Digital Themes

Business Intelligence

Business intelligence (BI) is a broad term that encompasses an array of tools and techniques used to analyze data as it relates to business operations. Data collected through regular operations can be accessed and analyzed by data analysts using business intelligence tools. Business intelligence combines multiple processes and disciplines to accomplish a common goal, derive actionable insight from data for business decisions.

Business intelligence encompasses a range of data analysis processes to uncover answers to questions about business operations like “Did the new product launch have an impact on overall customer satisfaction?” or “How can we improve manufacturing efficiency in our lowest performing sector?”. Answers to such questions are found with the help of business intelligence reporting tools and data analysts. Data analysis techniques commonly used in business intelligence analysis commonly includes data visualization, statistical analysis, performance benchmarking, data mining and preparation. 

Modern business intelligence tools offer an array of visualization tools and dashboards to highlight new trends discovered in incoming and historical data. Business data is funneled into a central location (a data warehouse) where it can be accessed using business intelligence tools. BI tools ingest business-generated data, then output readable reports and data visualizations. BI dashboards often contain multiple reporting visualizations in a single location. 

BI tools are one aspect of business intelligence. Data analysts dig into data to find correlations between performance metrics and business operations. Data analysts use BI tools for statistical modeling that may include predictive analytics, text mining, sentiment analysis, video analytics.

Business benefits of using business intelligence:

  • Understand Customer Behavior. Business intelligence helps businesses understand customer behavior throughout the customer lifecycle.

  • Uncover Upcoming Trends. Use predictive analytics to model potential trends based on historical data. 

  • Increased Data Accuracy. Use business intelligence tools and techniques to improve overall data accuracy.

  • Operational Optimization and Efficiency. Data-driven decision making can help improve overall operational efficiency by surfacing opportunities for optimization improvements. 
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