Why Data Cleaning is a Significant Step for Accurate Data Analysis?
To ensure that your marketing strategies are running as smoothly and effectively as possible, keeping a tight rein on data hygiene is essential. In order to do so, you should be aware of the exactness and timeliness of your information. This falls under the purview of data cleaning, which is an integral part of a data analyst’s job. The data cleaning process helps to identify, diagnose, and correct data discrepancies that may exist in data sets. It also contributes to data accuracy by ensuring data is clean, complete, and consistent.
There is a reason why this is so important: when your data is flawed, the outcomes of your analyses will also be unreliable. Inaccurate results can lead to misguided decisions, which could potentially have a negative impact on any business. It is, therefore, essential to maintain data quality. Keep reading to know more about data cleaning, why it is so important, and what you can do to ensure data accuracy.
What is Data Cleaning?
Before the data is processed, it undergoes a “cleaning”. This is when anomalies in the data are removed, it is transformed into a usable format, and organized to make it more useful. Without data cleaning, data analysts can have difficulty making sense of the data or interpreting the results correctly.
In short, data cleaning aims to ensure that accurate information and insights are produced from data sets.
Why is Data Cleaning Important?
Data cleaning is important because it ensures consistency within your data set and helps you achieve reliable results from any analysis you perform on it. Additionally, regularly checking for inconsistencies allows you to identify problems in your data sets before they become bigger issues down the line. Finally, when you clean your data properly, data analysis becomes smoother and more efficient.
Also Read: Why Data Scraping is an Essential Tool for Business Success in the Digital Age
The Data Cleaning Process
There are four steps to data cleaning. The process uses both manual data cleaning by analysts and automated cleaning with tools.
1# Data Identification
In this first step, data that is incomplete, outdated, or incorrect is parsed out. This data is identified with the use of data visualizations like histograms and boxplots. Even summary statistics such as mean, median, and mode help to do this. Data analysts should also be aware of errors caused by human data entry, coding mistakes, and data transformation processes.
Suppose data concerning a customer’s age is missing in an e-commerce data set, for instance. In this case, data identification would involve recognizing that data is missing and understanding why it’s incomplete. Only then can you proceed to the next step of data diagnostics.
2# Data Diagnosis
In this second step, data analysts focus on identifying the source of errors within data sets. This is also when they decide which data points should be removed or verified. Analysts employ a range of tools for the diagnosis. Along with data visualizations and summary statistics, they also use cross-validation techniques to identify potential culprits. For instance, if there are outliers in the data set that could affect results, they will be flagged for further investigation.
Additionally, data diagnosis may involve exploring data relationships to understand the correlation between data points. Doing so helps data analysts identify which data points are likely to have an effect on the data set and which ones should be removed or corrected. Understanding these data relationships can lead to more accurate data cleaning.
3# Data Correction
This third stage is the process of making data consistent and accurate by identifying data points that need to be changed. This involves filling in missing data and transforming data into a usable format by removing unnecessary characters or formatting data for analysis, for instance. It also verifies data points for accuracy.
For example, imagine there are multiple entries with erroneous values, such as negative numbers in an e-commerce data set. In this case, data correction would involve finding and replacing these incorrect values with correct ones. While this is a basic example, for more complex problems, analysts need to use more cleaning techniques.
4# Data Integration
The last and final step, data integration involves combining data from multiple data sets into a single data set for analysis and decision-making. Data integration requires analysts to identify data points and variables that can be merged across data sets. They also have to keep an eye out for any potential conflicts or duplications of data points.
For example, if you have two customer databases with different formats (CSV vs. JSON, for instance), data integration will involve transforming both into a unified structure before merging them together. Additionally, data analysts may need to update or create new fields depending on the type of data they are working with. Ultimately, this process helps ensure that data is consistent across all sources, thus improving the accuracy of analysis and decision-making.
What are the Key Benefits of Data Cleaning?
Data cleaning is an important data preprocessing step necessary to improve the accuracy and reliability of data analysis. Some key benefits of data cleaning include:
1. Better Organization
Data cleaning helps data analysts stay organized by helping them identify data points that may be missing or inaccurate. This helps data analysts improve their data analysis processes.
2. Improved Accuracy
By identifying, diagnosing, correcting, and integrating data sets into a unified structure, data cleaning helps ensure that data is accurate and consistent across all sources. This improves the validity of the results of data analysis, offers better insights, and thus, helps in better decision-making.
3. Reduced Time
Data cleaning can save data analysts significant amounts of time by automating data preprocessing steps such as filling in missing values or transforming data into uniform formats.
4. Better Mapping
Mapping is the process of connecting data points across data sets and data sources. Data cleaning helps improve the accuracy of data
mapping by ensuring that data is consistent and accurate across all sources. This leads to better insights, which ensure improved decision-making.
5. Avoiding Unnecessary Costs
Data cleaning can help data analysts avoid unnecessary costs by ensuring data is accurate and consistent across all sources. This reduces the need for data correction or other data preprocessing steps, thus helping data analysts save costs.
6. Higher Productivity
Data analysts improve their productivity by automating data preprocessing steps, thus freeing up time that can be spent on more complex data analysis tasks. This helps data analysts maximize their efficiency and deliver results faster.
7. Avoiding Mistakes
Mistakes can potentially lead to data loss or data-related issues. Analysts can identify incorrect or missing data points with the help of data cleaning. This helps them avoid data-related mistakes and ensures data accuracy for analysis and decision-making.
Learn More About Data with Emeritus
By following data cleaning best practices such as identifying errors and inconsistent values, applying statistical techniques to correct errors, integrating multiple data sets into a unified structure, and automating data pre-processing steps whenever possible, data analysts can ensure their data analysis projects are accurate and reliable. This ultimately leads to better insights and more informed decisions based on data analysis results. Explore Emeritus’ data science courses and learn more about data-related topics and get a headstart on your career.
