How do I use BI tools to preprocess data?

In today's digital age, data is not only the basis for business decision-making, but also a key enabler for innovation and growth. In the face of large and complex data sets, efficient preprocessing has become a crucial step in the field of data analysis.

In the daily work of data processing and analysis, Excel and SQL are commonly used in the business. However, there are some problems that may be encountered in the actual process of using these two tools for data processing:

excel：

Limited to data size:Excel can become slow and take up a lot of memory when processing large amounts of data, resulting in slow performance. This can be a challenge for datasets with millions of rows.

Manual operation error:Excel often requires manual data cleaning and transformation, which increases the potential for human error. Copy-pasting formulas and data manipulations can lead to erroneous results, especially in complex data processing tasks.

Version Control Issues:In team collaboration, if multiple people edit an Excel file at the same time, it is easy to lead to version conflicts, making the data processing process difficult to manage and track.

Limited automation capabilities:Excel's automation capabilities are relatively limited, especially when dealing with large, complex data sets, and the ability to automate and reuse them is relatively weak.

sql：

Complex syntax:SQL syntax is relatively complex, and it can take some time for beginners to learn Xi and understand SQL. Writing complex queries can be error-prone, and debugging them can be time-consuming.

Dealing with strings is relatively cumbersome:In SQL, the processing of strings is relatively cumbersome, especially when it comes to operations such as text splitting, merging, and fuzzy matching, which may require complex writing.

Performance issues:For large-scale datasets, some queries can cause performance issues, and query statements need to be optimized or indexes used to improve efficiency.

Difficult to process unstructured data:SQL is more suitable for relational databases, and it is relatively difficult to process unstructured or semi-structured data, so other tools need to be introduced in addition to SQL.

As data continues to grow in size and complexity, and the need for real-time decision-making grows, the industry is turning to more efficient and flexible BI (business intelligence) tools. Compared with the challenges faced by Excel and SQL when processing large-scale and complex data, BI tools provide users with more efficient and convenient data processing solutions with their powerful automation and intuitiveness. In this article, we will explain in depth the key skills of using BI tools for data preprocessing, hoping to provide data analysis help and ideas for enterprise employees who have introduced BI tools

The pictures in the article are all realized by Finebi, the star product of Finesoft!

Before data analysis can be performed, specific processing of the data structure is often required in order to carry out subsequent analysis work more efficiently. The raw data often does not directly meet the needs of our analysis, so some row and column transformations must be performed in order to adjust the format and structure of the data to the requirements of the analysis.

In FineBI, we encapsulate the functionality through data editingSplit Rows and Columns and Rows ConversionsQuickly and flexibly adapt and reorganize data to quickly obtain the desired analysis results. With the "Split Rows and Columns" function, we are able to split the raw data according to the specified rules, so as to separate the required information. Row and Column Conversion, on the other hand, allows us to flexibly transform rows and columns in the dataset to meet different analysis needs.

Original data structure: The fields are mixed, which is not conducive to analysis

Data structure after processing: After splitting the rows and columns and converting them, the field structure is simple and clear

2. Handle duplicate row data

In the actual business analysis process, data quality issues often become the most important obstacle to the smooth business analysis. One of the most common and intractable problems is the presence of duplicate rows. When dealing with these duplicate rows, we are usually faced with two main situations, each of which requires a specific way of handling.

First, there are cases where deleting any row will not have a material impact on the results of the analysis, such as if there are duplicate rows like "a, a, a" in the data, and only one of the "a" can be retained. In this case, FineBi encapsulates the "Remove Duplicate Rows" function, which can be implemented quickly and easily in business analysis. With this feature, we are able to easily eliminate redundant data to ensure that the dataset is clean and tidy, which is conducive to subsequent accurate business analysis.

Second, there is another case where a specific row of data needs to be selectively retained. For example, the same customer may have two different rows of data records in the system, and we may need to select the most recently entered data for analysis. In this scenario where only A is needed in A, B, and C, we first sort the data table to ensure that the latest data is at the top of the data table, and then use the logic of "Remove Duplicate Rows" to keep only the top row of data, so as to achieve the purpose of filtering and retaining specific rows. This process is both simple and effective, providing a flexible and controllable means of data cleansing for business analysis.

The header drop-down menu also makes it easier to check duplicate rows.

3. Handling null values

Handling null values is an unavoidable challenge in various business scenarios, and different business scenarios often require completely different processing strategies.

When faced with a large dataset, we can usually ignore null values if they occur relatively rarely, and these null values do not fluctuate significantly in calculations such as sums or averages. This method of processing can effectively reduce the impact on the calculation results when the amount of data is large.

On the other hand, for those cases where you want to treat null values as dirty data and eliminate them as whole rows, we can quickly exclude these null values with the help of the quick filter function in the header. This method can easily exclude the entire row of data containing null values by using the filter tool in the header, so as to ensure the cleanliness and accuracy of the data.

