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Algorithms and Analytical Prediction

If we refer to our finances or businesses, we always want to have a prediction of what may happen to us in the future, not only to be prepared and have room for movement but also to anticipate and be able to lead in our sector.

Algorithms play a very important role in helping us predict this future.


In business, where the ability to predict what lies ahead continues to differentiate leaders from the rest. Algorithms are especially important, but this is not easy to achieve and is usually quite expensive.


Prediction has traditionally been a manual process with people collecting, compiling, and managing data, often within spreadsheets. However, organizations are changing these processes, involving people with analytical prediction systems. All this is possible thanks to new technologies such as advanced analysis platforms, artificial intelligence and machine learning ...


But what exactly is algorithmic prediction?

Algorithmic prediction uses statistical models to describe what is likely to happen in the future. It is a process that relies on warehouses of historical and market data, statistical algorithms chosen by scientists with modern computing capabilities that make data collection, storage and analysis fast and affordable.

The data then also plays an important role, having a good compilation of the data is the key.


Prediction systems offer more value when they can account for errors, handle anomalies in the data, and correct them themselves. That's where machine learning comes into play. Over time, forecast accuracy improves as algorithms "learn" from previous cycles.

That is, having good data makes it easier to have more effective algorithms and in turn these improve through themselves, the new algorithms learn and improve from the previous ones.


These systems are more relevant when they are based on higher value data. In some cases, this could involve the use of natural language processing, which can read millions of documents and send them directly to algorithms.


Thanks to algorithmic prediction, financial organizations, for example, are already using automation tools to streamline tasks such as transaction processing. This makes work easier and improves results.


But the real push for algorithmic prediction comes when it's combined with human intelligence. It is this symbiotic relationship that makes algorithmic prediction effective.


The advantages are several and in different areas, such as being able to make smarter and more effective decisions, and in turn, they force companies and organizations to have talents specialized in these new technologies. Once again, we can say that technology depends on us to improve and be totally effective.


In the end, we can say that the impact of analytical prediction is not limited only to financial operations, but also affects other functions, from marketing to the supply chain as we have seen previously in our post on Machine Learning.





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