The knowledge and experience of a person, together with the time they have, determine their way of doing analysis and, therefore, also the results.
In this context, machine learning drives a new way of using analytics in business strategy thanks to its algorithms that ensure precision and broaden the vision of the business.
Automate tasks for faster analysis
The machine learning is a subfield of artificial intelligence, rooted in statistical modeling . And while artificial intelligence is making its way into the business landscape through different types of software, from chatbots to security systems, machine learning supports the optimization of big data analytics .
Data by itself is not very valuable. They only have value when companies take advantage of them to obtain information in real time, anywhere and at any time. With analysis driven by machine learning, companies can:
- Automate tasks, such as discovery ones.
- Identify hidden patterns through intelligent data discovery and interactive exploration.
- Seize opportunities proactively, rather than waiting to react.
Machine learning algorithms speed up understanding, as they have the ability to “learn” over time from collected data . Analytics platforms powered by machine learning and natural language processing models eliminate the need for much of the initial technical work. They do this, for example, by automating prediction tasks and facilitating intelligent data discovery through interactive visual exploration. In this way, companies can extract information from their data in a much faster and more sophisticated way.
‘Machine learning’: objective analysis
The machine learning makes systems learn and improve after repeated exposure data, automatically and without being explicitly programmed to do so.
The result is “trained” models from data sets that no longer depend on the knowledge of human domain experts, nor do they need to be expressed as software rules.
Thus, the process is free from biased diagnoses and support for analysts increases . This is a job in greater detail and does not suffer from the limitations of traditional software development.
By inferring important relationships directly from the data, machine learning methods routinely outperform human. This happens even in new examples not seen during the training process. The result is that the company gains the ability to solve problems that traditional coding could not solve.
Instead of analyzing just enough data to support a hypothesis, machine learning makes an inductive discovery : it extracts as much information as possible from the data, without limitations.
The use of machine learning is becoming more widespread and is driving digital transformation, enabling companies to automate tasks and obtain information at an unprecedented speed . The machine learning is a key component of innovation that enables organizations to increase their ability to cope with new challenges .