Data Analytics has been a very useful tool for organizations in recent years, enabling the processing and analysis of large sets of data to gain useful information in order to improve decision-making in business-related scenarios.

This technique allows managers to obtain information more effectively and accelerate the decision-making process based on intelligence.

Properly executed data analysis can reveal patterns in data collection that organizations managers are unaware of. The analyzed data can be used to guide resource allocation, improve production processes, increase productivity, discover market trends, and more.

Want to know what Data Analytics is, how it is used, and its importance? So, keep reading until the end. Enjoy! 

What is Data Analytics?

Data Analytics is the process of analyzing data to extract useful information that can be used to make management decisions.

Data Analytics can be implemented through various programming tools and languages. In this sense, in addition to specific Data Analysis tools, software developers also use languages such as Python, R, and SQL to create specific programs for this purpose.

Where Data Analytics is used and how important it is

In business organizations, Data Analytics is used to monitor and analyze data to improve operational efficiency.

Companies use Data Analytics to analyze trends, patterns, and relationships between data to make management decisions. Data Analytics can also be used to detect problems and opportunities, as well as predict future results.

Overall, there are four main areas where data analysis can be applied:

1. Marketing: Companies use Data Analytics to monitor the performance of marketing campaigns and understand what’s working and what’s not. This helps better target resources to the campaigns that are most successful. 
2. Finance: Companies use Data Analytics to monitor finances and make strategic decisions. This includes analyzing data to predict revenue and expense trends, as well as to detect fraud and risk issues.
3. Operations: Companies use Data Analytics to monitor operations and ensure they are working efficiently. This includes data analysis to optimize processes, as well as to monitor and detect problems.
4. HR: Companies use Data Analytics to monitor employee performance and make recruitment and selection decisions. This includes analyzing data to identify performance trends as well as predicting a candidate’s potential for success.

As we can see, Data Analytics is critical to getting valuable information about your business, products, and customers. It allows you to make decisions based on facts, rather than conjecture, to maximize profits and maximize the results of your campaigns.

In addition, Data Analytics also helps make businesses smarter and more proactive by allowing them to focus on growth opportunities. This also allows for better understanding of customers, their habits, behaviors and intentions.

Of course, Data Analytics use is not limited to the segments listed above, since there are numerous possibilities to extract its potential. Therefore, many businesses can use this tool to differentiate themselves from competitors and gain more visibility in the market.

See also: Unified namespace: concept, architecture and benefits for Industry 4.0

Existing Data Analytics analysis types

As mentioned earlier, the possibilities of using data analysis are immense. And this is made possible by the different techniques that can be applied to turn data into powerful insights.

Get to know about the main existing analyses types below:

• Regression analysis – Used to examine whether there is a relationship between two or more variables, and to predict the value of one variable based on values provided by other variables.
  
• Correlation analysis – Used to measure the strength of a linear relationship between two variables.
  
• Variance Analysis (ANOVA) – Used to examine whether there are significant differences between groups in one or more variables.
  
• Cluster analysis – Used to find relationships between variations of multiple datasets.
  
• Principal Component Analysis (PCA) – Used to reduce the number of variables in a dataset in order to detect similar groups.
  
• Discriminant analysis – Used to distinguish experimental groups based on measurements of variables.
  
• Logistic regression analysis – Used to predict the probability of a target variable result based on one or more input variables.

Examples of using Data Analytics

Mondelēz, Pão de Açúcar Group, Monsanto, Starbucks and Nike are companies with good examples of Data Analytics use in the real world.

Mondelēz, for example, used Data Analytics to help control who enters the company premises to ensure that everyone stayed safe during the pandemic. Smart turnstiles measured the temperature and checked that staff were wearing masks.

The Pão de Açúcar Group uses Data Analytics to better understand which products customers are buying. By doing so, the company is able to offer more discounts and promotions in a much more efficient way, which has positively impacted the results.

Another example came from Monsanto, which used Data Analytics to find the best time to plant seeds. This allowed for the improvement of the crop yield, in addition to the reduction of loss, in general, and the amount of land needed for planting.

Finally, we have EMPARN’s case with LogAp, a solution that is helping more than 213,000 rural workers in Rio Grande do Norte State in Northeast Brazil. Generally speaking, data collection and analysis applications have brought more efficiency to the state’s farmers.

Therefore, as we can see, Data Analytics is an extremely powerful tool for business. With cases in several areas, we know that the use of data as a strategic tool is already a path with no return.

Now, all we can do is see how this subject matter will evolve in the years to come.