What is getting more attention is the improvement of the intelligence and effectiveness of the execution of operational/transactional processes to improve business agility and intelligence.
The goal is to solve current problems, predict and prevent future issues, and implement and maintain solutions under changing conditions. There are key stages and processes involved in business intelligence, which can be synthesized into a BI framework:
Data Processing: Data Collection and Storage Gathering information from various sources, such as consumer interactions, transaction records, and employee data.
-Extraction: Identifying and copying data from its original sources.
-Transformation: Cleaning the data to fit analytical needs, which includes changing formats, removing duplicates, and renaming fields.
-Loading: Transferring the cleaned data into a data warehouse.
Data Analysis
-Descriptive Analysis: Using statistics to summarize data and understand dataset qualities.
-Exploratory Analysis: Looking for insights into the data via distributions and relationships between data fields, often using visualizations.
-Predictive Analysis & AI: Using techniques like machine learning and regression analysis to predict future trends and relationships.
-Implementation and Maintenance: Designing problem-solving systems that predict and prevent future problems, identify and solve current ones, and implement and maintain these solutions under changing conditions.
Methodologically, business intelligence is the process of transforming data into information and through discovery, transforming that information into knowledge/insight/foresight in better management and governance. BI has a lot of potentials but it can often end up being a static reporting tool instead of being used as an analytical tool. What is getting more attention is the improvement of the intelligence and effectiveness of the execution of operational/transactional processes to improve business agility and intelligence.
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