How did you use a spreadsheet to help prepare your data?
Microsoft Excel will only function efficiently if the data set contains correct entries or consistent values. The process of data preparation can take days to accomplish, depending on the type of data sets in the spreadsheet.
This article is not about how your spreadsheet looks, for they are mainly meant to organize and categorize actual data in a logical format.
This article will focus more on how you use a spreadsheet to help prepare your data. What steps must you take to make your data readable for Microsoft Excel or Google Sheets?
How helpful is Microsoft Excel spreadsheet?
Microsoft Excel spreadsheets are very helpful for business purposes such as organizing financial data (revenue, payroll, etc). It is also helpful for everyday tasks such as budgeting, calculating expenses, and storing personal data.
Using spreadsheets helps make data storage and organization easier for data analysis. Excel allows you to create charts and graphs for data visualization and spot trends.
How do you use a spreadsheet software?
Several people use spreadsheets for different purposes. A spreadsheet is primarily used for data entry and management, charting, and graphing. You can easily make spreadsheets adaptable to any workflow.
It is also used for financial analysis and as an accounting tool. You can integrate spreadsheets with tools specifically designed for financial documents. You can do time and task management with an Excel spreadsheet.
Using professionally-made spreadsheet templates can make your use of spreadsheets easier and faster. Simple Sheets offers hundreds of templates for data analysis, project management, finance, accounting, etc.
Steps in Preparing an Excel Spreadsheet for Data Storage and Data Analysis
Before you attempt to prepare your data in the spreadsheet, ensure you have saved a backup copy of the raw data. It is still best to keep a copy of the original data before you make changes to the Excel file.
Here are the steps for preparing data on a spreadsheet:
1. Preparing the Spreadsheet File
The first step in preparing your data is to prepare your spreadsheet file. When preparing your file, review the whole Excel file and understand the contents. Do the headers, graphs, tables, data, and other elements in the spreadsheet relate to each other?
Once you understand what your spreadsheet file looks like, you can proceed to the next step, which is to rename the data to a more appropriate file name. The file name should suggest the contents of the file. This way, anyone who sees it knows what it is about and what it contains.
2. Raw Data Importation
If you have an existing text file and you want to incorporate it into a spreadsheet, it is possible using the Data import function of Excel. There are three ways you can import data to Excel.
First is by splitting data along delimiters, next is extracting parts from data entries, and lastly, removing leading or trailing spaces.
Split Along Delimiters
A delimiter can either be a comma or a semicolon. When you import data into Excel and split data along delimiters, your data is converted into a tabular format.
Extracting Data Parts
If you want a more advanced operation, such as extracting specific parts of the data, you would need to perform an advanced splitting function. This feature is very useful when you want to separate email addresses into two parts: username and domain name.
Remove Leading or Trailing Spaces
Removing leading or trailing spaces in all your entries is essential in preparing your data to be clear and accurate. Leading and trailing spaces often appear when you import data into the spreadsheet. The trim function is the quickest way to remove these spaces.
3. Formatting Adjustment
After importing data from an external source, it is necessary to make the necessary adjustment to the format of the cells in the spreadsheet. There are four ways to proceed with format adjustment: to standardize it, keep data in the correct format, replace corrupted or unrecognizable characters, and check for truncated data.
Apply Standard Format
Before you make major changes to the spreadsheet, you must put order and standardize the format of your cells. If your file comes from a country that uses a different numerical data format, change it to a convention you are more comfortable with or with the convention specified by your company.
Data Should Be in the Correct Format
Data analysis is efficient if all the data in the spreadsheet are stored in the correct format. For numerical data, you should specify the number as it is. If a cell value is not a number, format it as text so Excel can accurately analyze data.
Be careful when formatting data, especially customer data which needs to be accurate and constantly updated.
Replace Characters That Are Corrupted or Unrecognizable
It is possible that your spreadsheet program cannot recognize corrupted characters when data is imported. Before proceeding further, you must correct this data by using Excel’s find and replace feature.
Check for Truncated Data
Truncated data means the storage location is too short that it cannot hold the data’s entire length and display it completely. Your spreadsheet program may or may not inform you if such an error occurred.
A sample data that represents truncation is a numerical value with decimal points. The same values may have their decimals removed during importation, hence, truncated data. The best remedy is to ask for a healthier copy of the file.
4. Correcting Inconsistencies
In preparing your data, you should expect there will be several inconsistencies that you need to address. When correcting inconsistencies, you must have basic knowledge of the data file.
For instance, in a given sample data, you have to understand what each data in a column or row stands for. You may say them in natural language, like “data cells in column C are weight values that are not lesser than or equal to zero.” Only then will you differentiate data that needs correcting.
There are three typical inconsistencies in data preparation that you should take note of. The three common inconsistencies that need to be corrected are outliers, wrong data categories, and missing entries.
Check Data Outliers
Anything is possible when you work with numeral data, and one thing that you need to look for are data outliers. Outliers are values that are either too small or too large compared to the majority of the other values in the data set. It is recommended to sort data by size values to check for outliers.
Check for Wrong Categories
When you categorize data, ensure the column headings follow a standard convention. For instance, similar product data should be placed under one column category and not elsewhere.
Check for Missing Entries
If you spot missing entries that are valuable to the spreadsheet file, you should request for the original copy of the file. Data analysis won’t be accurate with missing data.
5. Removing Duplicates
It is necessary to remove the same data or duplicates before you can combine or analyze data sets. This can be conveniently done by using Excel’s remove duplicates function.
Duplicates may not necessarily look alike. They may have slightly different spelling due to typographical errors, but they mean the same thing. If you suspect duplicates in a column, you can verify this by checking other related columns to see if the encoded data are the same or not.
6. Combining Data Sets
Combine your data so you can start analyzing data sets. You may copy data from one spreadsheet to another if the data have been properly formatted and checked for accuracy.
Be careful when combining data from other spreadsheets, for you may end up copying the wrong data set, which plays a huge factor in the accuracy of the data analysis.
Conclusion
You are now aware of the process you must go through to prepare your data for analysis. There is no better tool than a spreadsheet software to help simplify the data preparation process.
Data preparation can’t be accomplished overnight. It takes time and a lot of patience. But with the right tools and spreadsheet templates, you can make data preparation faster and more meaningful.
