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Differences Between Business Intelligence and Machine Learning

I do not think there is a common and unique position in the world of data about the difference between one and the other. 

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What is the difference between Business Intelligence and Machine Learning?

I do not think there is a common and unique position in the world of data about the difference between one and the other.

Let’s start first 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 into a structured database. These structured databases are usually called data warehouse.

Business analysts use data visualization techniques to explore data stored in structured databases. With this type of tools, they create visual panels (or dashboards) to make information accessible to business profiles that are not data specialists. The panels help to analyze and understand the results in the past and serve to adapt the future strategy that improves the KPIs (key business indicators).

The traditional Business Intelligence allows to have a descriptive vision of the activity of the company, very visual and based on data. It mainly uses aggregate data to describe future trends.

And what is the difference with Machine Learning?

At first it might seem that there’s no difference at all, since Machine Learning also uses the data to work, it uses ETL tools to access them and its main purpose is to improve the business objectives of the companies.

The mechanism by which it does so is the detection of patterns in millions of data. This is a first important difference compared to traditional BI, to which we could add, to our way of understanding, these three aspects:

Faced with the use of aggregate data, Machine Learning uses individual data with defining characteristics of each of the instances. In this way, thousands of variables can be used to detect the patterns.

Instead of relying on descriptive analytics, Machine Learning offers predictive analytics. That is, it not only makes an assessment of what has happened and extrapolates general trends, but makes individualized predictions in which the details and nuances define the behaviors of the future.

Display panels or dashboards are replaced by predictive applications. We are talking about one of the greatest potentials of Machine Learning: predictive algorithms automatically learn from the data and their models can be integrated into applications to provide them with predictive capabilities. The models are re-trained periodically so that they automatically learn new data.

An example

Imagine a scenario in which an ecommerce analyzes the behavior of its customers in the store. One of the objectives is to know in advance and with the greatest detail, how many clients are going to be removed from the system next month, since that is an important KPI for the business.

A Business Intelligence-based approach would use what happened in previous months or years along with other global variables such as the market trend or the number of customers in the current date with respect to other years. With these data, visual loaves of trends would be created that would inform of the expected percentage of clients that will be retired.

The development of predictive applications is one of the outstanding powers of Machine Learning, since they facilitate the automation of processes, the decision making and the continuous learning based on data.

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

Instead, an approach based on Machine Learning would use the complete database of clients, profiles, purchases and withdrawals to look for patterns of behavior and determine which of them were signaling that they would be discharged the following month:

  • The data to be used would be the details of the purchases of all customers, their personal data (age, sex, age …), product data (SKU database, categorizations, prices), promotions data, campaigns marketing … along with a final field that would indicate, for each client, if it has been unsubscribed.
  • Faced with trend analysis and global Business Intelligence, Machine Learning makes client-to-client predictions. In this example, a BI system would tell us what percentage of customers are going to unsubscribe. One of Machine Learning would tell us individually, for each client. Based on this information, the business can make personalized actions to prevent customer leakage.
  • With Machine Learning you can create real-time applications that are integrated into the reservation system to provide information about the probability that the client will leave. In addition, you can create an automatic system that sends for example email campaigns with personalized offers to those customers who are at risk.

Conclusions

Business Intelligence offers a useful approach that describes what happened in the past, allows to understand data to non-specialized business roles in analytics using powerful visualizations and serves to make decisions based on global trends.

Machine Learning, on the other hand, is a technique that allows detecting “low level” patterns in thousands of individual data. The development of predictive applications is one of the outstanding powers, since they facilitate the automation of processes, the decision making and the continuous learning based on data. In addition, these are systems that learn automatically over time, are integrated into the company’s developments and adapt to changes in the environment when they are constantly fed with new data.

Hope this post has been interesting for you and looking forward to your comments and recommendations!

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