Business Intelligence and Machine Learning differences

by Andrés González

It’s common for people to ask us what is the difference  between Business Intelligence and Machine Learning. I also asked myself that question when I started in this exciting world of data-based predictions.

I do not believe that there is a single common position in the world of data on the difference between both. In this article we will just give our point of view based on our experience, which we are sure it can be complemented and enriched with other professionals and specialists opinions.

Let us begin by understanding what the objective of each area is.

What is Business Intelligence used for?

The first step in any type of Business Intelligence is to collect raw data. Once stored, data engineers use what are called ETL (Extract, Transform and Load) tools to manipulate, transform and classify data in a structured database. These structured databases are usually called data warehouses.

Business analysts use data visualization techniques to explore data stored in structured databases. With this type of tool they create visual panels (or dashboards) to make information accessible to non-data specialists. The panels help to analyze and understand past performance and is used to adapt future strategy to improve KPIs (Key Business Indicators).

 

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In short, traditional Business Intelligence allows us to have a descriptive vision of the company’s activity, very visual and based on data. It mainly uses aggregated data to describe future trends.

And what is the difference with Machine Learning?

The mechanism that does this detects patterns in millions of data. This is an important first difference from traditional BI, to which we could add these three aspects:

  1. In contrast to the use of aggregated data, Machine Learning uses individual data with defining characteristics of each of the instances. This way, thousands of variables can be used to detect patterns.
  2. Instead of being based on descriptive analytics, Machine Learning offers predictive analytics. In other words, it not only makes an assessment of what has happened and extrapolates general trends, it also makes individualized predictions in which details and nuances define future behavior.
  3. Visualization panels or dashboards are replaced by predictive applications. We are talking about one of the greatest potentials of Machine Learning: predictive algorithms learn automatically from data and their models can be integrated into applications to provide them with predictive capabilities. Models are retrained periodically to learn automatically from new data.

An example

Let’s imagine a scenario in which an ecommerce makes an analysis of the behavior of its customers in the store. One of the objectives is to know in advance and in the greatest detail, how many customers will churn next month, as this is an important KPI for the business.

A Business Intelligence-based approach would work with previous months or years along with other global variables such as market trends or the number of customers at the current time compared to other years. With this data, visual trend sheets would be created in a way that would inform the expected percentage of customers that are going to churn.

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Based on this information, the ecommerce management team can make business decisions, such as targeting marketing campaigns to specific sectors of the population.

Instead, the approach based on Machine Learning would use the full database of customers, profiles, purchases and casualties to look for patterns of behavior and determine which of them were giving signs that they were going to churn next month:

  1. The data to be used would be the details of the historical purchases of all customers, their personal data (age, sex, seniority…), the data of the products (SKU, categorizations, prices), data of promotions, marketing campaigns… along with a final field that would indicate, for each customer, if he or she has churned.
  2. In front of global business intelligence and trend analysis, Machine Learning makes client-by-client predictions. In this example, a BI system would tell us what percentage of customers are going to churn. A Machine Learning one would tell us this information individually, for each client. Based on this information, the business can take customized actions to prevent customer churn.
  3. Machine Learning can be used to create real-time applications that can be integrated into the booking system to provide information about the likelihood of the customer leaving. In addition, an automatic system can be created to send email campaigns with personalized offers to those customers who are at risk, for example.

Conclusions

Business Intelligence offers a useful approach that describes what happened in the past, enables data to be understood in business roles not specialized in analytics using powerful visualizations and serves to make decisions based on global trends.

Machine Learning, on the other hand, is a technique that can detect patterns «at a low level» in thousands of individual data. The development of predictive applications is one of the most important strengths, as they facilitate process automation, decision making and continuous learning based on data. In addition, they are systems that learn automatically over time, integrate into company developments and adapt to changing environments when constantly fed with new data.