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Data-Driven, ML And AI Models From A Business Perspective

What Are The New Technologies At The Service Of Business?

The digital revolution is driving profound changes in business in a competitive global economy. A key driver of business model transformation is data science, which relies on the combined use of Machine Learning (ML), Artificial Intelligence (AI), mathematics, statistics, databases, and other techniques.

These new technologies have immense potential and allow enterprises to process and analyze huge volumes of data quickly and accurately, automating decision-making processes and revealing insights that can be used to gain a competitive advantage. The factors that today favor the use of Data Science techniques in many different sectors revolve around four main themes :

  1. The unprecedented increase in both the volume and type of data available, thanks also to the developments of the Internet of Things (IoT);
  2. Connectivity and access to data, leading to the development of new services and business models based on real-time generation and analysis;
  3. The continuous improvements in the algorithms that allow to optimize the processing of large volumes of data;
  4. The increased computing power of systems is due to advances in processor technology, developments in programming languages, and the emergence of cloud computing.

Machine Learning: What Is It, And What Is It For?

Equal and even surpass human information processing capabilities to solve complex problems and make data-driven business decisions by analyzing massive amounts of data. We could thus summarize Machine Learning, “automatic learning” – a subcategory of Artificial Intelligence that deals with creating systems that improve performance based on the data they are exposed to over time. Its operation is quite complex and is based on a process by which computers develop pattern recognition in data and become able to make predictions without having been programmed with explicit instructions. 

This has great potential in several business applications, such as fraud detection, customer segmentation or marketing campaigns. Machine learning techniques represent an important transformation lever and a step forward compared to classical statistical techniques, in the sense that they increase the predictive power, improve the data analysis and interpretation process, as well as the decision-making process, thus providing a more accurate representation of reality, and make more efficient use of resources. Machine learning tasks are generally classified into three broad categories called paradigms :

  1. Supervised learning: The data scientist provides the algorithm with labeled data to train it, i.e. inputs paired with outputs. The goal is that, based on this training, the system can make generalizations applicable to other similar datasets;
  2. Unsupervised learning – The algorithm has unlabelled data and needs to identify hidden patterns. It is based on the principle of similarity;
  3. Reinforcement learning: the model interacts with the environment and must optimize the results of its actions according to the rewards it receives.

Today the application of Machine Learning algorithms in a business context is increasingly widespread. This technology will likely be used across various industries to improve decision-making processes and achieve better performance, cost, and customer experience optimization.

The Impact Of Machine Learning On Business Intelligence

We live in the age of digital technology, and data is a source of value that organizations can use to improve business performance and stay competitive. Their volume has increased exponentially due to the digitization of processes and the growing interaction with customers, employees and suppliers through digital channels.

Artificial Intelligence and machine learning are taking place in Business Intelligence (BI) processes thanks to their ability to process huge amounts of data and draw significant insights from them. Companies are already harnessing the power of machine learning to understand consumer behavior better, improve customer service, optimize marketing campaigns, and more.

Machine Learning can give Business Intelligence an edge by providing access to greater volumes of data, guaranteeing more accurate and reliable insights, faster results and, another aspect to consider, finding anomalies in real time to act immediately and guarantee the best result. Also, thanks to predictive analytics, machine learning algorithms can make predictions based on historical data that help organizations create scenarios and make better decisions without wasting time. Ultimately, companies should consider integrating ML into their BI processes to gain insights, improve decision-making, optimize resources, and boost productivity.

Business Intelligence: What Is It, And How Does It Work?

Business Intelligence is a set of strategies and technologies companies use to analyze data and transform it into useful information to make strategic and tactical decisions. For example, a company that wants to manage its supply chain better needs BI capabilities to determine where delays occur and how to reduce them. The BI system, thanks also to the use of tools such as the Data Warehouse, collects data from multiple sources, executes queries and generates reports that can be used to make more informed decisions and improve operational efficiency.

In today’s digital environment, BI capabilities are essential for any organization that wants to stay ahead of the competition and take advantage of digital transformation opportunities by providing insights that would not be possible with traditional methods. The benefits offered by Business Intelligence are many and varied, but they all revolve around optimizing processes, improving the decision-making process and increasing productivity.

In addition to data-driven decision-making, faster analytics, intuitive dashboards, and increased organizational efficiency, BI improves employee satisfaction by fostering a corporate culture where data is at the heart of all decision-making, helps companies to monitor the evolution of the market and to anticipate the needs of customers who in this way receive a better service.

Business Intelligence, in conjunction with machine Learning and Artificial intelligence models, can give companies a greater boost by offering insights that can be used to create a digital transformation strategy aimed at optimizing processes and creating value for customers.

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