The business intelligence (BI) software market has entered a renaissance era. Fueled by exponential data growth and the democratization of analytics, organizations are strategically investing in next-generation platforms to harness data-driven insights that deliver competitive advantage.
This comprehensive guide examines the evolution of BI, components of a modern stack, critical capabilities to evaluate, and the leading solutions driving this $20B industry today.
The Evolution of Business Intelligence
Gartner forecasts the business intelligence market will exceed $20B by 2026, evolving from just reporting and monitoring to powering data-driven decisions across enterprises.
Past – Early BI focused on departmental reporting off business data warehouses while IT maintained control
Present – BI democratization with self-service analytics, AI, and flexible cloud deployment
Future – Pervasive, real-time intelligence fully aligned to business goals
Understanding this transformation is key to leveraging BI tools strategically.
Growth in Cloud and Embedded BI
The shift to cloud and SaaS delivery of analytics accelerated during the pandemic, bringing new levels of accessibility, flexibility, and scale. IDC predicts over 50% of new BI investments will shift to the cloud by 2025.
Embedding intelligence is equally prioritized. By integrating insights within existing apps and workflows, BI transcends reports to provide in-context value. Gartner sees embedded BI capabilities in over 70% of platforms by 2026.
Augmented and Automated Analytics
Leveraging AI techniques like machine learning, NLP, and computer vision, next-gen BI solutions can automate insights by:
- Cataloging, blending, and preparing data automatically
- Generating advanced analytics models tailored to your data
- Converting data into interactive visualizations and narratives
This increases efficiency, while freeing users to focus on higher value analysis.
Modern Business Intelligence Capabilities
The modern BI stack brings together a collection of technologies for ingesting, storing, analyzing, and sharing data-driven insights.
Components of a Modern BI Architecture
Assess BI software against the following core capabilities:
Data Connectivity
The foundation of reliable analytics relies on flexible and high-performance data connectivity to infrastructure storing enterprise data like:
- Relational databases – MySQL, Oracle, SQL Server, PostgreSQL
- Data warehouses – Snowflake, BigQuery, Redshift
- Data lakes – AWS S3, Azure Data Lake Storage
- SaaS apps – Salesforce, Marketo, ServiceNow
- Custom APIs – Connect to in-house applications and microservices
Optimizing ingestion using logical mappings, cache optimizations, query federation, and other techniques is vital to delivering performance at scale.
Many modern BI tools also provide integrated data preparation abilities for blending, cleaning, and cataloging to ready data for analysis.
Impactful Visualizations
Leveraging compelling visualizations, BI platforms convert abstract data into intuitive graphics tailored to how humans best process information.
From standard charts and plots to rich multimedia experiences, pixel-perfect dashboards make digesting complex data easy.
Interactive BI Dashboard (Image Source: Dundas)
Filters, tooltips, and drilling down into underlying details enable users to freely explore data from many angles at their own pace.
Actionable Insights
Driving strategic actions from analytics is the end goal. By using techniques like alerts, collaboration, and workflow integration, modern BI enables seamless consumption and sharing of insights across teams.
Platform capabilities like contextual notifications when KPIs shift, threaded discussions on reports, and publishing dashboards to portals or via TVs foster a truly data-driven culture.
Scalability and Security
As usage expands across global enterprises, BI infrastructure must scale to ingest huge data volumes while maintaining governance, access controls, and trust.
Multi-layer data architecture, smart caching strategies, and workload optimization provide consistent performance. Robust role-based access controls (RBACs), data masking, and encryption enable governance.
Finally, maximizing the return from BI hinges on change management and driving continuous usage.
10 Leading Business Intelligence Platforms
With the BI landscape changing rapidly, hundreds of tools exist across needs from free tools for individuals up to platforms managing petabytes of data for global enterprises.
Based on critical capabilities and proven implementations, these 10 leading options provide a robust starting point to evaluate.
1. Sisense
Sisense empowers complex analytics over large data sets by leveraging a high-performance, in-chip architecture to crunch terabytes of data from across sources and refine it with AI accelerated models.
