Top 6 Data Analytics Tools in 2019
By: Lewis Chou
Tags: Data Science, Data Analytics Tools, Business Intelligence, Data Analysis, Programming Languages

When it comes to data analytics tools, we always have questions. What is the difference between so many data analysis tools? Which is better? Which one should I study?

Although this is a commonplace topic, it is really important, and I have been working hard to pursue the answer to this ultimate problem. If you go online to search for relevant information in this area, it is difficult to see a fair point of view. Because the reviewers who evaluate a certain data analytics tool may be from a different perspective, with some personal feelings.

Today, let us put aside these personal feelings. And I am trying to talk objectively with you about my personal views on data analysis tools on the market, for your reference.

I have chosen a total of 6 tools in three types. Next let me introduce them one by one.

1. Excel

With a variety of powerful features such as form creation, PivotTable, VBA, etc., Excel’s system is so large that no analytics tool can surpass it, ensuring that people can analyze data according to their needs.

However, some people may think that they are very proficient in computer programming languages, and disdain to use Excel as a tool because Excel can’t handle big data. But think about it, do the data we use in our daily life exceed the limit of big data? In my opinion, Excel is a versatile player. It works best for small data, and with plugins it can handle millions of data.

To sum up, based on the powerful features of Excel and its user scale, my opinion is that it is an indispensable tool. If you want to learn data analysis, Excel is definitely the first choice.

2. BI tools

Business intelligence is born for data analysis, and it is born at a very high starting point. The goal is to shorten the time from business data to business decisions and use data to influence decisions.

The product goal of Excel is not like this. Excel can do a lot of things. You can use Excel to draw a curriculum, make a questionnaire, or use it as a calculator, or even use it for drawing. If you master VBA, you can also make a small game. But these are not really data analysis functions.

But BI tools are specialized in data analysis.

Take the common BI tools such as Power BI, FineReport, and Tableau for example. You will find that they are designed according to the data analysis process. First, data processing, data cleaning, and then data modeling, finally data visualization that uses presentation of charts to identify problems and influence decision-making.

These are the only way for data analysis, and there are some pain points of employees in this process.

For example, the repetitive and low value-added work of cleaning data can be simplified with BI tools.

If the amount of data is large, the traditional tool Excel cannot complete the PivotTable.

If we use Excel to do graphical displays, it will take a lot of time to edit the chart, including color and font settings.

These pain points are where BI tools can bring us change and value.

Now let’s compare the three popular BI tools on the market: Power BI, FineReport, and Tableau.

The core essence of Tableau is actually the PivotTable and PivotChart of Excel. It can be said that Tableau is keenly aware of this feature of Excel. It entered the BI market earlier and carried forward this core value.

From the perspective of development history and current market feedback, Tableau is better at visualization. I don’t think this is because of how cool its charts are, but its design, color, and user interface give us a simple and fresh feeling.

This is indeed like Tableau’s own propaganda, investing a lot of academic energy to study what kind of charts people like, how to give users the ultimate experience in operation and visual. As Tableau advertises, their team puts a lot of academic energy into researching what kind of charts people like, and how to give users the ultimate experiencein terms of operation and vision.

In addition, Tableau has added data cleaning function and more intelligent analysis functions. This is also a predictable product development advantage for Tableau.

The advantage of Power BI lies in its business model and data analysis capabilities.

Power BI was previously a plug-in for Excel, and its development was not ideal. So it got out of Excel and developed into a BI tool. As a latecomer, Power BI has iterative updates every month and catches up very fast.

Power BI currently has three licensing methods: Power BI Free, Power BI Pro, and Power BI Premium. Just like Tableau, the features of its free version are not complete. But they are almost enough for personal use. And the data analysis of Power BI is powerful. Its PowerPivot and DAX language allow me to implement complex advanced analysis in a way similar to writing formulas in Excel.

What makes FineReport unique is that its self-service data analysis is very suitable for business users. With a simple drag and drop operation, you can design various styles of reports with FineReport and easily build a data decision analysis system.

From FineReport

FineReport can directly connect to all kinds of databases, and it is convenient and quick to customize various styles to make weekly, monthly, and annual reports. Its format is similar to the interface of Excel. The features include report creation, report permission assignment, report management, data entry, etc.

In addition, the visualization function of FineReport is also very prominent, providing a variety of dashboard templates and a number of self-developed visual plug-in libraries.

In terms of price, the personal version of FineReport is completely free and all features are open.

3. R & Python

R and Python are the third type of tools I want to talk about. Although softwarelike Excel and BI tools have been designed with the utmost effort to consider the most application scenarios of data analysis, they are essentially customized. If the software doesn’t design a feature, or develop a button for a feature, chances are that you won’t be able to complete your work with them.

The programming language is different for this. It is very powerful and flexible. You can write code to do anything you want. For example, R and Python are the indispensable tools for data scientists. From a professional perspective, they are definitely powerful than Excel and BI tools.

So what are the application scenarios that R and Python can realize, while it is difficult for Excel and BI tools to achieve?

1) Professional statistical analysis

In terms of R language, it is best at statistical analysis, such as normal distribution, using algorithm to classify clusters, and regression analysis. This kind of analysis is like using data as an experiment. It can help us answer the following questions.

From Google

For example, the distribution of data is a normal distribution, a triangular distribution or other types of distribution? What is the discrete situation? Is it within the statistical controllable range we want to achieve? What is the magnitude of the effect of different parameters on the results? And there is also hypothetical simulation analysis. If a certain parameter changes, how much impact will it bring?

2) Individual predictive analysis

For example, we want to predict the behavior of a consumer. How long will he stay in our store? How much will he spend? We can find out his personal credit status and make a loan amount based on his online consumption record. Or we can push different items based on his browsing history on the web page. This also involves the current popular concepts of machine learning and artificial intelligence.

Conclusion

The above comparison illustrates the difference between several softwares. What I want to summarize is that what is real is reasonable. Excel, BI tools or programming languages have overlapping functions, but they are also complementary tools. The value of each depends on the kind of application being developed and situation at hand.

Before you choose a data analytics tool, you must first understand your own work: whether you will use the application scenarios I just mentioned. Or think about your career direction: whether it is toward the data science or business analysis.

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