Business Intelligence (BI)
Technologies and strategies for analysing business data to support better decisions – with dashboards, reporting, data warehousing and visualisation.
Data is the new oil – but only when it is analysed and turned into insights. Business Intelligence turns raw business data into actionable information. Instead of gut-feel decisions, BI tools provide dashboards and reports that show trends, anomalies and forecasts. From retail to enterprise, data-driven companies make better and faster decisions.
What is Business Intelligence (BI)?
Business Intelligence (BI) covers technologies, strategies and practices for collecting, integrating, analysing and presenting business data. The goal is to turn raw data into useful information for better decisions. BI systems typically include: data sources (ERP, CRM, shop), a data warehouse (central store), ETL (Extract, Transform, Load) for preparation, and BI tools (Power BI, Tableau, Metabase, Looker) for visualisation and analysis. Modern BI increasingly uses machine learning for predictive analytics.
How does Business Intelligence (BI) work?
The BI process: 1) Extract data from sources (databases, APIs, files). 2) Transform: clean, standardise and enrich (ETL). 3) Load into a data warehouse or lake. 4) Model: structure for analysis (star/snowflake schema). 5) Analyse: users build reports, dashboards and ad-hoc queries. 6) Act: insights feed into decisions. Self-service BI lets business users run analyses without IT.
Practical Examples
Sales dashboard: Real-time view of revenue, pipeline, conversion and forecasts by region, product and rep.
Churn analysis: BI identifies customers likely to churn from usage patterns – teams can act in time.
Inventory: Analysis of sales, seasonality and lead times optimises stock and can cut excess by ~30%.
Marketing attribution: Multi-touch attribution shows which channels actually drive conversions.
Finance: Automated month-end close, budget vs actual and cash flow forecasts replace manual Excel.
Typical Use Cases
Sales: Pipeline management, forecast accuracy and sales performance
Finance: Budget control, profitability analysis and regulatory reporting
Marketing: Campaign performance, customer journey and ROI
Operations: Capacity, supply chain and quality metrics
HR: Turnover, recruiting funnel and labour cost analysis
Advantages and Disadvantages
Advantages
- Data-driven decisions instead of gut feeling – faster and more informed
- Automated reporting saves hours of manual Excel work per week
- Early warning: Anomalies and trends are spotted before they become problems
- Self-service: Business users can analyse without IT tickets
- Transparency: Everyone works with the same, up-to-date numbers
Disadvantages
- Data quality: BI is only as good as the underlying data (garbage in, garbage out)
- Implementation: Data warehouse, ETL and model need significant investment
- Tool overload: Many BI tools can lead to inconsistent analyses
- Misinterpretation: Data without context and statistical literacy can lead to wrong conclusions
Frequently Asked Questions about Business Intelligence (BI)
Which BI tool is right?
Do I need a data warehouse?
What does introducing BI cost?
Related Terms
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