Data Cleaning

Data Scientists and analytics specialists spend most of their time cleaning data. Data cleaning also referred to as data cleansing is the process of making raw, noisy, inaccurate and incomplete data correct, complete and useful. As a process in data pre-processing, data cleaning involves a set of techniques applied to raw data from the source to produce a clean dataset useful for modelling and analytics. In large data projects data cleaning can be an independent project with structured management process. The key objective of data cleaning is to provide a high-quality dataset that gives true picture about the business. In this post we will look at data cleaning process and why it’s important in machine learning and analytics.

Importance of Data Cleaning

Data cleaning is an important process in the data management strategy of any organization. It helps to ensure the data is useful and trustworthy. Below are few benefits of data cleaning;

1. Removes erroneous data.
2. Easier data transformation. Clean data makes it easy to transform and apply business logic and formulas.
3. Detection of sources of errors. The data cleaning process provides more details on the source of errors. Knowing what causes errors helps in fixing the problems permanently.
4. Increase trust in data. Clean data can be trusted unlike erroneous one.
5. Automated data cleaning process helps in getting data for decision-making ready quickly.

Features of Quality Data

Quality data is asset to the organization hence every organization and businesses strives to have quality data. Below are attributes of quality data;

1. Accuracy
2. Consistency
3. Uniformity
4. Validity
5. Completeness.

Data Cleaning Process

There are different ways of cleaning data depending on how noisy/unclean the data is. Below are some of the important techniques to use for data cleaning;

1. Remove Duplicates
2. Remove outliers
3. Handle missing data
4. Correct structural errors. These include typos, naming conventions, date and time formats etc.
5. Data Validation. After cleaning validate the data to ensure its up to quality standard required.

Conclusion

As they say “garbage in garbage out” the results retrieved from data are as good as the quality of the data. Data cleaning is a core process in achieving the data quality within an organization. In this post we have looked at what’s data cleaning, its benefits, and characteristics of quality data and the data leaning process. Data cleaning can be complex to the extent of being treated as an in depended project or it can be a simple process within the data pre-processing phase. In the next post we will look at data integration as a step in data pre-processing phase in Data Science process. To learn about data pre-processing check our previous post here.