Business Intelligence Features

The Goal of Business Intelligence

We recognize that the goal of each business intelligence application is to fulfill a business need and the most important component of fulfilling that need is the business logic. Information presentation and user interaction, though important, are a means to the end. To better serve the programmers and enable them to concentrate on the soul of the application, Style Intelligence simplifies the tedious task of formatting, layout and handling user actions. More importantly, developers do not actually need to know how a report is accessed.

InetSoft's product is not a standalone server, but a collection of server-side components. The server components are based on the open Java RMI, CORBA and Servlet standards. This enables the components to be tightly integrated into existing applications and servers and to fully take advantage of the services of the underlying distributed platform. Another benefit is the reduced system administration cost. Instead of being managed separately from the existing server, the InetSoft components can become an integrated part of the server and share the same administration procedures.

Key Features of a Business Intelligence (BI) Application

Ensuring data accuracy, completeness, and reliability is critical for meaningful analysis. Data quality management tools within a BI application help maintain the integrity of the data.

Key aspects include:

• Data Cleansing: Automatic identification and correction of errors, inconsistencies, and duplicates in the data.
• Data Profiling: Assessing data quality and understanding data distributions, patterns, and anomalies.
• Master Data Management (MDM): Creating a single, authoritative source of truth by managing and consolidating key business data.

4. Data Modeling

Data modeling involves creating abstract representations of the data structure, which simplifies the analysis process.

Key aspects include:

• Logical Data Models: Defining data entities, attributes, and relationships without considering physical storage.
• Physical Data Models: Detailed representations of data storage, including tables, indexes, and schemas.
• Dimensional Modeling: Creating star or snowflake schemas to facilitate OLAP (Online Analytical Processing) and complex querying.

5. Advanced Analytics

Advanced analytics features enable users to perform complex data analysis and derive deeper insights.

Key aspects include:

• Statistical Analysis: Tools for performing regression analysis, hypothesis testing, and other statistical procedures.
• Predictive Analytics: Leveraging machine learning algorithms to forecast future trends and behaviors based on historical data.
• Prescriptive Analytics: Providing recommendations for actions to achieve desired outcomes based on predictive insights.

6. Data Visualization

Data visualization tools are critical for presenting data in an accessible and understandable format.

Key aspects include:

• Interactive Dashboards: Customizable dashboards that display key metrics and KPIs (Key Performance Indicators) through charts, graphs, and gauges.
• Geospatial Visualization: Mapping tools that visualize data based on geographic locations, useful for identifying regional trends and patterns.
• Drill-Down and Drill-Through: Interactive features that allow users to explore detailed data underlying summarized visualizations.

7. Reporting

Reporting capabilities enable the creation and distribution of detailed reports based on data analysis.

Key aspects include:

• Ad-Hoc Reporting: Empowering users to create custom reports on-the-fly without relying on IT support.
• Scheduled Reporting: Automated generation and distribution of reports at specified intervals.
• Report Formatting: Tools to customize the layout, format, and presentation of reports to meet specific requirements.

8. Collaboration and Sharing

Collaboration features facilitate the sharing of insights and promote teamwork.

Key aspects include:

• Shared Dashboards and Reports: Allowing users to share dashboards and reports with colleagues, ensuring everyone has access to the same information.
• Annotations and Comments: Enabling users to add notes and comments to reports and visualizations, fostering collaborative analysis and decision-making.
• Version Control: Tracking changes and maintaining different versions of dashboards and reports to ensure consistency and accuracy.

9. Mobile Access

Mobile BI extends the reach of the BI application to smartphones and tablets, allowing users to access insights on-the-go.

Key aspects include:

• Responsive Design: Ensuring that dashboards and reports are optimized for viewing on mobile devices.
• Offline Access: Enabling users to access key reports and dashboards even without an internet connection.
• Mobile Notifications: Real-time alerts and notifications sent to mobile devices to keep users informed about critical metrics and events.

10. Security and Governance

Security and governance features are essential to protect sensitive data and ensure compliance with regulations.

Key aspects include:

• Role-Based Access Control (RBAC): Restricting access to data and functionalities based on user roles and permissions.
• Data Encryption: Encrypting data both at rest and in transit to safeguard against unauthorized access.
• Audit Trails: Logging user activities and changes to data to ensure accountability and traceability.

11. Integration with External Tools

Modern BI applications offer seamless integration with other enterprise tools and platforms.

Key aspects include:

• APIs and Connectors: Providing APIs and connectors to integrate with CRM, ERP, and other enterprise systems.
• Big Data Integration: Compatibility with big data platforms like Hadoop and Spark to handle large datasets.
• Third-Party Analytics Tools: Integration with specialized analytics tools and libraries to extend the capabilities of the BI application.

12. Self-Service BI

Self-service BI empowers non-technical users to perform their own data analysis without needing IT assistance.

Key aspects include:

• User-Friendly Interface: Intuitive, drag-and-drop interfaces that simplify the process of data analysis and visualization.
• Data Discovery: Tools for exploring and identifying patterns and trends within the data.
• Customizable Dashboards: Allowing users to create personalized dashboards tailored to their specific needs.

13. Scalability and Performance

A robust BI application must be scalable and performant to handle growing data volumes and user demands.

Key aspects include:

• Distributed Computing: Leveraging distributed computing architectures to enhance processing power and handle large datasets.
• In-Memory Processing: Utilizing in-memory processing to speed up data retrieval and analysis.
• Load Balancing: Distributing workloads across multiple servers to ensure optimal performance and reliability.

14. Cloud BI

Cloud BI offers flexibility, scalability, and cost-efficiency by hosting the BI application on cloud platforms.

Key aspects include:

• Scalability: Easily scaling resources up or down based on demand.
• Cost-Efficiency: Reducing capital expenditure on hardware and infrastructure by utilizing a pay-as-you-go model.
• Accessibility: Providing access to BI tools and data from anywhere with an internet connection.