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No matter how much thorough planning you do when doing research and gathering information, your data is going to be messy and might be missing values. Yet, the most common issue is making sure that data is cleaned right, and genuine data is being kept.
What is Data Cleaning?
Data cleaning involves using programming languages or software to remove inaccuracies, poor formatting, duplicates, and incomplete data within a dataset. When multiple datasets are involved the amount of cleaning will increase and you’ll need to review each column and row to make sure everything is in their proper place. Be it as it may, there is no right process of cleaning data as each dataset is unique and gets more specialized as more datasets are involved. You can establish a template on your data cleaning process, so you and others know you are doing it the right way.
Data Cleaning and Data Transformation, What’s the Difference?
Data cleaning removes data out of your dataset like nulls and outliers or anything that could not be related to your data. Data transformation is converting data from one format to another, to then be further analyzed to support a decision.
What are the Benefits of Clean Data?
- Improved Efficiency and Cost Savings: Errors and inconsistencies in data are removed, allowing analysts to focus on valuable insights while also saving your organization money by preventing future mistakes and eliminating incorrect data.
- Enhanced Decision-Making: Clean data ensures that business decisions are based on accurate and reliable information, leading to more effective strategies and outcomes.
- Better Insights: Data cleaning provides a clearer understanding of your industries behaviors and preferences, enabling businesses to tailor their products and services more effectively.
- Regulatory Compliance: Keeping data accurate and up-to-date helps businesses comply with regulations and avoid legal issues related to data inaccuracies.
- Increased Revenue: Accurate data analysis can identify new opportunities for revenue generation and help business stay ahead of the competitors.
Once you create a standard operating procedure (SOP) on how your company should clean data, you’ll be able to save your company revenue in the long run. This method will allow you to limit errors and enable your data analysts to focus on translating the data for their target audience.
Krista’s Studio is a small business that will clean your data and build a visualization to help you connect with your customers. Reach out today to get started!
