Business intelligence (BI) refers to the technologies, strategies, and processes that organizations employ to analyze critical business data to gain actionable and timely insights. By aggregating and interpreting volumes of information from diverse sources, BI empowers leaders to make smart strategic decisions driven by facts rather than gut instinct.
As the global data universe expands exponentially – expected to grow to over 180 zettabytes by 2025 according to IDC – BI has become an indispensable tool for organizations to achieve operational excellence and sustained success through data-informed planning.
The Promise and Business Impact of BI
BI platforms bring together capabilities in reporting, visualization, dashboards, predictive modeling, data mining, benchmarking, and more to uncover trends and patterns within company data. Leading solutions make it possible for all levels of employees to safely access, comprehend, and act on analytics.
According to a survey by Dresner Advisory Services, 8 in 10 organizations consider BI to either enable or transform their operations. Adopters reported an average ROI of $13.01 for every dollar spent on BI tools.
By providing accurate visibility into past performance and future risk scenarios, BI enables confident strategic decision making with metrics to measure impact over time. Companies excel at customer retention, supply chain optimizations, financial forecasting, and other critical priorities guided by data rather than feelings. BI also facilitates richer cross-departmental alignment.
Core Capabilities and Components
Mature BI platforms share common technical architecture and components that enable various analytical applications:
ETL (Extract, Transform, Load): Safely extracts data from multiple sources, transforms it into compatible formats, and loads it into a central data warehouse or lake for analysis.
Data Warehouses and Lakes: Serves as a central repository to store structured, unstructured, and semi-structured data from across the organization and beyond.
Analytics and Reporting: Analyzes vast datasets using methods like statistics, data mining, modeling, machine learning, and natural language processing to uncover insights. Dashboards and dynamic reports then visualize patterns to guide decisions and actions.
Predictive Analytics: Uses statistical algorithms and machine learning techniques to make quantifiable forecasts about future outcomes, risks, and opportunities.
Data Mining: Employ sophisticated methods to segment data sets and identify hidden correlations, customer behaviors, and other non-intuitive discoveries.
Business Performance Management: Set KPIs and metrics then enable data-centered strategies and initiatives – from budgets and forecasts to risk assessments and more – to optimize performance.
8 Leading Business Intelligence Platforms
Here are 8 widely adopted BI solutions to consider for elevating data-informed decision making:
1. Microsoft Power BI
As a trusted provider of familiar tools like Excel, Microsoft Azure, and SQL Server, Power BI earns top marks for integrating tightly with the Microsoft ecosystem. The Power BI Service and Mobile App also make it easy for casual business users to produce rich interactive reports. Customizable dashboards deliver overviews of all critical metrics.
For larger enterprises, Power BI Pro offers enhanced collaboration, data capacities, and real-time analytics. It runs seamlessly across public and private clouds. Embedded analytics provide developers ways to fully customize and integrate Power BI for internal tools.
Key Strengths
- Complete range of analytical visualizations
- Natural language query interface via Q&A
- Real-time dashboards with streaming data
- Integration with Office 365, Dynamics 365, and Azure
- Granular drill downs to the original data source
- Embedded analytics and custom visual development options
Limitations
- Limited forecasting capabilities beyond visualizations
- Needs add-ons and tools for advanced predictive modeling
- Can encounter scalability bottlenecks for massive heterogeneous data sets
2. SAS Analytics
SAS has decades of experience powering analytics for the world‘s largest institutions in finance, government, healthcare, and more. SAS Analytics strikes an optimal balance between powerful advanced capabilities and ease of use for non-technical business teams. Users can tap into diverse data from SAS solutions and third parties to feed unique analytics needs.
SAS Visual Analytics provides intuitive and interactive data discovery. Machine learning underpins predictive modeling, forecasting simulations, optimization scenarios, and other smart functionalities like automated insights. Model management, model monitoring, champion/challenger approaches and other governance guardrails enable analytics accountability.
Key Strengths:
- Leading AI/ML data mining algorithms
- Real-time event stream processing
- Industry-specific solutions and advisory services
- Integrates third-party data sources like Hadoop
- Broad partner integrations ecosystem
- Global enterprise scalability and security
Limitations:
- Higher learning curve than self-service tools
- Generally more expensive than competitors
- Limited input flexibility compared to code-based platforms
3. Tableau
Tableau pioneered intuitive interactive data visualization capabilities for BI, helping non-technical business users easily explore information. Customers can build beautiful dashboards using clicks instead of code that highlight crucial KPIs, trends, and insights. Users can fluidly investigate details on demand with ad hoc analysis capabilities.
Along with simplicity, Tableau offers speed of deployment and a vast range of analytical functionality like forecasting, regressions, clustering, simulations, what-if analysis, and more. Tableau Desktop provides expert users advanced authoring capabilities. The SaaS Tableau Online delivers secure browser-based analytics manageable by IT.
