization and gain a competitive edge. In this article, we will explore the concepts of Business Intelligence and how it differs from an organization’s Online Transaction Processing (OLTP) systems. We will also delve into the architecture of a typical BI system based on the Microsoft SQL Server and Business Intelligence Platform.
OLAP vs OLTP
OLTP systems are designed to track and record transactions in real-time, automating the process of data entry and retrieval for functional areas. On the other hand, OLAP systems are core to Business Intelligence and allow users to analyze vast amounts of summarized information in multidimensional views and hierarchies. OLAP tools enable business users to extract and view data from different dimensions, such as time, geography, gender, and product, to perform trend analysis and answer complex business questions.
While some of these questions can be answered using OLTP databases, it is not recommended due to the impact on performance. OLTP systems are optimized for transaction processing, whereas OLAP systems are designed for analytical processing on large datasets. The BI platform pre-calculates or summarizes aggregates beforehand, allowing for faster query results and interactive exploration of aggregated data.
Business Intelligence System Architecture
When designing a Business Intelligence System, several components need to be planned, designed, and developed. Let’s take a look at the key components:
Data Integration or ETL Pipeline
An organization may have multiple applications catering to different functions. To create a comprehensive view of organizational data, a Data Warehouse is required. Data Integration or Extract, Transform, and Load (ETL) processes are used to acquire data from various source systems and integrate it into the Data Warehouse. SQL Server Integration Services (SSIS) is a powerful tool for data integration, providing capabilities for data cleansing, profiling, and management.
Analysis
Once the Data Warehouse is created, an OLAP multi-dimensional structure needs to be built. SQL Server Analysis Services (SSAS) is a leading OLAP tool that pre-calculates, summarizes, and stores data in a highly compressed form. SSAS enables interactive exploration of aggregated data and supports data mining to identify patterns and trends.
Information Delivery
After creating the SSAS cubes, different reporting tools can be used to analyze the data. SQL Server Reporting Services (SSRS) allows the creation of formatted and interactive reports, including tabular reports, charts, graphs, maps, and KPI-based scorecards. PowerPivot, Power View, Excel services, and SSRS provide users with the ability to define and execute ad-hoc reports from a standard data model.
Collaboration and Hosting Platform
SharePoint can be used as a collaboration, hosting, and sharing platform for reports and dashboards. It provides enterprise content management, collaboration, social networks, and business intelligence capabilities. SharePoint offers features like Performance Point services for creating dashboards and Excel and PowerPivot services for deploying Excel or PowerPivot to SharePoint. Role-based security can be implemented to ensure data access control.
By understanding the concepts and components of Business Intelligence and leveraging the capabilities of SQL Server and the Business Intelligence Platform, organizations can gain valuable insights from their data and make informed decisions to drive success.
Stay tuned for our next article, where we will dive deeper into Data Warehousing and its design methodologies.