The use of data analytics, without a doubt, is one of the most valuable resources to transform a company. Data analysis has often helped management make the right decisions to prosper a business.
Despite this, the same survey reveals that only 28% of businesses can transform this data into relevant information for the company, showing a low knowledge level about data analytics.
Some of the reasons that make data analytics so important for a company are:
- Performance evaluation of advertising campaigns;
- Product development;
- Internal process improvements ;
- Creation of strategies;
- Benchmarking ;
- Market monitoring.
The study and correct visualization of data allow the company to prosper by making more logical decisions, which avoids taking actions that could harm the business.
Thus, the company must dedicate resources to performing data analytics through specialized software and, if necessary, do so with the help of professionals in the area, such as data scientists.
How Does Data Analytics Work?
You may have already understood what data analytics is, but how it works in practice and how to apply it may still need to be discovered. In general, data analysis is performed by specialized software for this function. Some automatically generate reports, organize data, and present graphs.
Data analytics takes place from data categorization and studies and, finally, the presentation of the interpretation obtained.
However, it is known that machines can still be flawed when ignoring, for example, specific contextual and cultural issues.
Therefore, today there is a professional called a data scientist, prepared to gather, interpret and solve problems from relevant information extracted from data analyzed by companies.
The interpretation of this data will help the company to make more strategic decisions based on facts and evidence, which certainly helps to maximize profits.
Data analytics can range from simplicity to complexity, depending on the amount of data being processed; therefore, it will only sometimes be necessary for a professional to interpret this information.
For example, suppose data were provided from a survey about the consumer profile of a product. In that case, a marketing professional can analyze and interpret them.
The data analytics process is one of the most relevant in Business Intelligence (BI) and improves organizational performance.
How Can Data Analytics Be Used In The HR/DP Sector?
The Human Resources and Personnel Department sectors must be strategic. This means that decision-making based on impressions or speculation should be avoided.
The Human Resources sector is one of the most strategic of the entire company, as it needs to work with data and information to enable the business’s success. To help with this task, it is possible to create an HR report, a document that helps develop strategies for the company.
The HR report is a tool that should be part of the company’s routine, but it is necessary to understand what it is and what is the best way to create it without requiring excessive time from employees.
Therefore, professionals in these areas must work from data analysis to seek solutions to problems under their responsibility, such as controlling turnover rates and absenteeism among employees.
In practice, if professionals in these sectors can obtain data on employee delays, absences, and benefit payments and, based on this information, generate reports and graphs, it becomes easier to identify problems and find a solution.
Therefore, ensure that these sectors are supported by modern software that helps them visualize and analyze essential company data.
Thus, it became evident how data analysis is a decisive factor in helping any company to overcome challenges and leverage its results, in addition to helping it organize itself and better understand itself within the market.
To keep a business competitive, you need to be on top of what’s happening, know how to deal with a high volume of data, interpret them to notice failures, and remain in constant improvement. And your company must comply with it before starting to work with data analytics.
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