Artificial intelligence is driving a revolution in today’s world. Emerging technologies such as machine learning, natural language processing, and advanced robotics are redefining the boundaries of efficiency and creativity in the business world.
In the masterclass given at the UEMC Business School, Jorge Crespo, coordinator of the Official Master’s in Big Data, explored various facets of artificial intelligence (AI) and its impact on the digital transformation of companies.
AI as a pillar of transformation
Artificial intelligence is defined as a set of technologies that enable machines to perform tasks that normally require human intelligence, such as speech recognition, decision-making, and language translation. There are several branches of AI-powered data analytics:
- Descriptive Analysis: Answers the question “What happened?” by providing a retrospective understanding of the data.
- Diagnostic Analysis: It seeks to understand “Why did it happen?” by analyzing the underlying causes of the observed events.
- Predictive Analytics: It focuses on “What will happen?” using statistical models and machine learning algorithms to predict future outcomes.
- Prescriptive Analytics: Seeks to answer “How can we make it happen?” by recommending actions based on predictions.
- Cognitive Analysis: Aims to improve or replace human reasoning.
Machine Learning: The Heart of AI
Machine learning is the field of study that gives computers the ability to learn from data without being explicitly programmed to do so. During the training process of machine learning models, a cost function is optimized, which varies depending on the algorithm used.
AI application development
AI application components
To develop effective AI applications, several essential components must be considered:
- Data Acquisition: The application must be able to acquire data regardless of its source. A key aspect of acquisition is integrating and combining data irrespective of its type (structured or unstructured), the rate at which it is generated, or its accuracy.
- Data organization: Data should be organized for easier handling, using, for example, LDW Analytical Architecture to connect to data as needed and collect it efficiently, physical data servers, etc.
- Analysis: Data should be analyzed when and where it makes the most sense. This can include creating reports, running tactical and ad hoc queries, visualizing data, using machine learning, and more.
- Delivery: of Information and Data at the optimal time to help human activity to make decisions in real time, embed the analyses within the business processes that are capable of taking some action or analyzing the data as it is sent to the company and automatically take action based on the results.
AI application development
Developing AI applications requires a multidisciplinary team that includes:
- Business analysts
- Expert in the field
- Data Scientist
- Data Engineer
- Software Engineer
- Machine Learning Model Architect
- Application developers
Is it better to develop or buy an AI application?
Only 5% of all the code in an Analytics Application is new Machine Learning code. The remaining 95% is reused code. This has always raised the question of whether it’s better to develop an Analytics Application in-house from scratch or to purchase an application and focus efforts on integrating it into the institution’s application ecosystem.
Data management within the organization is a critical point for the proper functioning of the institution and is considered the most important driver of business value.
Once the initial steps are taken, most companies choose to increase the number of use cases gradually. Maintaining 10 to 20 use cases generally results in a positive balance for the company, but beyond a certain point, the marginal value of the next use case is less than the marginal cost.
In conclusion, artificial intelligence has the potential to digitally transform the business landscape, highlighting both the technical and strategic components needed to implement successful AI solutions.
If you’re interested in specializing in Artificial Intelligence, our Official Master’s Degree in Big Data Management and Analysis will provide you with a comprehensive overview of data analytics, covering everything from data collection to visualization, including the necessary infrastructure. Our methodology is entirely practical, based on real-world case studies and specific projects.
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