Key Features
- Patented In-Chip engine for complex ETL and ML workloads
- Design flexible dashboards using drag-and-drop
- Embed analytic apps with REST APIs and JS Framework
- Cloud, on-prem or hybrid deployment
Ideal For
- Data and statistics heavy use cases
- Building analytic features into products
- Data science and ML teams
- Companies with complex data
Interactive Supply Chain Analysis Dashboard (Image Source: Sisense)
2. Tableau
Tableau helps business users across skill level quickly visualize data by providing an intuitive, self-service interface to see and understand their information in minutes.
Key Features
- Pixel-perfect dashboards and rich visualizations
- Vibrant ecosystem of charts, tools and apps
- Supports live queries to over 70 data sources
- Integrates statistical, mapping, and forecasting capabilities
Ideal For
- Individual to enterprise self-service BI
- Enabling data democratization
- Rapid prototyping of new data analysis
- Sharing insights across the business
Interactive Sales Analysis Dashboard (Image Source: Tableau)
3. Qlik
Qlik sets itself apart with its high-performance associative engine allowing users to explore all connections in data across multiple sources for deeper analysis beyond simple dashboards.
Key Features
- Associative engine revealing relationships in data sets
- Touch-based exploration and mobility
- GeoAnalytics with intelligent mapping
- Conversational analytics via AI assistant
Ideal For
- High-scale complex analysis
- Centralizing multiple data sources
- Data loading and transformation
- Field worker mobile analytics
4. Microsoft Power BI
Wholly integrated into the greater Microsoft cloud ecosystem, Power BI makes discovering, visualizing, and sharing data-driven insights seamless for Microsoft-centric organizations.
Key Features
- Hundreds of data connectors to Microsoft apps
- Interactive reports and AI-powered analytics
- Dashboards filtered by roles and permissions
- Native integration with Office 365 and Azure
Ideal For
- Microsoft shops seeking an integrated BI solution
- Embedding intelligence in Dynamics 365 apps or Power Platform
- Building reports on Excel or SQL Server data
- Scale while maintaining governance
Sales and Marketing Analytic Dashboard (Image Source: Microsoft)
5. Looker
While Looker requires more technical skill than self-service tools, its unified data platform allows enterprises to embed analytics within products and serve insights tailored to individual teams.
Key Features
- Sophisticated data modeling with LookML
- API access and ability to embed analytics
- Writeback integration and personalized dashboards
- Integrated collaboration tools
Ideal For
- Large enterprises desiring customization
- Data analysts, engineers and developers
- Companies wanting analytics integrated into apps
6. Domo
Domo consolidates all enterprise data onto an integrated cloud platform with tools to connect, fuse, and visualize data from anywhere while enabling robust governance.
Key Features
- Library of 1000+ connector types
- Integrates biz ops, sales, finance data
- Curated industry and dept templates
- Collaboration tools to share securely
Ideal For
- Fortune 500 companies
- Heavily regulated industries like Financial Services
- Executives seeking holistic firm insights
- Creating branded analytic content or portals
7. IBM Cognos Analytics
IBM Cognos unifies business planning, modeling, predictive analytics, reporting and monitoring within one governed workspace.
Key Features
- Self-service modeling to shape data
- Conversation-driven insights via NLP
- Planning, modeling and predictive together
- Ability to embed and customize apps
Ideal For
- Complex enterprise reporting needs
- Advanced forecasting and predictive use cases
- Building planning models
- Custom app development
8. Oracle Analytics Cloud
Oracle Analytics Cloud fully integrates AI and conversational guidance to enhance decisions, while offering extensive flexibility to access insights everywhere.
Key Features
- Pixel-perfect dashboards and visualizations
- Natural language interaction by voice
- Industry specific analytic templates
- Broad data connectivity options
Ideal For
- Balance of usability and customization
- Voice-driven analytics
- Heavily regulated sectors
- Existing Oracle application users
9. SAP Analytics Cloud
SAP Analytics Cloud combines business intelligence, predictive analytics, and planning onto a unified platform powered by SAP HANA able to connect 28+ data sources.
Key Features
- Unified BI, planning and predictive
- Conversation-driven insights via NLP
- Industry specific analytics content
- Broad connectivity beyond just SAP data
Ideal For
- Unifying enterprise planning, reporting and monitoring
- Embedded, extendable analytics
- SAP partner ecosystem
- Existing SAP customers with HANA
10. Zoho Analytics
Zoho Analytics makes enterprise-grade analytics accessible for SMBs with an easy end-to-end platform to connect, prepare, visualize and share data-driven insights.
Key Features
- 200+ connector types
- Intuitive self-service functionality
- User management and access controls
- Pixel-perfect dashboard design
Ideal For
- SMB departmental analytics
- Blending data from niche applications
- Basic dashboards and reporting
- Sharing reports with wider team
Considerations for Evaluation
With fundamentals on BI capabilities and leading options covered, what process should you follow to identify the best platform aligned to data culture and strategic analytics vision?
Build Requirements Matrix
The first step entails capturing business requirements across stakeholder groups:
Executive – How will BI impact desired business outcomes like efficiency, agility in responding to emerging opportunities or threats?
IT – What existing infrastructure must integrate? How to scale analytics in the cloud while maintaining security, compliance, and governance?
Data leaders – What are current gaps preventing using analytics? What emerging techniques like AI may prove strategic longer-term?
Business users – How will BIempower roles across sales, marketing, finance and beyond day-to-day?
Customer-facing – What analytics or intelligent features create value for customers and partners?
Consolidating responses into a requirements matrix focused on critical needs rather just desired functionality enables sharper vendor evaluations.
Request Platform Demos
Many evaluation mistakes originate from limited visibility into how users actually leverage BI software.
Seeing is believing, so take advantage of trial offers or customized demonstrations from leading vendors showing their platforms benefits based on your unique use cases.
Observe ease of use, flexibility in data connectivity, dashboarding, predictive modeling and other areas significant to your objectives.
Prioritize Scalability
The most frequent breakdown in BI success traces back to shortsighted infrastructure decisions unable to flex with data complexity, user growth, or new techniques.
While today‘s need may be straightforward reporting, prioritizing architecture, data management and governance allows graceful evolution to augmented, predictive and other cutting-edge analytics.
Consider Total Costs
Licensing fees mark just one component of overall investment and ongoing costs tied to the BI platform choice such as:
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Hidden IT infrastructure – Additional database, storage and computing capacity
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Maintenance and support – Vendor or partner provided packages
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Training and adoption – Enabling users and growing consumption
Using TCO models ensure accurate forecasting of direct costs plus vital change management for adoption.
Emerging Trends Reshaping Business Intelligence
Beyond current analytics techniques leveraging descriptive and diagnostic data analysis, several technologies on the horizon promise even deeper business value from BI initiatives.
Composable BI
Rather than monolithic platforms, composable BI provides flexibility to select only required capabilities across best-of-breed data, analytics, visualization and other components. Think "Lego blocks".
As Gartner notes, these composed solutions help, "optimize price performance by right-sizing BI capabilities".
Organizations also mitigate risk of vendor lock-in around singular products unable to keep pace as needs mature.
No Code Business Intelligence
No code BIsolutions aim to open analytics even for non-technical users via simple drag-and-drop visualization features, customizable templates, conversational natural language and more.
By democratizing capabilities once possible only via consultants or data engineers coding complex scripts and queries, problem-solving accelerates across the business.
The tradeoff remains less flexibility than traditional coding-based customization for now.
Data Fabric
Data fabric platforms provide consistent access and sharing of data across storage infrastructure, applications and organizational silos via semi-automated pipelines plus unified metadata and governance.
This logical abstraction layer helps companies, "handle…analytics at greater volumes, velocities and complexities" reports Forrester.
Smarter enterprise data management unlocks more impactful BI scaling to users across headquarters, stores and beyond.
Open Source Business Intelligence
While lacking some sophisticated capabilities or user friendly features at times, open source BI tools such as Metabase, Redash, and Superset offer compelling free options.
The community frequently adds innovative functionality matching proprietary equivalents rendering open source ideal for trial projects or cost-cautious teams.
Just ensure to factor in total implementation costs beyond merely software licensing.
Take the Next Step Towards Intelligence
The potential of business intelligence to unlock data-centric decision making helping companies compete and respond in dynamic markets is profound. And BI innovation is accelerating daily.
I hope this guide covering modern BI capabilities, leading solutions, and emerging trends offers a strategic foundation to shape plans leveraging analytics as a vital enterprise asset.
While tactical reporting remains crucial, the present pace of disruption demands nothing short of pervasive, real-time intelligence fueling constant adaptation.
Choose technology partners seeking to push the boundaries of what data can reveal. Let human imagination, not stale software limitations, bound BI ambition. The future rewards those harnessing insight at unmatched speed and scope.
