Business Intelligence

What is Business Intelligence?

Business Intelligence (BI) is a technology-driven process that involves collecting, analyzing, and presenting useful data.

BI is crucial in helping organizations make better business decisions based on the scrutiny of available information.

Business Intelligence comprises tools, strategies, and methodologies used to transform raw data into meaningful insights.

Competitive Edge: Access to data-driven market trends and customer behaviors positions businesses ahead of competitors.
Predictive Precision: BI forecasts future trends by analyzing historical data, and improving planning and resource allocation.
Customer-Centric Insights: BI uncovers deep customer preferences, aiding in targeted marketing and personalized experiences.
Operational Efficiency: It is possible to optimize processes, automates data analysis, and minimize manual effort, through Business Intelligence.
Risk Mitigation: BI detects potential risks through data analysis, enabling proactive measures for risk management.

There are many types of Business Intelligence, each suitable for a particular analytical need.

Descriptive analytics: This type of BI provides insights into what has happened in the past. It can be used to track performance, identify trends, and make predictions about future outcomes.
Diagnostic analytics: This type of Business Intelligence helps businesses to understand why things happened the way they did. It can be used to identify the root causes of problems and opportunities for improvement.
Predictive analytics: This uses data to make predictions about future events. It can be used to forecast demand, identify risks, and make better decisions.
Prescriptive analytics: It provides recommendations for how to improve performance. It can be used to optimize operations, allocate resources, and make better decisions.

Business Intelligence is a complex system made of several individual processes.

They include:

Data collection: Gathering data from a variety of sources, such as transactional systems, operational systems, and external data sources.
Data cleansing: Removing errors and inconsistencies from the data. This is important to ensure that the data is accurate and reliable.
Data analysis: Using BI tools to analyze the data and extract insights. This can be done using descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Data visualization: Presenting the data in a way that is easy to understand and interpret. This can be done using charts, graphs, and other visuals.
Data governance: Ensuring that the data is managed and used in a consistent and ethical way. This includes setting policies and procedures for data collection, and analysis.