In a situation where the market is becoming increasingly competitive, data analysis is no longer a reality reserved for large industries, but a process that is needed in all situations: in small, medium and large companies.
In fact, today no organization can ignore data: the goal is to improve the company’s productivity level, manage the entire production process in a more orderly and efficient manner, know and predict market trends, and improve their marketing strategies.
Increasingly successful companies in recent years have been basing their strategies on data, rather than relying on instinct, guesswork, or habit, and acting on what comes out of analysis.
However, the data itself does not matter, it is necessary to identify trends, correlations and relationships so that the information necessary for making the right decisions can be extracted from them.
Today, this is even more true: organizations have to deal with large amounts of data from multiple sources that are constantly updated.
In this scenario, it is unthinkable to be able to manipulate data and transform it into useful information without resorting to a platform that simplifies the entire analytical process and ensures the security, speed and reliability of the data being processed
Services such as data analytics consulting services be Dataart has been used in the business environment for several decades, but only in the last 10-15 years has this area undergone a significant and rapid evolution, becoming a real force for those companies that have decided to adopt a data analytics approach.
Technological development and advances in IT have actually led to the emergence of disciplines such as business intelligence and data visualization, which have greatly improved and simplified analysis and understanding through the introduction of data visualization tools.
Data analysis: what is it really
A brief definition of data analysis can be as follows: the collection and processing of large amounts of data to extract hidden information.
In practice, it is a science that combines mathematics, statistics and logic and uses the high computing power of modern computers to provide useful information to support decision-making and ensure more efficient and competitive management of companies.
However, the method of data analysis, contrary to what one might think, is not a methodology born along with the development of information technology in the 21st century, but dates back to the 18th century.
In fact, in 1785, William Playfair began using the bar chart to present data regarding food imports and exports in the United Kingdom and cotton in Europe.
However, in the early 19th century, the cartographer Charles Joseph Minard created a line chart to represent the course of Napoleon’s Russian campaign and to show how defeats were related to the cold of winter and the length of time the army had been away from the refueling line.
In 1890, Herman Hollerith invented the “tabulator” for storing US Census data on punched cards, which made it possible to analyze the data much faster.
However, there has been a real push in recent decades, thanks in part to advances in data science, artificial intelligence, machine learning, and big data.
How is the data analyzed?
Analyzing hundreds and hundreds of information per second throughout the day is a complex process that goes through several phases and can be done using a variety of methodologies and procedures.
There is no single answer to the question of how to analyze data. It depends on the context in which the data analysis is to be carried out, on the specific situation in the company and on the goals.
The latter play a fundamental role in proper data analysis: in fact, the first step that needs to be implemented for any company that wants to become truly data-driven should be the requirements collection process, that is, the definition of requirements and desires. Without this initial step, there is a risk of making the wrong choice, neither in terms of methodology nor in terms of tools and platforms.
Then comes the data collection phase, which is the moment of the actual “data collection”, which of course can be based on various technologies: database, data warehouse, ERP, files and repositories locally or in the cloud, etc.
In this way, “collected” datasets can be processed (data processing) and processed as they are, or, more often, a preliminary data cleaning and standardization phase (data cleaning) becomes necessary before the actual analysis of incorrect data.
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