To effectively apply data analysis tools on a large scale, the proper structuring of the information is essential, otherwise the cost that the company will have to incur to reverse the errors will be very high.
Data governance, which encompasses the set of processes, functions, policies, standards and measurements that ensure the effective and efficient use of information, becomes relevant to enterprises, which increasingly benefit from the use of machine learning tools and statistical analysis.
Technology and tools for analyzing large volumes of information used by large corporations to make business decisions are also available to businessmen and small companies.
Finding the best location for a new sales point, finding the areas where potential customers move and analyzing their purchasing power and their behavior as consumers are just some of the things that can be done today with the help of new technologies.
The ability to identify and understand hidden patterns and correlations in large volumes of data and use them to make business decisions is becoming a strategic competition for companies for the future.
Machine learning and statistical analysis are some of the most popular techniques used today in artificial intelligence (AI) applications. Automatic learning is the AI technique for identifying hidden patterns and correlations in a large amount of data that humans cannot identify on their own, explains Brian Ka Chan, technology strategist and researcher at Smart City.
The business models operating under the concept of a subscription, such as Netflix, Amazon or even PriceSmart, can have great advantages over their competitors if they manage to properly manage the large volumes of data they handle.
The payment of subscriptions is not only for modern online services, as retail companies apply it in the region, as is the case of the supermarket chain PriceSmart.
Having the required resources to manage the data needed to make decisions is crucial to the success of businesses in today's environment.
Today's data savvy organizations, those with a top-down approach to decision making, do a better job of extracting value from the data, explains a Coursera publication.
Using information without defined objectives and not integrating it across the entire company are part of the mistakes that organizations can make when analyzing large volumes of data.
Although there are still many companies that have not begun to analyze the information they accumulate in their operation, there is a risk that the efforts they make in the future will not achieve the expected results if the mistakes that some organizations tend to make in the process are not avoided.
With the boom in data mining and the use of algorithms to make recommendations to customers, companies are beginning to face the decision of whether to provide "human" assistance or through bots.
At a global level, several companies have opted to develop their data analysis departments, with the aim of finding suitable information to develop models that automatically make recommendations to their customers.
In Costa Rica, the Ministry of Finance is using a predictive model designed with data mining techniques to determine the behavioral patterns of companies that might be circumventing tax payments.
Analyzing and crossing checking historical information from multiple databases, the statistical model used by the Directorate General of Taxation attempts to predict which companies are more likely to evade paying taxes depending on their historical behavior measured through transactions, tax returns and other data.By linking all of the information, they identify patterns of behavior similar to those of other companies that have evaded taxes in the past.
Recognized Brazilian company of backhoe loaders, telescopic, articulated and other types of cranes looking for companies interested in representing the brand and distributing their machinery in Central America and Mexico. The company manufactures and sells telescopic,...
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