{"id":12966,"date":"2020-12-31T09:00:00","date_gmt":"2020-12-31T08:00:00","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=12966"},"modified":"2022-01-05T20:03:25","modified_gmt":"2022-01-05T19:03:25","slug":"data-cleaning","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/big-data\/data-cleaning\/","title":{"rendered":"8 Techniques for Efficient Data Cleaning"},"content":{"rendered":"\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tTable Of Contents\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
  1. What is Data Cleaning?<\/a>
  2. Is Data Cleaning Essential?<\/a>
  3. What are the Benefits of Data Cleaning?\u00a0<\/a>
  4. Remove Unwanted Observations<\/a>
  5. Filter Unwanted Outliers<\/a>
  6. Avoid Errors Like Typos<\/a>
  7. Convert Numbers Stored as Text Into Numbers<\/a>
  8. Deal with Missing Values<\/a>
  9. Convert Data Types<\/a>
  10. Get Rid of Extra Spaces<\/a>
  11. Delete All Formatting\u00a0<\/a>
  12. Data cleaning: recap<\/a><\/ol>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\r\n\r\n\r\n

    Data is an essential part of data analytics<\/a>, data security<\/a>, and data science<\/a>. That\u2019s obvious.\u00a0Sometimes, however, that data<\/span> can get a little dirty. No, not like in a gangster film. More like where suddenly we are having to deal with \u2018dirty data\u2019 after a hold up at a data centre. When there is a mistake in the spelling, arrangement, formatting, or construction which has made that data unclear. For these reasons, every so often you need to apply data cleaning<\/strong>.<\/p>\r\n\r\n\r\n\r\n

    \r\n
    \"\"<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

    Data cleaning may seem like an alien concept to some. But actually, it\u2019s a vital part of data science. Using different techniques to clean data will help with the data analysis process<\/strong>. It also helps improve communication<\/a> with your teams and with end-users. As well as preventing any further IT issues along the line.<\/p>\r\n\r\n\r\n\r\n

    Unfortunately, data cleaning can take up a huge chunk of time for data scientists. Yet, as having poor or wrong data can be detrimental to a task, it\u2019s an important thing to do. It\u2019s not all bad, though. High-quality data that has been cleaned can make your job so much easier.\u00a0<\/p>\r\n\r\n\r\n\r\n

    So, professionals must know techniques to perform it properly and efficiently. Then you can get on with other work. Like developing a cloud contact center<\/a>. Or making amazing AI.\u00a0<\/p>\r\n\r\n\r\n\r\n

    Of course, different types of data require different types of cleaning<\/strong>. But there are general approaches that make a good starting point. Here are eight techniques for essential data cleaning.\u00a0<\/p>\r\n\r\n\r\n\r\n

    What is Data Cleaning?<\/h2>\r\n\r\n\r\n\r\n

    Before we jump in, it\u2019s important to know what data cleaning actually is. It\u2019s the process of identifying<\/span> and removing or fixing \u2018bad\u2019 data<\/strong>. This is usually inaccurate, unreliable, or unfinished data from databases or tables.\u00a0<\/p>\r\n\r\n\r\n\r\n

    The data then needs restoring, removing, or remodelling. Sometimes, if the data is dirty or crude, it needs removing completely.\u00a0<\/p>\r\n\r\n\r\n\r\n

    For example, say it is your job to handle the data on platforms for eCommerce<\/a> sites. If the data you put out is bad, this can create problems on the site that lead to a loss in profit and reputation. Such as the wrong items being advertised next to the wrong description.\u00a0<\/p>\r\n\r\n\r\n\r\n

    Data cleaning can be done either interactively with data cleansing tools or as batch<\/span> processing through scripting. After it has been cleaned, the data needs to match up with other related datasets<\/span> in operation.<\/p>\r\n\r\n\r\n\r\n

    Is Data Cleaning Essential?<\/h2>\r\n\r\n\r\n\r\n

    Although it isn\u2019t spoken about as often as it should be, data cleaning is an essential part of a data scientist\u2019s job<\/strong>. Especially as more industries than ever are adopting some sort of cloud storage<\/span>. As the use of data storage grows, the more likely there is to be a problem.\u00a0<\/p>\r\n\r\n\r\n\r\n

    For example, say a company uses a hosted predictive dialer<\/a> to contact clients. They will have a large volume of customer information stored as data. If the data stored is not clean – i.e., the wrong name is next to the wrong number – agents run the risk of making mistakes when contacting clients. Which can lead to a few disgruntled customers, to say the least.\u00a0<\/p>\r\n\r\n\r\n\r\n

    This means that as a professional in the IT industry, it\u2019s your job to make sure things run smoothly in this area. And a huge part of that involves data cleaning.\u00a0<\/p>\r\n\r\n\r\n\r\n

    What are the Benefits of Data Cleaning?\u00a0<\/h2>\r\n\r\n\r\n\r\n

    As well as helping other companies, data cleaning makes your job as a data professional easier too. Whether you are working on deep learning<\/a> or developing a site, these are just a few ways in which it will help you in your work:<\/p>\r\n\r\n\r\n\r\n