Business intelligence/Data analysis: differences and complementarity

Business Intelligence (BI) or business intelligence

Business Intelligence (BI) records, studies and works with business data to execute in the future. Data that generally explains the company’s past performance. BI provides elements that help the manager and managers to assess the company’s progress in relation to progress.
To process Business Intelligence (BI), there is a set of tools that can extract information about the company. These tools analyze the company’s history of successes and underperformance. BI provides a flow of information, a single tool that QlikSense with a data visualization solution, to initiate improvement actions.

The concept of data analysis or business analysis

Data Analytics transforms raw data into meaningful information to infer patterns, measure trends and transform patterns to drive business growth. In this process, business leaders analyze data and strive to bring innovation, analysis allows a company to make unique changes to increase the level of success.
Data Analytics (DA) or Business Analytics involves predictive analysis based on past patterns that reflect future growth. Many tools dedicated to data analysis help managers and executives to adopt a relevant strategy for their business.
Data Analytics converts raw data into meaningful information and analyzes future trends using predictive models and technical tools that help the manager grow the business based on a set of algorithms.

Differentiators between BI and data analysis

In terms of innovation

Business Intelligence revolves around the operation while the other is more inclined towards innovation. Since Business Intelligence collects raw data and assesses a company’s historical growth, it may or may not emphasize innovation.
Data Analytics converts raw data, analyzes it to define future trends and patterns, enabling managers to engage with operations in innovative ways. Business Intelligence, unlike Data Analytics, stores data in a raw format, which is woven into an algorithm that helps extract the underlying patterns.

When it comes to predicting the future

Business Intelligence is more backward-looking when Data Analytics is forward-looking. BI emphasizes the study of data based on situations that have already occurred in the company’s history. Data Analytics tends to highlight future patterns that may occur in the future. BI is found to be more relevant when it comes to past operating patterns that led to the formation of data for DA.
BI targets the company’s historical records while DA implements innovative trends in the future for better growth. Business Intelligence is more concerned with achieving goals that were already part of the business goals and Data Analytics leads to the addition of goals to advance it according to the patterns to be followed. A distinction is therefore made between adding goals and achieving goals.

Different concepts that fit together

Both concepts are essential, with Business Intelligence and Data Analytics helping to collect raw data and analyze it for future operations. Although the 2 concepts seem similar, major differences, achievement of goals and decision-making processes for Data Analytics, the study of growth and business models according to data collected during operations are passed for BI.
Business Analytics deals with the transformation of raw data into meaningful material to ultimately chart future trends on a predictive basis by questioning past patterns and strategies.
If the two concepts are very different from each other, they overlap in such a way that Business Intelligence cannot do without Business Analytics and vice versa.

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