In order to obtain truthful and accurate information free of any bias, it is preferred to analyze consumers through their online behavior, because when conducting traditional surveys, people tend to lie to give a good impression.
Although the most recommended is to analyze large volumes of data that are generated by consumers when interacting in the digital environment, surveys can work under specific conditions.
Thanks to the digitalization of writing and the rise of Internet searches and interactions, words have become very valuable inputs for statistical models and algorithms that allow inferring and reaching very precise conclusions about consumer behavior, among other uses.
Today, language has become the subject of big data analysis. Since the links used by Google are composed of words and the searches performed on that page are also composed of words, the study of text as data is becoming increasingly important.
Analyzing satellite photos to estimate the production capacity of an area or evaluating images of a product being sold on the streets to calculate its market-share are some of the business solutions that arise from the transformation of images into data.
Traditionally, when teachers or businessmen wanted data, they requested surveys. Data would be ordered, in the form of numbers or boxes checked on questionnaires.
Does it make sense to keep doing surveys to evaluate, for example, the ranking of a brand, when all the real, honest, and unbiased information can be inferred from people's behavior on the Internet?
"... Traditionally, when teachers or business people needed data, they commissioned surveys. They obtained data in an orderly fashion, either in figures or in boxes marked on questionnaires.
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.
The availability of data, a new generation of technology, and a cultural shift toward data-driven decision making continue to drive demand for big data and analytics technology and services.
Availability of information, new technologies and cultural change towards making decisions based on data is changing the way we do business.
According to estimates by the firm International Data Corporation (IDC), at the global level "...
Just as a company can not make decisions without information regarding the course of its business, no country is able to create a long term development plan without knowing its real needs in depth .
The last census to be published is the one from the Population Reference Bureau (PRB), but "... Some of the data provided are not complete for the country. "
The use of mass information on students´learning patterns can help schools to personalize education in increasingly sophisticated ways.
The collection, analysis and intelligent use of educational data can drive the profound changes that are needed for the education systems to generate skillful and talented people to create the innovations that will support economic growth in the long term.
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