Data Scrubbing and Cleansing

Quality vs. Quantity B2B Lead Generation
July 13, 2021
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Data Scrubbing and Cleansing

Data scrubbing is defined as the process of making changes in a database and keeping it up-to-date. Several data-intensive organizations, such as banks, retail, insurance, and telecommunication, use data scrubbing tools on a regular basis to assess their databases and make the required changes. 

Also known as data cleaning, data cleansing is a less involved process of sorting out your data. It deletes the data that does not fit into your dataset. Simply put, data scrubbing is a subset of data cleansing. Several factors, such as cryptic data, contradicting data, missing values, inappropriate use of address lines, reused primary keys, and non-unique identifiers, can degrade the quality of your data. 

Data scrubbing helps with the following issues:

  1. Duplicate data:

Data scrubbing identifies identical data and removes it from the dataset. This feature can also help you merge data from 2 different systems. 

  1. Inconsistent Data:

With the help of data scrubbing tools, you can examine your data and make sure it is consistent with the rules set for that dataset and follows a certain format. 

  1. Redundant Data:

Data scrubbing helps you remove data that is no longer required and minimizes the amount of disk space needed to store it. 

  1. Errors and typos:

Prevalent errors, such as typos and missing information, can be corrected by data scrubbing. 

There are various companies that offer data cleansing services in San Jose and can help you with the aforementioned issues. 

How do you clean data?

  1. Delete irrelevant observations

Removing duplicate and irrelevant observations is the first and foremost step in having a clean database. Not having any irrelevant records in your data will make it easier for you to analyze it, and you won’t get distracted from your primary target. It will also be easier to manage and will yield results more efficiently. 

  1. Fix structural errors

Structural errors include typos and wrong capitalization. These errors can lead to mislabeled classes or categories. 

  1. Handle missing data

Algorithms do not accept missing values. To handle missing data, you can either drop those observations or assume the missing values based on other observations. However, remember that both of these ways have their disadvantages and are not perfect. 

  1. Question – Answer

Once you have followed these steps, ask yourself the following questions:

  • Does the data make sense?
  • Does it follow all the required rules?
  • Does it provide any valuable insight into your working theory?

If the answer to the questions above is yes, it means the quality of your dataset is satisfactory. 

Conclusion 

Data scrubbing is one of the essential parts of a data management strategy. By keeping your dataset clean and updated, top data cleansing companies in San Jose can help you make well-informed decisions, minimize errors, increase efficiency, and avoid inconsistencies. Make sure you conduct detailed research and find a data scrubbing tool that best fits your company’s demands. 

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