{"id":167727,"date":"2023-04-20T13:28:02","date_gmt":"2023-04-20T12:28:02","guid":{"rendered":"https:\/\/liora.io\/en\/?p=167727"},"modified":"2026-02-06T09:04:28","modified_gmt":"2026-02-06T08:04:28","slug":"data-cleaning-definition-methods-and-relevance-in-data-science","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/data-cleaning-definition-methods-and-relevance-in-data-science","title":{"rendered":"Data cleaning : Definition, methods and relevance in Data Science"},"content":{"rendered":"Data cleaning is an essential step in Data Science and Machine Learning. It consists in solving problems in data sets, to be able to exploit them later on. Definitions, techniques, use cases, training&#8230;\n\n<b>Data is essential<\/b> in Data Science, Artificial Intelligence, and Machine Learning. They are the fuel of these technologies.\n\nTherefore, it is very important to ensure data quality. Today, it is very easy to find good quality, clean and structured data on dedicated marketplaces. On the other hand, for a company to clean its <b>internal data<\/b>, a company must resort to data cleaning.\n\n<iframe title=\"What is Data Quality ? Importance &amp; challenges for business\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/qc4oWfpjnio?list=PLbH8UGHWFlsTo6cG8Fgbgz0MWT7X4TGTW\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n\n<br \/>\n.elementor-column .elementor-spacer-inner{height:var(&#8211;spacer-size)}.e-con{&#8211;container-widget-width:100%}.e-con-inner&gt;.elementor-widget-spacer,.e-con&gt;.elementor-widget-spacer{width:var(&#8211;container-widget-width,var(&#8211;spacer-size));&#8211;align-self:var(&#8211;container-widget-align-self,initial);&#8211;flex-shrink:0}.e-con-inner&gt;.elementor-widget-spacer&gt;.elementor-widget-container,.e-con-inner&gt;.elementor-widget-spacer&gt;.elementor-widget-container&gt;.elementor-spacer,.e-con&gt;.elementor-widget-spacer&gt;.elementor-widget-container,.e-con&gt;.elementor-widget-spacer&gt;.elementor-widget-container&gt;.elementor-spacer{height:100%}.e-con-inner&gt;.elementor-widget-spacer&gt;.elementor-widget-container&gt;.elementor-spacer&gt;.elementor-spacer-inner,.e-con&gt;.elementor-widget-spacer&gt;.elementor-widget-container&gt;.elementor-spacer&gt;.elementor-spacer-inner{height:var(&#8211;container-widget-height,var(&#8211;spacer-size))}\n<br \/>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]&gt;a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}\n<h3>What is Data Cleaning?<\/h3>\nData Cleaning encompasses several processes to <b>improve data quality<\/b>. There are many tools and practices to eliminate problems in a dataset.\n\nThese processes are used to correct or remove inaccurate records in a database or dataset. Generally speaking, this means <b>identifying and replacing<\/b> incomplete, inaccurate, corrupt, or irrelevant data or records.\n\nAfter a properly performed data cleaning, all data sets should be <b>consistent and error-free<\/b>. This is essential for the use and exploitation of the data.\n\nWithout cleaning, the results of the analyses are likely to be skewed. Similarly, a Machine Learning or AI model trained on bad data can be biased or deliver poor performance.\n\nData Cleaning is different from Data Transformation. Cleaning is about <b>converting data from one format to another<\/b>, while Transformation (also called Wrangling) is about <b>converting the raw data into a format suitable for analysis<\/b>.\n<h3>What is the purpose of data cleaning?<\/h3>\nData is now an essential resource for companies in all sectors. In the age of Big Data, it is used to support <b>critical decision-making<\/b>.\n\nAccording to a study conducted by IBM, poor data quality now costs $3.1 trillion per year in the United States. And that cost is <b>growing exponentially<\/b>.\n\nPrevention through data cleansing is relatively affordable, but fixing existing problems can cost ten times as much. Even worse, fixing a problem in the data after it has caused an outage is <b>100 times more expensive<\/b>.\n\nA wide variety of problems can arise from<b> low-quality data<\/b>. For example, a marketing campaign may be poorly targeted and therefore fail.\n\nIn the <b>medical branch,<\/b> poor data can lead to inappropriate treatments and even to the failure of drug development. A study by Accenture reveals that a lack of clean data is the biggest barrier to AI adoption in this field.\n\nIn <b>logistics<\/b>, data can cause problems with inventory, delivery planning, and thus affect customer satisfaction. In the manufacturing field, factories configuring robots with bad data are exposed to serious problems.\n\nFinally, data cleaning is required to comply with privacy regulations imposed by laws. Regardless of the sector, this practice can therefore avoid major problems.\n<h3>The advantages of data cleaning<\/h3>\nData cleaning offers many benefits. One of the main benefits is to enable better <a href=\"https:\/\/liora.io\/en\/data-driven-definition-benefits-and-methods\"><b>data-driven decision-making<\/b><\/a>.\n\nHigher quality positively impacts all activities involving data. Data is becoming increasingly important in all sectors.\n\nTo take full advantage of this practice, <b>data cleaning<\/b> must be seen as an enterprise-wide effort. It not only streamlines business operations but also increases productivity as teams no longer have to waste time on incorrect data.\n\nSales can increase if marketing teams have access to the best data. The accumulation of these <b>various internal and external benefits<\/b> leads to increased profitability.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-management\">Start a Data Cleaning training course<\/a><\/div><\/div>\n\n<h3>The different types of data problems<\/h3>\nCompanies collect a wide variety of data, from <b>multiple sources<\/b>. This information can be collected internally or from customers, or even captured from the web and social networks.\n\nHowever, during this process, different problems can arise. First of all, a dataset can contain duplicate data, i.e. several identical records.\n\nData can also conflict. A dataset may contain several similar records with different attributes.\n\nOn the contrary, sometimes <b>data attributes<\/b> are missing. The data may also not be compliant with regulations.\n\nThese problems can be caused by different sources. It may be a synchronization issue, where data is not properly shared between two systems.\n\nAnother cause can be a software bug in <b>data processing applications<\/b>. Information may be &#8220;written&#8221; with errors, while correct data may be overwritten by accident.\n\nFinally, the cause may simply be human. Consumers may deliberately provide incomplete or incorrect data to protect their privacy.\n\n<iframe title=\"Discover our Data Scientist training - DataScientest\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/kNPe_pgbuHg?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<h3>What are the characteristics of high-quality data?<\/h3>\nTo be considered high quality, data <b>must meet several criteria<\/b>. It must be &#8220;valid&#8221;, which means that it corresponds to the rules and constraints set by the company. This may include constraints on data types, values, or the organization of data in databases.\n\nQuality data must also be accurate, complete, consistent, uniform, and traceable. These are the characteristics that impact data quality and that can be corrected with data cleaning.\n<h3>The steps of data cleaning<\/h3>\nTo be effective, data cleaning must be considered as a <b>step-by-step process<\/b>. To begin, a data quality plan must be established.\n\nThis plan consists of identifying the main source of errors and problems, and determining how to remedy them. Corrective actions should be distributed to the appropriate managers.\n\nIn addition, metrics should be chosen to <b>measure data quality <\/b>in a clear and concise manner. This will subsequently help prioritize data-cleaning initiatives.\n\nFinally, a set of actions and steps to be taken must be identified to start the process. These actions will be <b>updated<\/b> over time as the data quality changes and the business evolves.\n\nThe <b>second step<\/b> is to correct the data at the source before it enters the system in the wrong form. This practice <b>saves time and energy<\/b> and allows problems to be corrected before it is too late.\n\nAfter that, it is important to measure the accuracy of the data in real-time. There are various tools and techniques available for this purpose.\n\nIf you, unfortunately, cannot remove duplicates at the source, it is important to <b>detect <\/b>and actively remove them afterward. You should also standardize, normalize, merge, aggregate and filter the data.\n\nFinally, the<b> last step<\/b> is to complete the missing information. After completing this process, the data is <b>ready to be exported<\/b> to a data catalog and analyzed.\n<h3>How to get trained in Data Cleaning?<\/h3>\nData Cleaning is <b>essential for Data Science<\/b> and&nbsp;<a href=\"https:\/\/liora.io\/en\/artificial-intelligence-definition\" target=\"_blank\" rel=\"noopener\"><b>Artificial Intelligence<\/b><\/a>. It is therefore imperative to master the various tools and techniques that exist to work in these fields.\n\nTo acquire these skills, you can opt for Liora training. Our different programs <a href=\"\/en\/courses\/data-ai\/data-engineer\"><b>Data Engineer<\/b><\/a>, <a href=\"\/en\/courses\/data-ai\/data-analyst\"><b>Data Analyst<\/b><\/a>, and <a href=\"\/en\/courses\/data-ai\/data-scientist\"><b>Data Scientist<\/b><\/a> allow you to learn how to process data and especially how to clean it.\n\nAt the end of these professionalizing courses, you will be ready to work in<b> Data Science<\/b>. Among former students, 93% found a job immediately. You will also receive a <b>degree certified by the Sorbonne University<\/b>.\n\nAll our courses are offered as BootCamp or Continuing Education. The<b> Blended Learning approach<\/b>, innovative in France, reconciles distance and face-to-face learning to offer the best of both worlds. Don&#8217;t wait any longer and discover our Data Science training courses!\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-management\">Discover our Data Management course<\/a><\/div><\/div>\n\n\nNow that you know everything on Data cleaning, discover our blog post on <strong><a href=\"https:\/\/liora.io\/en\/data-science-definition-issues-and-use-cases\">Data Science<\/a><\/strong> or the one on the <strong><a href=\"https:\/\/liora.io\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">main concepts of Machine Learning<\/a><\/strong>.","protected":false},"excerpt":{"rendered":"<p>Data cleaning is an essential step in Data Science and Machine Learning. It consists in solving problems in data sets, to be able to exploit them later on. Definitions, techniques, use cases, training&#8230; Data is essential in Data Science, Artificial Intelligence, and Machine Learning. They are the fuel of these technologies. Therefore, it is very [&hellip;]<\/p>\n","protected":false},"author":74,"featured_media":167728,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-167727","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167727","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=167727"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167727\/revisions"}],"predecessor-version":[{"id":206425,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167727\/revisions\/206425"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/167728"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=167727"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=167727"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}