What is Business Intelligence?
Business Intelligence is the combination of technologies, practices, and strategies used to collect, integrate, analyse, and present business data in a way that supports better decision-making. It transforms raw data into meaningful dashboards, reports, and visualisations that give leaders a clear view of organisational performance.
What is Business Intelligence?
Business Intelligence, commonly referred to as BI, encompasses the tools, technologies, and practices that help organisations turn raw data into actionable insights. At its core, BI answers the question: "What is happening in our business right now, and how does it compare to what happened before?"
BI systems pull data from across the organisation, consolidate it into a unified view, and present it through interactive dashboards, automated reports, and data visualisations that business leaders can understand and act on without needing technical skills.
How Business Intelligence Works
A typical BI implementation involves four layers:
1. Data Integration
Data is collected from multiple source systems including ERP, CRM, accounting software, e-commerce platforms, marketing tools, and operational databases. This data is consolidated through ETL processes into a data warehouse or data mart.
2. Data Modelling
The integrated data is organised into logical structures, often called data models or cubes, that reflect how the business thinks about its operations. A sales data model, for example, might organise data by region, product category, customer segment, and time period.
3. Analysis and Exploration
Analysts and business users explore the data using BI tools. This includes slicing and dicing data across dimensions, drilling down from summary to detail, identifying trends, spotting anomalies, and comparing actual performance against targets.
4. Presentation and Distribution
Insights are delivered through dashboards (real-time visual summaries), scheduled reports (automated periodic updates), ad hoc queries (user-driven exploration), and alerts (notifications when metrics cross defined thresholds).
Popular BI Tools
| Tool | Strengths | Best For |
|---|---|---|
| Tableau | Powerful visualisation, large community | Complex visual analytics |
| Microsoft Power BI | Microsoft ecosystem integration, affordable | Companies using Microsoft 365 |
| Looker (Google) | Strong data modelling, cloud-native | Google Cloud-centric organisations |
| Metabase | Open-source, easy setup | Budget-conscious SMBs |
| Apache Superset | Open-source, flexible | Technical teams comfortable with configuration |
| Qlik Sense | Associative data model, AI-assisted | Complex multi-source analysis |
For SMBs in Southeast Asia, Power BI and Metabase are often the most practical starting points. Power BI offers excellent value for organisations already using Microsoft tools, while Metabase provides a free, open-source option that is easy to deploy.
Business Intelligence in Southeast Asian Companies
BI is particularly valuable for companies operating across ASEAN because it provides:
- Cross-market visibility: A single dashboard showing performance across Singapore, Malaysia, Indonesia, Thailand, and other markets with consistent definitions and metrics.
- Real-time monitoring: Track key metrics like daily revenue, order volumes, and customer acquisition across markets as events happen rather than waiting for monthly reports.
- Self-service analytics: Empower country managers and regional directors to explore data and answer their own questions without depending on a central analytics team.
- Competitive benchmarking: Compare performance across markets and business units using consistent metrics to identify best practices and areas needing improvement.
BI vs Advanced Analytics
It is important to understand where BI ends and advanced analytics begins:
- BI focuses on historical and current data: what happened, when, and where. It is primarily descriptive and diagnostic.
- Advanced analytics (predictive and prescriptive) focuses on the future: what will happen and what to do about it. It uses machine learning and statistical modelling.
Most organisations should establish strong BI capabilities before investing in advanced analytics. BI provides the data infrastructure, data literacy, and analytical culture that advanced analytics requires to succeed.
Implementing BI Successfully
- Start with executive sponsorship: BI adoption requires cultural change. Leadership must champion data-driven decision-making.
- Define key metrics: Identify the 10-15 most important metrics that leadership needs to track. Do not try to measure everything at once.
- Invest in data quality: BI is only as good as the data it presents. Ensure source data is clean, consistent, and timely.
- Design for the user: Dashboards should be intuitive and answer specific business questions. Avoid cluttered displays with dozens of charts.
- Enable self-service gradually: Start with pre-built dashboards for common questions, then train power users to create their own analyses.
- Iterate based on feedback: BI is never done. Continuously refine dashboards and reports based on how users interact with them.
Business Intelligence is the most fundamental and widely applicable form of data analytics. While advanced AI and machine learning capture headlines, BI is what most organisations need first and use most often. It provides the daily operational visibility that enables consistent, informed decision-making across the organisation.
For companies in Southeast Asia managing growth across multiple markets, BI eliminates the common problem of information fragmentation. Without BI, leadership relies on manually compiled reports that arrive late, use inconsistent definitions, and often disagree with each other. With BI, everyone works from the same data, in real time, with shared definitions.
The business case for BI is well-established. Organisations with mature BI capabilities make faster decisions, identify problems earlier, and allocate resources more effectively. For a CEO or CTO, BI should be the first analytics investment because it creates the data culture, infrastructure, and literacy that all subsequent analytics and AI initiatives depend on.
- Choose a BI tool that matches your team technical capabilities and existing technology stack. A powerful tool that nobody uses is worse than a simpler tool that the whole team adopts.
- Start with a small number of well-designed dashboards for the most critical business questions. Expansion should be driven by user demand, not technical ambition.
- Data quality is the biggest determinant of BI success. Users will abandon dashboards the moment they spot inaccurate numbers. Invest in data validation before launching.
- Self-service BI sounds appealing but requires investment in data literacy training. Plan for training and ongoing support, not just tool deployment.
- Mobile-friendly BI is important for leaders in Southeast Asia who travel frequently across markets. Ensure your BI tool provides a good mobile experience.
- Budget for ongoing BI development. Dashboards need maintenance, new data sources need integration, and user needs evolve. Allocate at least 20 percent of initial build effort annually for maintenance.
Frequently Asked Questions
How much does Business Intelligence cost for an SMB?
BI costs vary widely. Open-source tools like Metabase are free to self-host. Power BI Pro costs approximately USD 10 per user per month. Tableau starts at around USD 70 per user per month. Beyond software, factor in data warehouse costs (USD 200-2,000 per month for cloud services), implementation effort (2-8 weeks for initial setup), and ongoing maintenance. A realistic total cost for an SMB BI implementation is USD 500-5,000 per month.
How long does it take to implement BI?
A basic BI implementation with one or two data sources and a handful of dashboards can be completed in two to four weeks. A more comprehensive implementation connecting multiple systems with complex data models typically takes two to four months. The timeline is heavily influenced by data quality, with significant time spent on data cleaning, standardisation, and validation before dashboards can be built.
More Questions
Not always, but a data warehouse significantly improves BI quality and performance. Some BI tools can connect directly to operational databases, which is fine for simple use cases. However, querying operational databases can slow down production systems, and data from multiple sources needs to be consolidated somewhere. For anything beyond basic single-source reporting, a data warehouse is strongly recommended.
Need help implementing Business Intelligence?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how business intelligence fits into your AI roadmap.