Key Strengths:
- Powerful, customizable data visualizations
- Rich ad hoc analysis functionality
- Fast to implement with limited IT help
- Blend data easily across disparate sources
- Strong analytical and statistical models
- Can publish dashboards to web and mobile apps
Limitations:
- Steep pricing, especially for casual viewers
- Performance issues at extreme enterprise scale
- Advanced authoring requires expertise
4. Sisense
Sisense empowers customers to prepare, analyze, and visualize complex large datasets with minimal IT overhead. Users can cost-effectively crunch terabytes of data from across Cube, Cloud data warehouses, and Postgres – with flexibility beyond typical SQL limitations.
Sisense makes it easy to incorporate machine learning innovations to extract insights hidden within scattered silos of data. Customers can buildaccurate predictive models and interactive dashboards tailored to specialized needs. The platform monitors data continuously for changes to trigger instant alerts and updated reporting.
Key Strengths:
- Handles complex large datasets with ease
- Highly customizable AI analytic capabilities
- Continuously monitors data for alerts
- Cost-efficient scalability
- Transforms data silos into actionable insights
Limitations:
- Steep learning curves for some features
- Generally requires robust infrastructure
- Advanced functionality needs technical experts
- Limited forecasting and regressions
5. Information Builders
Information Builders emphasizes usability, flexibility, and speed-to-insight even for large global deployments. With an integrated analytics platform, it‘s straightforward for non-technical users to access, prepare, analyze, and visualize data at scale. Hundreds of out-of-the-box KPIs accelerate reporting.
Information Builder offers 360-degree customer analytics and combines external and internal data sources effortlessly. Users can add modules to enable more advanced analytics like predictive insights, location intelligence, and text analysis without adding complexity. Customers can also embed BI capabilities into existing workflows.
Key Strengths:
- User-friendly analytics suitable for beginners
- Highly scalable to the enterprise level
- Integrates internal and external data
- Extensible modular architecture
- Hundreds of pre-built KPIs and reports
Limitations:
- Limited advanced AI/ML capabilities
- Dated interface needs modernization
- Can encounter performance bottlenecks
6. Oracle Enterprise Performance Management (EPM)
Oracle EPM enables organizations to model business plans, forecast revenues, allocate resources, report performance, and more guided by data analytics. Tight integration across Oracle’s cloud software suite gives customers built-in financial intelligence, rich behavioral insights, and broad data access.
Powerful modeling, scenario planning, narrative reporting, and interactive dashboards help leaders determine the best strategies for efficiency, growth and risk management. Oracle EPM Cloud combines transactional, statistical and big data sources for accurate forecasting. Role-based BI applications provide data to users when and where they need it.
Key Strengths:
- Unified financial/operational data model
- Connects with full Oracle software ecosystem
- Sophisticated predictive planning capabilities
- Customized role-based intelligence
- Guided user experience needs minimal training
Limitations:
- Primarily focused on finance and operations use cases
- Steeper learning curve than self-service tools
- Potential integration issues with non-Oracle data
7. TIBCO
TIBCO Software delivers comprehensive enterprise-grade analytics ideal for large global organizations. TIBCO Data Science offers an extensive library of machine learning algorithms, text analytics, geospatial analytics, streaming analytics, and more to address specialized needs.
Smart capabilities like auto-insights, data cataloging, data preparation, and analytics lifecycle management enable scalability. TIBCO Spotfire brings together dashboards, reporting, visual analytics, and data wrangling in an intuitive interface. Users can apply predictive analytics, run simulations, and quickly share analytics through web interfaces and mobile clients.
Key Strengths:
- Broad advanced analytics functionality
- Automated insights and data management
- Real-time data streaming capabilities
- Integrates analytical assets enterprise-wide
- Scalable delivery of analytics to any application
Limitations:
- Significant IT infrastructure requirements
- Steep learning curve limits citizen access
- Generally one of the most expensive options
8. IBM Cognos
IBM Cognos Analytics excels at large-scale deployments, especially for global mid-market to enterprise organizations with complex needs. It features AI-driven data modeling and metadata management to accelerate analytics. Users can tap into data anywhere across public, private and hybrid multicloud data sources.
Interconnected modules enable functionality from ad-hoc querying to interactive dashboards, AI predictions, planning scenarios, virtual assistants, and more. Users can easily customize dashboards and analysis for any device. It’s straightforward to embed or share analytics through web portals, mobile apps, conversational interfaces, and other last-mile endpoints.
Key Strengths:
- Handles extremely large, complex datasets
- Augmented data preparation and modeling
- Ease of use even at scale with modules
- Flexibility to embed and share analytics
- Global enterprise security and governance
Limitations:
- Generally requires robust infrastructure
- Steep learning curves limit citizen access
- Can struggle with real-time data at scale
- Needs add-ons for top-tier AI/ML capabilities
Conclusion: Getting Started with Business Intelligence
As organizations create exponential data volumes, business intelligence platforms have become mission-critical to realize the abundant opportunities for strategic enhancements and innovations this data represents. BI empowers all employees – from C-suite leaders to frontline individual contributors – to safely tap into analytics.
But where should motivated teams start? Every organization has unique data challenges and analytical ambitions. There is no universal perfect BI tool.
The keys are to start small, learn quickly from controlled pilots, and iterate collaboration between decision makers, data experts, and technologists. While investing in culture and talent development to nurture the seeds for a fully data-driven enterprise in the years ahead.
