The advancement of Artificial Intelligence has created increasingly attractive opportunities for managers, including improved data analysis and decision-making processes. More specifically, the emergence of new AI approaches (such as Machine Learning) is directly impacting Business Intelligence.

Today, the combination of these two technologies is the key to optimizing routine processes, modernizing operations, and preparing the organization to consume and handle a large volume of data. Want to understand how this happens? Just keep reading!

What is the importance of using decision support technologies?

One of the most recurrent issues in the business world today is the ability to make better data-driven decisions rather than relying purely on intuition, which about 38 percent of companies still do, according to an MIT poll.

This is presented as a great evolution for management thanks to the support of the tools that have appeared in the market. But, why is it important to be guided by these support technologies? First, because a decision that is based on a large amount of information presupposes a prior analysis of risks.

When the collected data is processed by analytical systems, managers are able to quantify everything and understand what is happening in numerical and graphical terms. When choosing an action A or B, for example, you can count on forecasts that point to which option will maximize success, and what the risks of each of them not working are. That in itself is already a great advantage as it assures safety to leaders.

By filtering this information, the visualization of what is relevant according to the objectives and goals is ensured, as well as contemplating scenarios of several sectors at once is made possible in an integrated and holistic way. In addition, with the support of these technologies, the choices can be made in real time, solving the problems before they grow.

It is in this context that two technologies are being used together to optimize results —  Machine Learning and Business Intelligence.

What is the relationship between Machine Learning and Business Intelligence?

Also called BI, Business Intelligence is the ancient process of collecting and transforming raw data into valuable business insights to support management decisions.

Basically, it is concerned with describing the state of a company through charts, reports, and performance indicators. This function, however, gained new contours and meanings with the evolution of the computational systems and the beginning of the discussion about the great volumes of data, called Big Data.

Machine Learning (ML), in turn, is a field that seeks to make machines smarter from their own learning ability. It came up with the evolution of concepts from many areas such as data science, statistics, and even BI itself.

Both are already used by companies to generate understanding and communication by identifying patterns and trends in their data. But their goals differ — while BI focuses on describing the state of the business and understanding what has already happened, Machine Learning has been used to predict the company future, that is, to identify what will happen.

In many scenarios, the information output from analytical tools is used as input to ML algorithms. But their relationship is even deeper — modern demands are creating the need for BI applications that incorporate Machine Learning.

What are the advantages of this union?

Now that the operation of these tools has become clear, let’s take a closer look at the key benefits of using them in an integrated way.

Smart Dashboards

One of the first benefits of using Machine Learning for business data analysis is the ability to create smarter platforms. After all, it allows you to automate some common panel actions to extract and uncover insights that could go unnoticed by humans.

A simple chart, for example, may show that the results are satisfactory in your company, although there might be some concerning factor too small to be displayed in this form of visualization. Using Machine Learning algorithms, however, it is possible to identify these small anomalies and signal that something is wrong. It may not be something big, but it already works as an alert for the future.

Predictive Analyses

Another benefit, as we have said, is the possibility of making predictive analyses. Roughly, it is a prediction of what will happen in the near future based on the identification of standards in the historical data of the company. Due to the use of ML algorithms that learn to map expected outputs to defined input sets, it is possible to prepare actions in advance and avoid future losses.

In the case of financial fraud, for example, the algorithm receives as input the behavior of consumers who were classified as malicious. Based on this, the system is able to predict and classify other users according to the risk they may represent.

Another example is the early identification of productive bottlenecks that can disrupt operations and undermine internal agility.

Behavior analysis

Another application worth mentioning is analyzing customer behavior to better understand their profile and segment actions. In this way, management knows exactly what the consumer will need based on their previous habits and preferences, and can direct services and care more accurately.

Mobility

We know that speed in decisions is a factor of great concern today. According to another MIT study, about 81 percent of today’s leaders see time as a challenge in the future of data analysis. That’s why Machine Learning is also useful in this regard.

It greatly optimizes mobility, since there are natural language processing applications to facilitate the analysis of reports and charts in dashboards. With voice commands, managers have access to all the important information in real time. And this control is made by smartphones, which accompany the professional wherever he or she goes, reducing the limitations of space and time.

What are the risks of not investing in process improvement?

Given all the dynamism of the current market, one of the risks of neglecting this type of update is precisely the fact that the company loses competitive advantage.

As a business cannot generate value for customers like other companies in the market, it ends up losing more and more space. Not to mention that not investing in this process improvement can also be detrimental to internal operations as it facilitates the creation of productive bottlenecks that affect agility.

Finally, we can see that Business Intelligence gains more robustness and agility when combined with Artificial Intelligence tools. Both technologies are already related and, in the modern context, can still be integrated to optimize management with speed and providing a broader view.

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