Discover 4 advantages of applying machine learning in your company

A culture data driven, that is, with management based on data orientation, ensures competitive advantage, faster decision-making, and revisions of previously defined strategies. In this way, the machine learning – a subset of Artificial Intelligence that allows systems to learn from data, identifying patterns and making predictions without relying on pre-programmed rules – is one of the tools that contributes to the entire process. 

Douglas Costa, CTO of Grupo Deal, a technology services consultancy, states that machine learning has become indispensable for companies. The projections support his thesis: Gartner pointed out that 75% of companies will invest in some form of machine learning by 2025. 

“Machine learning algorithms evolve as they are exposed to more data. Today, it is already widely used in sectors such as e-commerce, finance, fraud detection, as well as optimizing production chains and providing personalized recommendations across various activities,” notes Douglas. He also explains that machine learning processes data on a large scale and at high speed, generating insights that were previously impossible to achieve manually. “By leveraging data, companies can increase efficiency, improve customer experience, and drive innovation,” he comments.

To emphasize the importance of machine learning, the expert highlights 4 advantages for businesses:  

1. Automates processes by reducing repetitive and manual tasks: “Teams gain time to focus on more strategic activities that require intellectual efforts,” evaluates Douglas. 
2. Accurate predictions: machine learning data anticipates market trends, future demands, and consumer behaviors. 
3. Data-driven decisions: “it becomes feasible to convert raw data into insightsactionable insights, which support more effective and agile decisions.” 
4. Personalized experience: with machine learning, it is possible to offer a more relevant and individualized customer journey.” 

The executive makes a caveat: “The models are only as good as the data they are fed. Incomplete or biased information can lead to inaccurate or harmful results. Therefore, it is necessary to approach with care to ensure integrity and security, with solutions that validate data quality and protect systems against digital threats,” concludes the CTO.