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5 Differences Between Business Intelligence and Analytics

Technology solutions that define themselves as “business intelligence” (BI) or “analytics” both provide insights based on past and present data in order to shape decisions that will help to achieve business objectives. What are the similarities and differences between business intelligence and analytics solutions? Both types of solutions help to organize data and present it in the form of effective visualizations. When it comes to the differences, some experts have opined that BI helps to run the business, while analytics are needed if you want to change the business. 

Let us look at five major differences between business intelligence and analytics. 

1. BI helps to understand the present situation based on the past, while analytics shows what could happen in the future based on different scenarios

BI collects data and creates visualizations that enable interpretation. These visualizations help stakeholders to see trends in the past and make decisions based on them. Business analytics solutions, on the other hand, have the ability to predict or forecast the future. They do this by building models based on available data and predicting the outcomes of different scenarios.

Analytics capabilities help companies to make more informed decisions that will maximize opportunities and better manage potential risks.

For instance, a BI product will show you who your top 10 customers were last year. An analytics product will show you who are likely to be your best customers next year, based on historical trends.

2. BI shows what has happened, analytics helps us understand why

The focus of BI is to collect, report, and monitor relevant data, while that of business analytics is to find actionable insights and predict future outcomes.  

Let’s assume that you are using both BI and analytics to achieve KPIs related to production. A dashboard from the BI application helps you track performance versus target for the delivery time of raw material from suppliers and the rejection rate of various materials. 

Let’s say that you can see that the defect rate for plastic components, which was very low before but has become alarmingly high in the past three months. This problem obviously needs further investigation, which is where analytics comes in. You can now analyze the data about individual components and see whether the defects are across all or only some. You can also analyze the data by different suppliers,as well as different types of plastic material.

The ability to dive deep into the data and use a variety of queries can help you to identify the root cause. For instance, maybe you find that there were certain new components designed a few months ago, and one of the suppliers is finding it tricky to manufacture them. Using this insight can help you solve this specific problem.

3. BI is used to increase efficiency, while analytics is used to uncover opportunities

Choosing between business intelligence and analytics also depends on your strategic focus. If your business model is stable and you are looking to increase efficiency and take operational decisions in real-time, then a BI solution will serve you well. On the other hand, if you are seeking insights in order to uncover new business opportunities, then you should consider an analytics product, as it will enable you to analyze your company performance and market trends so that you can shape the future.

4. Business intelligence and analytics tools perform different functions

The list of functions performed by a typical BI tool includes: reporting data, tracking KPIs and metrics, automated monitoring and alerts, delivering visualizations on dashboards, providing data cubes, the ability to slice and dice,  drill-down, and perform ad hoc queries. 

The functions of an analytics tool are: statistical and quantitative analysis, data mining, predictive modeling, multivariate testing, forecasting, and scenario analysis. 

5. Machine learning and artificial intelligence are making analytics more powerful by the day

As machine learning and artificial intelligence (AI) capabilities develop further, analytics products are evolving rapidly to unveil deeper insights, make predictions, and even recommendations. These techniques are often called augmented analytics and include data and text mining, pattern matching, forecasting, sentiment analysis, network, and cluster analysis, and multivariate statistics. While BI is largely unchanged, analytics is a fast developing space.

With this clarity about the similarities and differences between business intelligence and analytics technology solutions,  you can deploy the one most relevant to your specific business goals. 

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