Data-driven decision-making is the new golden rule in the world of business. A Statista survey revealed that 77 percent of US businesses rely on data to guide their decisions in the United States. However, data in its raw form is dirty and unusable, which is why you must first apply data cleaning techniques.
Before analyzing data and drawing insights from it, you must know how to learn data cleaning and the different steps you can follow to create high-quality data. This article will cover the different data cleaning methods and explore data cleaning ideas that can make the entire process more insightful, exciting, and successful.
Best Data Cleaning Technique Examples
The dataset you work with determines the type of cleaning technique you use. Some datasets are simple and can be cleaned with only one technique, while others are more complex and may require more steps. Below, you’ll find a list of the most effective data cleaning techniques.
Remove Duplicate Observations
Due to human error, improper imputation method, or data scrubbing, you may encounter duplicate values in your dataset. Duplicate values detract from the accuracy of your research, so you need to remove them before starting your analysis. The search and delete functions are usually enough to fix this issue.
Fix Structural Errors
Structural errors refer to any deficiency in your data. This includes typographical errors, incorrect capitalization, incorrect class names, wrong string sizes, and excess white space. Solving structural errors could be as easy as doing a spell check. In complicated cases, you may have to map the incorrect values and convert them.
Filter Unwanted Outliers
Outliers in statistics are values that fall outside of the majority of your observation. Although they are not inherently bad, you must distinguish which data is valuable to your model and which is not. These values could be informative and support your theory, so don’t remove data simply because it is an outlier. You must always have a legitimate reason before altering the dataset.
Manage Missing Data
You must address missing data to protect the integrity of your research. There are various ways to do this, such as inputting the missing values based on your observations. You could alter the way you use the data to avoid the missing values or tell the algorithm that the data value is missing by inputting null values like zero or N/A.
Convert Data Types
Before you can even begin to wonder what data analysis is, your data types must match. The most common data type mismatches are with numerals written as text. Text is classified as a string, and it is impossible to do proper analysis with strings. You need to convert all numbers written as text into numerals. If you’re unable to convert a value, indicate it in the dataset.
If you gather data from several different sources, the odds are that each dataset will come with different formatting. Machine learning models cannot analyze heavily formatted data, so it’s best that the formatting on your document is uniform. Most data analysis systems have functions to erase all the formatting on a document so that you can begin from scratch.
The natural language processing models that run data analysis software are currently incapable of bilingual or polylingual data processing. All the data in your document must be in the same language to avoid errors. Any observations that are not in the host language should be translated to match.
Remove Irrelevant Observations
Irrelevant observations in your dataset can slow down your analysis and make it unnecessarily complicated. Unlike outliers, these values have nothing to do with the research. You can filter out the unwanted observations in your data by sketching out the goals of your analysis and determining which ones further those goals and which ones do not.
How to Clean Data in Excel
- Remove white spaces. Blank spaces in your dataset can cause errors in your analysis. Since Excel does not display extra spaces, these white spaces can be tough to spot manually. By using Excel’s Trim function, you can quickly remove any blank spaces that may be interfering with your data.
- Treat blank cells. You need to address empty cells in your dataset so you can run your data analysis smoothly. You can replace empty cells with zero for numeric values and N/A for alphabetic values. Doing this manually can consume a lot of time, so you can press the F5 button to use the Excel dialogue box to highlight all blank cells.
- Highlight errors. Instead of manually searching for errors in your data, you can use Excel’s conditional formatting function to highlight errors and correct them. Select the entire column you wish to correct, navigate to the conditional formatting option in the styles section, and apply the highlight rules that best fit your objective.
- Use find and replace. The Find and Replace function helps you identify and change a particular value in the entire dataset. If you’ve misspelled a name several times throughout a dataset, Find and Replace enables you to determine all the places where that name appears. Once found, you can replace them with the appropriate value.
- Analyze data using text to column. You may have multiple data elements in a single cell delimited by a space or punctuation. To be properly analyzed, each element needs to have its own column. You can use Excel’s text-to-column function to separate these elements by selecting your data and the delimiter to separate the data elements.
Data Cleaning Ideas: Top 5 Tips to Master Data Cleaning
Data cleaning is exhausting, monotonous work, but you can’t afford to skip it. You need it to create high-quality data and make good decisions. According to the Bureau of Labor Statistics, the average yearly salary of operations research analysts is $86,200. If you can master data cleaning, you’re sure to have a successful and fruitful career.
Although data cleaning is tedious, there are ways to make the process more bearable. Below you’ll find a few tips you can incorporate into your data cleaning routine to boost the process and increase its efficiency.
Develop a Data Quality Plan
In data analysis, you should begin by creating a data quality plan. This ensures that you are collecting the right data in the correct format and that it’s fit for the purpose you intended. Knowing what you want to do with your data before collecting it can save you stress cleaning data.
Validate Data Accuracy
Checking the accuracy of your data is an excellent way to gauge the success of your data cleaning efforts. Provided with the right data analyst tools, you’ll easily be able to validate your data. Email verification tools and import lists are good examples.
Make a Copy of Your Data
Ensure that you have a backup of your raw data before you start cleaning. In the unfortunate event of your program getting deleted or lost, having a backup copy means you have something to fall back on. Also, this comes in handy if you make any irreversible errors in the cleaning process.
Standardize Your Data Collection
You cannot maintain a healthy dataset if you consistently let in dirty data. It’s important to standardize your data collection before the data collection process begins, so you can ensure a minimum level of data cleanliness. Setting standards, value limits, or limiting a field to the alphabet or numeric data are all means of doing this.
Use a Data Cleaning Tool
A dataset can contain thousands of entries. Cleaning each one manually could take forever. Using the data cleansing tools in your data analysis software. or employing linear regression models can smooth out your data. If you work in Excel, Microsoft has a list of third-party providers you can explore for more data cleaning products.
Are Data Cleaning Techniques Worth Learning?
Yes, data cleaning techniques are worth learning. Not only is it a relevant skill for data analysts to learn, but it can also save you from drawing false conclusions and potentially informing wrong business decisions. Enrolling in the best data analytics bootcamps will boost your skills and teach you more data cleansing techniques and tips.
Data Cleaning Techniques FAQ
Data analysts claim that data cleaning makes up about 80 percent of the entire analysis process. It is a difficult process, but there are data tools you can use to speed it up.
Yes, you can automate data cleaning. You can write code or use more sophisticated programs that use machine learning and artificial intelligence to automate your data cleaning. Automated data cleaning is quicker and, in some ways, more efficient than manual data cleaning.
No, data cleaning is not hard, provided you take the time to learn. The real challenge is cleaning data efficiently. Data cleaning is tricky and time-consuming, and if your data is going to be of any use, you need to be organized and efficient in your cleaning process.
Data cleaning is necessary because dirty data produces incorrect results. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. Clean data is important to ensure that you have the most accurate data for analysis. Simple algorithms should be able to understand it and draw insights from it.
About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication.