Whether you are a data engineer or a data scientist, you will spend most of your time cleaning data! It is estimated that data scientists spend about 80% of their time cleaning data. This means only 20% of the time will be used to analyze and create insights from the data science process. Data cleaning enhances data quality.
The concept of data cleaning ensures that the quality of data is preserved and enhanced to meet the business needs. Insights are drawn from quality data help make the best business decisions.
Data quality is the measure of how adequate and how fit the data can be used to solve the organization’s specific needs. Trusted decisions can only come from quality data.
Data is therefore considered high quality if it fits your organization’s needs or your business question. The following are some of the characteristics of quality data:
- Consistency -
Data cleaning ensures that the quality of data is preserved and enhanced to meet the business needs. Insights are drawn from quality data help make the best business decisions.
Note: Data Cleaning is different from data transformation.
While data transformation aims at converting data from one format to another, data cleaning focuses on maintaining or enhancing the quality of data by removing or filling missing values on the dataset.
Data cleaning is one of the essential steps in the data science process. Some of the benefits of doing good data cleaning include:
- It enhances the results one gets from their analysis.
Having a clean dataset means you have ample resources to work with — data- to help create the insights needed in the analysis. This increases productivity and reduces the time taken to get the insights.
- Data cleaning removes errors from the dataset.
Working on a dataset with errors will give inconsistent results, which would impact the insights that you, as a data scientist, will draw. As a result, cleaning your data ensures that your insights are consistent with the goals you have.
- The data cleaning process can be used to help solve the incorrect or corrupted data collection.
Once you identify a common error in the dataset, you can easily suggest or solve the issue of erroneous data collection. This would subsequently save your business the cost of data cleaning on common problems that are solvable.
- Clean data makes it easy to make decisions.
Having clean data means fast analysis and model creation. This saves time in the decision-making process.
Data cleaning process
There are various techniques to clean data. These are based on the needs and the organization.
Data cleaning follows general concepts, which include:
- Dealing with missing values
- Dealing with outliers
- Removing duplicate & unwanted observations
- Categorical variables and encoding
In the following couple of days, I am going to handle each of the above steps. Join me on this journey as we learn together how to clean our data and reap the benefits of a clean dataset!
Let’s start here: Dealing with missing values