Data Analytics topics for business projects
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Data analytics for SMEs: business intelligence, dashboards and decision quality
Data analytics in a business context does not start with the BI tool but with the question: which decisions should data improve, and which data is available and reliable enough to support them? This page structures the path from data quality through KPI architecture to meaningful dashboards.
The typical mid-market starting point: data is distributed across ERP, CRM, production systems and Excel silos. Without central integration, dashboards display numbers but do not reflect a single source of truth. Data warehouses or modern lakehouse architectures lay the foundation for consistent reporting.
Data quality is the most common project delayer: missing values, inconsistent keys, historical data migration errors. A data quality assessment at project start saves more time than a retrospective cleansing project after dashboard launch.
KPI architecture: from business goals to measurable indicators
Meaningful dashboards are built top-down: strategic KPIs first, then operational drivers, then detail drill-downs. Bottom-up dashboards showing all available data points produce information noise rather than decision support.
Every KPI needs a binding definition: calculation formula, authoritative data source, refresh cadence and threshold for action required. Without this definition, conflicts arise between departments calculating the same metric differently.
Predictive analytics extends historical reporting with forecasting: demand forecasting, churn probability, maintenance need prediction. The effort for model development and maintenance is significantly higher than for descriptive dashboards – the value must be assessed separately per use case.
Tool selection and operations: Power BI, Tableau, Looker and custom
For Microsoft 365 environments, Power BI is the natural choice through native AD integration, familiar licensing and low entry barrier for Excel-proficient users. Tableau and Looker excel at complex exploratory analysis and enterprise-wide self-service scenarios.
Custom development (React-based dashboards, D3.js, Observable) makes sense when standard tool visualisation is insufficient or the dashboard must be deeply embedded in an existing application. Maintenance effort is higher than for commercial BI platforms.
Data security in BI systems: row-level security, data classification and access logging are GDPR-relevant requirements as soon as personal data flows into reports. This configuration belongs in the rollout scope, not a later hardening sprint.
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