First Steps to Store and Analyze Data

We are constantly exposed to charts and statistical analyses, whether on television, social media, or other platforms. Never in history has there been such an abundance of this type of information. Visual and statistical data are used to inform us about a wide variety of topics. Market trends, sports results, social issues, and political matters are just a few examples of what we encounter in our daily lives.

Learning about data analysis is essential, both to present your company’s data more effectively and to boost your career development.

Organizing and presenting data, although time-consuming, is not a complex task. In this guide, we will show the first steps so you can begin this journey with confidence.

Understanding the Data

Before taking action, we need to understand the data we will be working with. Deep knowledge of the business is more important than the data itself. Impressive charts and analyses have no value if they do not make sense to those interpreting them.

Explore all available data sources for your project. Identify, categorize, and establish correlations. Prioritize the most relevant data, focusing on what has the greatest impact on the audience that will analyze it.

Choosing the Type of Storage

There are different ways to store data. Choosing the best option depends on the type and volume of data, as well as your analysis needs.

The first aspect to consider is the storage environment, whether local or cloud-based. Although cloud storage may involve higher costs, in most cases it proves to be a viable solution. The cloud offers the advantage of allowing data to be published and accessed online. However, when choosing this option, it is crucial to evaluate factors such as security, data volume, and processing capacity.

Local storage offers the advantage of starting projects in a more economical and secure way. If your budget is limited, you can begin internally, creating models and analyzing data without incurring cloud publishing costs.

Data Collection

After identifying the data and choosing the storage method, the next step is data collection. At this stage, you may receive information from multiple sources. Databases, APIs, sensors, and social networks are among the most commonly used sources. Your challenge will be finding the best ways to extract and consolidate data from these different origins.

Data collection can range from direct file extraction to system integration through APIs and other automated tools. Depending on the complexity of the project, you may use automation scripts to streamline this process. It is important to consider data quality and integrity throughout this stage to avoid future issues.

Ensure that the data is complete and accurate before moving on to analysis. This will help prevent errors and ensure that derived conclusions are precise and reliable.

Data Cleaning and Preparation

Once the data has been collected, it is essential to establish correlations between datasets. At this stage, it is common to encounter inconsistent, duplicated, or incomplete information. This is one of the most critical moments in the process, as data quality directly impacts results. Working with high-quality data, even in smaller quantities, is essential to ensure accurate analyses. On the other hand, excessively filtered or discarded data can lead to distorted results.

Data Presentation

Finally, we create dashboards, tables, and charts to present the data. Use specialized tools for these presentations, such as Tableau or Power BI. If you are not familiar with these tools, presentations can also be created using Excel or PowerPoint.

Conclusion

Storing and analyzing data is an essential skill in the modern world, where visual and statistical information is abundant. By following the steps outlined in this article, you can begin to understand and manage data effectively. Data quality is fundamental for accurate analyses, and clear presentation of information supports better decision-making. By using the right tools and practicing consistently, you can transform data into valuable insights that drive projects and advance your career.