The above are simple scenarios, but in actual business, you may encounter situations where null values have business implications.

For example, in the data in the example, the reason why this student's English score is empty may be that he did not take the test due to illness, and he can neither leave it alone nor delete his line of data directly.

In this case, what we need to do is to label a particular case with a corresponding label, so that we can selectively filter it in the subsequent analysis. In Finebi, it is possible to use:Add Formula ColumnOr more convenientlyConditional Label Columnto achieve it.

Multi-table merge analysis refers to the method of merging information from multiple different data tables together for comprehensive analysis in the process of data analysis. In actual business or research, data is usually distributed across multiple **, and the purpose of multi-table combined analysis is to obtain more comprehensive and comprehensive information, so as to draw deeper conclusions.

This process usually includes the following steps:

Data joining:The first step in multi-table merge analysis is to connect data from multiple tables through some kind of association. This often requires connections through shared key fields (e.g., customer ID, product number, etc.) to ensure that the relevant data is properly correlated.

Merging:Once the connection is established, the next step is to merge the data of the relevant ** into a larger data set. This can be achieved through different merging methods, such as inner, left, right, or outer, depending on the analyst's needs for the data.

Analysis:The combined datasets can be used for more in-depth analysis, such as generating statistical metrics, building models, conducting trend analysis, and more. Since the data comes from multiple sources, merging multiple tables can help to gain a more holistic view, making the analysis results more comprehensive and convincing.

In practice, the data we need often comes from multiple tables. Another big challenge before analyzing is how to merge the tables. For those who are new to BI, we have summarized the following two merger scenarios.

Let's first imagine the state of the merged table, one is**Scaling up and down, the number of fields analyzed has not increased, but the number of rows has increased. It can be used at this timeMerge up and downQuickly complete the splicing of tables.

Another complication is post-merger** is scale-out, i.e., there are more fields to analyze.

Before we talk about left-right mergers, let's take a lookAdd Columns to Other Tables

Maybe you're scratching your head about the name, but for sureYou won't be unfamiliar with Excel's Vlookup and SumiT

That's right, this feature can aggregate the metric fields of other tables and merge them (sumif) or query the corresponding dimensions to match them to this table (vlookup).

For SQL experienced players, left join, right join, .....It may be more gracious, at this point you can choose BI in data editingMerge left and rightThe function is consistent with the logic of SQL, and it is more convenient than the operation of SQL, and it does not need to be realized.

After simplifying the data structure and merging multiple tables, we need to pause and look at the problem we are analyzing and whether the metrics needed to address it are already in the table.

Generally speaking, things may not go so smoothly, of course, this is also common sense, for example, in the analysis of the retail industry, we often need to calculate the gross profit margin, growth rate and other indicators ourselves.

Before we start the analysis, we can add these calculated metrics to the data table. How?

The first is the most familiarAdd Formula ColumnThis function is the same as writing formulas in excel, you only need to enter the corresponding formula to generate the corresponding field. This is followed by encapsulation features for some common calculationsAdd Summary Columncan help us with simple aggregate calculations.

Select the corresponding group and calculation method to calculate the metric.

WhileConditional Label ColumnThis feature solves the biggest headaches for many analysts on a daily basisif nesting issueThere is no need to write nested seven or eight layers of if formulas, and you can assign different labels (values) to the data by configuring different conditions with the mouse.

The biggest problem encountered by friends who are new to BI is not only that they do not understand the calculation logic of many functions of BI, but also that they do not trust the results of data processing. "Did I do it right?This is one of the most common questions that novice friends ask themselves. In order to facilitate the user to verify, the data editing interface also has many built-in convenient functions.

1. Verify the header data

After selecting the field, you can quickly get the average, sum, number of records and other data in the lower left corner, and we can verify the familiar data and judge whether it is correct based on experience.

For example, in the example below, the math score field is verified to give an average score of 8592, in line with the class historical average.

2. The key steps in the step area are canceled and applied

BI can insert new steps between processing steps, and can also set certain steps to be temporarily canceled.

Using this, we can do trial and error by filtering out some of the key data and removing some of the key steps that are doubtful. Just like the multiple checks of Xi inertia when you first learned mathematics, although it is a little cumbersome for veteran players, it is indeed the most reassuring for novices.

In summary, BI tools provide a powerful and flexible platform for data preprocessing, and by mastering the skills in them, we can deal with complex data scenarios more efficiently and provide more powerful support for business decisions. In this data-driven era, a good understanding of data preprocessing will be an important skill for every data analytics professional. Not only does it improve the efficiency of our analysis, but it also ensures that we can extract accurate and deep insights from our data, paving the way for business success.