Machine learning - or machine learning- is the branch of artificial intelligence that works with algorithms that are improved through experience, In other words, iteratively learn from data.
Machine learning systems are used to create predictive models based on continuous inputs that are used to anticipate, predict and make decisions.
Machine learning models learn from data and can be adjusted to produce better results. The more data they have, the faster they will learn and the more accurate their results will be. It is continuous improvement, applied to knowledge.
Machine learning and data management: a great possibility
Managing an organization's data is a growing challenge for companies. Despite this, the answer to this challenge does not lie in focusing on business processes and systems, it has to do with innovation.
Resorting to machine learning by training an algorithm and achieving a predictive model is the way to transform difficulty into possibility and turn inconveniences into benefits such as the following:
- A growing volume of data: If complex data management, heterogeneous and fast in a big data environment is beyond human capabilities, the same is not the case with machine learning. Take advantage of all those zetabytes of information and take advantage of the hundreds of millions of IoT sensors that are connected today to learn and help create a smarter system.
- A series of business users that continues to grow: Even though it poses a security challenge for companies that must scrupulously take care of endpoint management, is extremely effective in preventing the algorithm from continuously learning.
- New habits: migrations, transformations, data integration or advanced analytical processes are not exceptional circumstances in any organization; rather, are patterns that repeat themselves more and more, as business users opt for experimentation and institutions empower them to do so, providing them with the right tools. Machine learning takes advantage of all of these tickets to continue learning and giving the system new perspectives, a more complete vision and a deeper knowledge of each data.
The use of machine learning for data management is an extraordinary possibility to move towards an information-based leadership model, that drives the organization towards success in each of its disruptive initiatives. At the same time, will allow you to find answers to all those questions that you could never have answered, due to budgetary constraints or simply because it was not humanly feasible.
Download the guide here “How to implement a Data Driven culture in my company” and discover everything you want to know.
Machine learning: 4 benefits for your business
Is data your priority? Is your organization ready to unleash the potential of every bit of information? It is essential to pay attention that the results of any digital initiative can only be as good as the quality of the data from which it is executed..
At the same time to implement quality software to ensure adequate standards, The decision to go with machine learning for data management has many benefits for business users. As an example:
- Increased data delivery speed for critical business initiatives.
- Increased productivity and efficiency of processes.
- Better adequacy of recommendations, when the predictive model is combined with metadata visibility across the company.
- Reduced latencies by automating many data management tasks.
Artificial intelligence in general – Y, for this case, machine learning in particular – opens up a world of possibilities previously unthinkable for human intelligence: we see it in medical diagnostics, in massive facial accreditation analysis, and even now with the monitoring of the contagion of COVID-19.
Companies are already developing this type of project to take advantage of every bit of their data and thus solve strategic issues, identify large-scale patterns and predict scenarios, among many other uses that, before as a matter of time, cost and space, they were unable to complete.
