How to apply data analytics in business decision making

Today, many companies are using data analytics to make the most of available information and improve their business strategies.

When talking about data analytics, the term big data is often used, referring to the collection, management, and analysis of a large volume of data that, due to its size and complexity, exceeds the processing capabilities of traditional tools.

Data analytics, if used properly, provides a competitive advantage over other companies in the industry by enabling organizations to identify new opportunities and leverage their insights to make strategic decisions.
Data analytics programs are evolving as the digital transformation of companies progresses. 

Despite the complexity that can be attributed to them, any company can take advantage of their benefits with the right methodology.  

In this article, we share some tips on how to apply data analytics in business decision-making.

What is data analytics?

Data analysis (DA) consists of inspecting a series of data to detect trends and draw conclusions about the available information.

This is done through specialized software that transforms the information into powerful visualization tools to maximize strategic decision-making.

The goal of data analytics is to drive business performance.

Decision-making based on data analytics

To make decisions based on data analytics, it is necessary to ensure that the available information is well organized, accurate, and easily interpretable.

The first step is to create a standard procedure for integrating data across different sources from both inside and outside the organization.

After automating this first phase, it is time to monitor and analyze the values obtained from it.

This is done through interactive dashboards specifically designed to make data analysis visual and intuitive, providing the possibility of understanding the information in a clear and fast way.

In addition, this system extracts data in real-time, allowing for more accurate analysis.

The use of data to guide decision-making in business strategy is known as "data-driven decision making".

Let's take a look at some stages of this methodology

Defining the Problem

First, it is necessary to know the initial state of the situation and, if there is a problem, to identify it clearly.

To do this, questions such as: What is the ideal scenario of this analysis? What is the current problem?

Data preparation

Once the problem is identified, it is necessary to understand what data needs to be analyzed to improve the baseline situation or solve the problem.

In this case, some questions that might help are: what data needs to be collected to solve this problem? How can such data be obtained?

Data processing

Once the necessary data have been obtained, the next step is to process them and prepare them for further analysis. In this phase it is important to question which information is relevant and which should be suppressed, that is to say, to carry out a cleansing of all the data to obtain the information that is really useful for our purpose. 

Data analytics to generate knowledge

Finally, we move on to the data analysis stage, to investigate the problem and find possible solutions. In this phase, we must answer what information about the problem the data provide us with and how this knowledge helps us to solve the problem. 

Implementation

It is time to implement the analysis performed and the decisions made based on the data obtained.
In other words, define an objective (what needs to be solved), design the strategy (how it will be solved), determine the tactics (actions to be taken), and choose the key metrics that will be used to analyze the results.

Data archiving

Finally, the last stage consists of the electronic archiving of all this useful information resulting from data processing and analysis.

Either for use at the same time or at a later time, keeping them under data protection legislation.

Conclusion: data analytics is key to making good decisions

The high level of competition in the market forces large businesses to resort to data analytics to improve their decision-making capacity.

Today, a large amount of information is stored, allowing the use of artificial intelligence for the generation of reports and dashboards that facilitate the search for solutions, which ultimately aim to optimize the profitability of the business.

Through data analytics techniques, it is possible to interpret raw information to detect trends or discover revelations that will help in decision making to achieve business success.