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DIY: BI in Machine learning

The Role of Business Intelligence in Machine Learning and AI

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“In this article, we want to delve deeper into the world of automated decision-making. Specifically, we will focus on emerging technologies such as Machine Learning (ML) and Artificial Intelligence (AI). In this rapidly evolving context, Business Intelligence plays a crucial and significant role.

Big Data, thanks to advanced algorithms, enables computers to make decisions quickly and effectively in our favor.

Once these conditions are sufficiently developed, a computer can even draw better conclusions than a human. In the medical field, we already see progress where software can make better diagnoses than the most experienced doctors.

Is this frightening? We don’t think so. In the past, we traveled through the streets in horse-drawn carriages, but soon, we will be able to go to work in self-driving cars while peacefully reading a book, without having to steer ourselves. This development is inevitable and unstoppable.

Today, a lab technician still manually assesses a blood sample to detect anomalies.

Soon, a computer can take over this task and, with sufficient data, history, rules, and scientific knowledge, make better judgments than any human.

Let’s take it a step further and consider that in the future, software will be written by other software, and robots will be manufactured by robots. These processes will likely be more efficient and intelligent than what humans can achieve.

Why shouldn’t we make use of the most optimal predictions and decisions made for us? What is essential is that we understand how they come about.

Of course, there will come a point where these decisions become too complex for us to understand. However, think about how many people still know how to drive a horse-drawn carriage.

Machine learning (ML) or artificial intelligence (AI)

What does Machine Learning mean? In an excellent explanation by an expert from Oracle Netherlands, we gain a better understanding:

Controlled self-learning

Machine Learning allows computers to become controlled “self-learning” by providing them with vast amounts of data and instructing them about the outcomes we want to achieve.

Imagine letting the computer look at a blood sample and detect signs of kidney failure. We guide the computer to the same conclusion.

Once this controlled phase has generated enough rules, the computer can function independently. It can analyze the same blood sample and draw conclusions that go beyond just kidney failure.

Future perspective

We agree with Dr. Asimov’s standpoint. He suggests that we should always instruct robots and computers to leave humans alone.

He means that it’s not wise to let machines operate entirely autonomously. We should not let machines develop self-improving robots and software. At some point, they may come to the conclusion that humanity is a threat to the planet and needs to be eliminated.

But if we impose restrictive conditions on machines and software to protect us, they will acquire human characteristics, and we can prevent them from starting wars.

And now?

Let’s apply this to the present. Imagine having a dashboard in Oracle Business Intelligence that warns us of exceptions, such as unreceived purchase orders.

If employees consistently respond by contacting suppliers, can’t we automate this process?

Within Oracle BI, we call this ‘automatic decision-making.’ The rules, logic, and actions we apply based on provided information can be automated.

Compare it to a thermostat in your home: it can independently determine when to turn on the heater to maintain the desired temperature. If we extrapolate this concept to the future and provide the thermostat with centuries of historical weather data and predictive power, it can even anticipate the upcoming days’ weather.

Is this frightening? No, on the contrary. Artificial Intelligence should enrich our lives with devices that can make faster and more accurate decisions than we can.

Recently, I heard a prediction that self-driving cars could reduce the number of traffic accidents to zero in the near future.

The significant role of Business Intelligence

Now that we’ve established a solid foundation on the rise of Machine Learning and Artificial Intelligence, let’s explore the significant role of Business Intelligence (BI) in all of this.

BI is the glue that connects the world of data to business decision-making. It provides insights and analyses that help companies make informed decisions and achieve strategic goals.

Data as fuel

In the world of Machine Learning and AI, data is the crucial fuel. Without sufficient high-quality data, algorithms and models cannot function.

This is where Business Intelligence plays a key role. It collects, processes, and transforms data into actionable information for decision-making.

Identifying patterns

One of the fundamental aspects of Machine Learning is identifying patterns in data. BI tools enable companies to analyze historical data and discover these patterns.

These insights can then be used to make predictions and support decisions.

Data visualization within Oracle BI

Another important aspect of Business Intelligence is data visualization. Translating complex data into charts, graphs, and dashboards makes it easier for decision-makers to quickly understand information and take action.

This is essential for both Machine Learning and AI, where understandable output is crucial.

Real-time information

In the world of Machine Learning and AI, real-time information is invaluable. BI systems can collect and analyze data in real-time. This allows companies to respond more quickly to changing circumstances and opportunities.

Automating decision-making

An intriguing possibility is automating decision-making using Machine Learning models.

Imagine a company automatically adjusting prices based on supply and demand, or a customer service team supported by an AI system that generates answers to frequently asked questions. This kind of automation can increase efficiency and save costs.

Predictive analysis

BI and Machine Learning can collaborate to perform predictive analysis. By using historical data, companies can make predictions about future trends, customer behavior, and market developments. This is invaluable for strategic planning.

Business intelligence in practice

Let’s take a look at some practical applications of Business Intelligence in combination with Machine Learning and AI:

Customer service

Companies can use AI chatbots to answer customer questions and resolve issues. These chatbots use Machine Learning to learn from interactions and become smarter at providing support.


Machine Learning can assist in segmenting customers and personalizing marketing campaigns. By understanding which offers and messages are most effective, companies can optimize their marketing efforts.

Fraud detection

Financial institutions use Machine Learning algorithms to identify suspicious transactions and prevent fraud.

These systems continuously analyze large volumes of transaction data and can recognize suspicious patterns.

Supply chain management

Companies can use Machine Learning to optimize their supply chain. Predictive models can help in forecasting demand fluctuations and maintaining inventory levels.


In the medical field, Machine Learning can assist in making faster and more accurate diagnoses.

Using advanced algorithms, medical imaging can be analyzed, and potential health issues can be identified.

Financial analysis

For investment funds and financial analysts, the use of Machine Learning has become indispensable.

This technology can analyze large datasets and identify trends that would otherwise be difficult to discover.

Challenges and considerations of Machine Learning

While the integration of Business Intelligence, Machine Learning, and AI is promising, there are also some challenges and considerations to keep in mind:

Data quality: Prevent incomplete data

The quality of data used for Machine Learning is crucial. Incorrect or incomplete data can lead to incorrect conclusions. Ensuring data quality is essential.

Prioritize AI, privacy, and ethics

The use of AI and Machine Learning raises ethical and privacy concerns. How data is collected and used, and its impact on individuals’ privacy, must be taken seriously and adequately addressed.

Ensure human involvement

While automation and AI can provide efficiency, human involvement is still necessary, especially in complex decisions and situations involving ethical considerations.

Training and skills

Organizations need to invest in training and developing

their staff to acquire the necessary skills for Machine Learning and AI. This includes understanding algorithms, data analysis, and ethical considerations.


The convergence of Business Intelligence, Machine Learning, and Artificial Intelligence offers businesses unprecedented opportunities. It allows them to turn data into valuable insights, leverage automation for efficiency, and support better decision-making. While there are challenges, the future of these technologies is promising.

It is essential for organizations to be aware of the opportunities and risks associated with these technologies and carefully plan how they want to implement them. With the right approach, companies can benefit from the advantages of Machine Learning and AI while keeping ethical and human aspects in mind.

So, is the rise of Machine Learning and AI frightening? No, as long as we manage and deploy these technologies with understanding and responsibility, they can significantly enhance our society and businesses. The future is promising, and Business Intelligence will play a key role in shaping that future.”

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