Data Analysis in Financial Market

What is the importance of Data Analysis at present?

The use of data analytics in finance is expanding globally. From big financial names to stock market analysis, financial markets rely on data analytics to better understand their consumers and trends.

What are the skills required for Data Analysis?

If you’re wondering who analyzes large datasets in a huge financial organization, financial data analysts are professionals who analyze the data of any organization to make accurate business decisions for their company. One of their key responsibilities is analyzing and assessing the firms' financial information. Often, in a firm, Financial Data Analysts collaborate with higher management corporate executives like Chief Financial Officers (CFOs).

The responsibilities of a Financial Data Analyst are to find significant and relevant insights from the organization’s records and prepare analytical reports that help other employees in the organization make wise decisions. The financial data analyst may be needed to gather data on the costs of mutual funds, equities, bonds, variable annuities, and other important financial assets. These insights can benefit firms and organizations in making certain important decisions including a firm's marketing strategy, and promotion strategy.

Programming languages like Python and R are commonly used in financial sectors due to the demand for effective analysis and “cleaning” of data. These tools listed below are capable of producing effective organizing and analysis of data.

Why is Data Analysis needed?

Data Analytics is employed in many organizations to analyze and give predictions on an organization's financial records(datasets). Data Analysis can be carried out due to some of the advantages listed below −

• minimize the possibility of human errors

• produces precise and accurate results

• provides accurate predictions in less time

• highlights factors that impact consumer behavior and purchase preferences

List of tools used in Data Analysis

There are numerous software and tools available these days to perform Data Analysis. Some of the most popular and commonly used tools are listed below −

• Python

• Microsoft Excel

• R Programming language

• SQL (Structured Query Language)

• Tableau

• Microsoft Power BI

How to choose the right Data analytic tool for your data?

Let's say that you have your data set available to you, you need to find the best tool to analyze it. But, how do you find the most appropriate and suitable one for your company?

You can find below a brief summary of each Data analytic tool, whether open-source or paid, along with information on its uses, advantages, and disadvantages.

Choosing the right Data Analytic tool - Things to consider

While choosing the appropriate Data Analytic tool, some of the factors to be considered are −

• availability of the tool/software application

• Business needs

• licensing and pricing of the software

Now that we have mentioned some of the commonly used Data Analytic tools, let us now dive into each of the Data Analytics tools in detail.

1. Python

Python is an effective data analytic tool for every data analyst as it has a wide range of applications. Compared to other programming languages, Python places a higher priority on readability, and because of its widespread use in the corporate world, many professionals are already having good knowledge of it. Moreover, Python comes with a wide range of resource libraries suitable for a wide range of diverse data analytics operations. Python's libraries like NumPy and Pandas are ideal for the general processing of data as well as simplifying complex machine learning tasks.

• Advantages − Simple and easy to learn for beginners, commonly used

• Disadvantages − Comparatively poor memory efficiency

2. Microsoft Excel

Excel is the most well-known software application. It is widely used for its computation and graphing tools which are excellent for data analysis. No matter what your area of expertise is, Excel is a standard data analytical tool. What's more, its useful built-in features including pivot tables are mainly used for sorting data. It also provides a wide range of additional features that simplify data manipulation.

• Advantages − commonly used for data entry and storage, used for calculations

• Disadvantages − unstructured data can’t be easily managed, runs very slowly with big datasets.

3. R Programming language

Similar to Python, R is a well-known open-source programming language. This software is widely used for statistical data analysis and computing these days. One of the advantages of the R programming language is that it can be integrated with programming languages like C++.

• Advantages − handles complex statistical computing tasks, and provides a wide range of tools for data analysis.

• Disadvantages − very slow compared to other programming languages like Python

4. SQL (Structured Query Language)

Structured Query Language (SQL) is a standard domain-specific Database language that is used to create, update and manipulate the data present in the relational database. To use SQL, it is not required to have substantial knowledge of SQL to use this programming language.

• Advantages − case-insensitive, faster efficiency

• Disadvantages − does not serve the purpose of handling larger dataset

5. Tableau

Are you looking for a software application to create interactive visualizations for your financial data? Tableau, one of the most preferred and widely used commercial data analysis tools, allows you to build interactive visualizations and dashboards without requiring you to have a deep understanding of programming. Tableau is incredibly user-friendly and designed to handle massive volumes of data better than most Business Intelligence tools.

• Advantages − user-friendly, handle massive volumes of data, less working knowledge of software required

• Disadvantages − Reports are manually refreshed, and performance is slow with the usage of large datasets.

6. Microsoft Power BI

Microsoft Power BI is a business intelligence (BI) software tool that is used to visualize, collect and analyze data. Its user interface is pretty similar to and self-explanatory to Excel users. A company's financial data can be analyzed to find various insights using Microsoft Power BI.

• Advantages − tell stories using charts and dashboards, open-source (paid and free versions are available)

• Disadvantages − bulky user interface, limited collaborating or publishing of features in free version

Conclusion

We have discussed what Data Analytics is and why it is needed in the financial markets these days. After finally choosing a Data Analytical tool from our list, you can further explore it if you are still unfamiliar with the tool. You can also take a look at our other articles below.