As businesses generate and accumulate large amounts of data, it becomes a herculean task to remove unwanted or inaccurate data. While modifying such data sets requires effort, identifying and removing them are not easy either. Data cleaning identifies incorrect data and modifies it according to requirements. Data that is cleaned will then need to be transformed into a standard format so that it can be used easily in the future. This process is known as data standardisation.
Data cleaning and standardisation help businesses to get rid of clutter in their databases, improve system performance, generate better insights, and have a standardised format of data that can be recognised, shared and used across departments.
In this article, let us look at what data cleaning and standardisation is, in detail, and learn how these processes help businesses.
“Data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analysing it.”
Why data cleaning and standardisation is inevitable?
- Data cleaning recognises errors
The first step towards cleaning data is to identify errors and inconsistencies. For example, this involves identifying mistakes in email addresses, phone numbers, ensuring that names are all written correctly, etc. Identifying and rectifying errors in dataset is performed by comparing data with various reliable sources. Error identification is an important aspect of data audit.
For example, making sure that the email address field does not have “2” symbol twice, or making sure that there is no space between characters of an email address. Cross-checking mobile phone numbers are typed in correct format and are of the specified limit of characters.
- Remove duplicate data
Many businesses suffer from duplicate data entries, which cause a lot of confusion and operational errors. Duplication of data can be minimised when software programs are integrated but they cannot be eliminated completely. Data cleaning ensures that duplicate entries of data are removed, so that your database is free from multiple duplicate entries
- Validate scrubbed and corrected data
Once incorrect data is identified and corrected, and duplicate entries are deleted, it is important to validate remaining data for accuracy as a last step. This is done with the help of data cleaning tools that analyse data in bulk. Validation ensures that your final copy of data is error-free, most-recent, and accurate.
Once data is validated, final versions are communicated to various departments that may use the data. This ensures that all business processes are efficient and that efforts are not wasted.
4.Handling missing values
One of the important stages of data cleaning is handling missing values. Real-world data tends to be incomplete, noisy, and inconsistent.
If we specifically look at dealing with missing data, there are several techniques that can be used. Choosing the right technique is a choice that depends on the problem domain — the data’s domain (sales data? CRM data? …) Either u can delete the rows, entire column depending on the dependencies and requirement. Substitution, Last observation carried forward, Maximum likelihood, etc…
What is standardisation of data and how is it done?
Once data is cleaned and made ready for use, it needs to be standardised into a common format that can be used by various entities. Data standardisation ensures that all your information is stored on platforms that are recognisable by various users. Data standardisation ensures advanced analytics, collaboration with external and internal agencies, and other processes take place smoothly. Once standardized, data is stored in a common data model (CDM) format. This format varies depending on the industry you are in.
To standardize data, we need to first clean it and understand the data entry points. Next, we need to choose data standards so that unruly data sets can be written into a commonly recognizable format, also known as CDM, as discussed before. Finally, data needs to be mapped into matrices so that it is indexed for future use.
Some of the most important benefits of data cleaning and standardisation can be summarised below:
- Reduce and eliminate duplicate entries of data
- Identify and rectify errors in data sets
- Streamline business processes with the help of mapped data
- Easily collaborate with multiple internal and external entities after standardising data
- Derive more accurate insights and reports from the data you have stored
BI Developer specialized in SQL Query Development/ SSIS/ SSAS/ SSRS /Power BI/ MS Office. Enjoy working in data analysis and presentation, challenging projects where I can uncover valuable business insights for an organization from previously under-utilized data sources and creating custom made reports and interactive dashboards
When someone writes an post he/she retains the image of a user in his/her brain that how a user can know it. Thus that’s why this post is perfect. Thanks!|
Wow, superb blog structure! How long have you been blogging for? you make running a blog glance easy. The entire look of your website is great, as neatly as the content!
I constantly spent my half an hour to read this web site’s posts all the time along with a cup of coffee.|
Good way of describing, and nice post to get facts regarding my presentation focus, which i am going to present in academy.|