Data Quality Metrics You Should Track and Measure
Tags: webdev, database, productivity, devops
Published: 2023-03-31 08:46:59


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/t4pv9h1p6fev2uif1wvk.png)<p class="c12"><span class="c9">Low-quality data is one of the major reasons why companies miss out on revenue-driven opportunities and make poor business decisions. So, what can be done </span><span class="c4">about it? </span></p><p class="c12"><span class="c4">Read this post to find which data quality metrics every business should track and measure to realize their full potential.</span></p><h2 class="c14" id="h.zi4pkum1u1yu"><span class="c17">Why is data quality important?</span></h2><p class="c12"><span class="c7">The answer to this question is as simple as that &ndash; the better the quality of your data, the more benefits you can get from it. In other words, data quality is important because it helps businesses acquire accurate and timely public information to manage service effectiveness and ensure the correct use of resources. </span></p><p class="c12"><span class="c9">As discovered by </span><span class="c22"><a class="c11" href="https://www.google.com/url?q=https://www.ibm.com/blogs/journey-to-ai/&amp;sa=D&amp;source=editors&amp;ust=1680255359083496&amp;usg=AOvVaw2KVBDavLJaj9SxzIFvcimP">IBM</a></span><span class="c7">, in the US alone, businesses lose $3.1 trillion due to poor quality data annually. What&rsquo;s important to mention, the impact is not only financial. Bad data wastes your team&#39;s time, leads to customer dissatisfaction, and drives out top employees by making it impossible to perform well.</span></p><p class="c1"><span class="c9">All these issues call for an effective way to track and access the collected public data in order to make sure it&rsquo;s of the highest quality. </span><span class="c22"><a class="c11" href="https://www.google.com/url?q=https://www.linkedin.com/in/allenoneill/&amp;sa=D&amp;source=editors&amp;ust=1680255359083895&amp;usg=AOvVaw0cNYZjgwQwqGVpHogT5jkp">Allen O&rsquo;Neill</a></span><span class="c9">&nbsp;already stressed the importance of ensuring consistency in the quality of data in his informative </span><span class="c22"><a class="c11" href="https://oxy.yt/Hph0">guest post on our blog</a></span><span class="c9">, stating that &ldquo;</span><span class="c9 c15">If your data isn&rsquo;t of high enough quality, your insights will be poor; they won&rsquo;t be trustworthy. That&rsquo;s a really big problem&rdquo;</span><span class="c7">. </span></p><p class="c1 c19"><span class="c7"></span></p><p class="c12"><span class="c9">On the other hand, some potential </span><span class="c22"><a class="c11" href="https://www.google.com/url?q=https://www.lotame.com/why-is-data-quality-important/&amp;sa=D&amp;source=editors&amp;ust=1680255359084553&amp;usg=AOvVaw356GlTU6oxDRCHN4wGWkEr">advantages</a></span><span class="c7">&nbsp;of high-quality data include:</span></p><ul class="c3 lst-kix_w07lmvop7nbd-0 start"><li class="c5 li-bullet-0"><span class="c7">Easier analysis and implementation of data</span></li><li class="c5 li-bullet-0"><span class="c7">More informed decision-making</span></li><li class="c5 li-bullet-0"><span class="c7">A better understanding of your customers&rsquo; needs</span></li><li class="c5 li-bullet-0"><span class="c7">Improved marketing strategies </span></li><li class="c5 li-bullet-0"><span class="c7">Competitive advantage </span></li><li class="c5 li-bullet-0"><span class="c7">Increased profits</span></li></ul><h2 class="c14" id="h.pjy4eztk3rvu"><span class="c17">What are the 6 dimensions of data quality?</span></h2><p class="c12"><span class="c7">Now that you have a proper understanding of why data quality is essential, we can dive into explaining each of the data quality dimensions that together define the overall value of collected public information. </span></p><p class="c1"><span class="c9">Organizations agree that data quality can be broken down into </span><span class="c22"><a class="c11" href="https://www.google.com/url?q=https://www.cdc.gov/ncbddd/hearingloss/documents/dataqualityworksheet.pdf&amp;sa=D&amp;source=editors&amp;ust=1680255359085673&amp;usg=AOvVaw2Zhl8HSh_gp-OLo4O4FB7n">6 core categories</a></span><span class="c9">:</span></p><a id="t.15285f364ab6174ae63441270d4e6087f539f234"></a><a id="t.0"></a><table class="c20"><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c2">Dimension</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c2">Defining question</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Completeness</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Is all the necessary data present?</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Accuracy</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">How well does this data represent reality?</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Consistency</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Does data match across different records?</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Validity</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">How well does data conform to required value attributes (e.g., specific formats)?</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Timeliness</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Is the data up-to-date at a given moment?</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Uniqueness</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Is this the only instance of data appearing in the database?</span></p></td></tr></table><h3 class="c28" id="h.ieca2320igyv"><span class="c2"></span></h3><h3 class="c18" id="h.ziiubbr4qorc"><span class="c6">Completeness</span></h3><p class="c12"><span class="c7">A data set can be considered complete only when all the required information is present. For instance, when you ask an online store customer to provide their shipping information at checkout, they will only be able to move on to the next step when all the required fields are filled in. Otherwise, the form is incomplete, and you might eventually have problems delivering a product to the right location.</span></p><h3 class="c18" id="h.emics08mmv1i"><span class="c6">Accuracy</span></h3><p class="c12"><span class="c7">Data accuracy represents the degree to which the collected public information describes the real world. So, when wondering if the public data you got is accurate, ask yourself: &ldquo;Does it represent the reality of the situation?&rdquo; &ldquo;Is there any incorrect data?&rdquo; &ldquo;Should any information be replaced?&rdquo;</span></p><h3 class="c18" id="h.xzvhwexwdpd"><span class="c6">Consistency</span></h3><p class="c12"><span class="c7">A large number of organizations tend to store information in various places, and maintaining synchronicity between them is one of the integral steps toward ensuring the data is of high quality. In case there&#39;s even a slight data difference between two records, unfortunately, your data is already on a path to losing its value.</span></p><h3 class="c18" id="h.da6n2vey4rxp"><span class="c6">Validity</span></h3><p class="c12"><span class="c7">Validity is a measure that determines how well data conforms to required value attributes. For example, when a date is entered in a different format than asked by the platform, website, or business entity, this data is considered invalid. </span></p><p class="c12"><span class="c7">Validity is one of the dimensions that are easy to access. All that has to be done is a check if the information follows certain formats or business rules.</span></p><h3 class="c18" id="h.dglyy8qrp914"><span class="c6">Timeliness</span></h3><p class="c12"><span class="c7">As the name suggests, timeliness refers to the question of how up-to-date information is at this very moment. Let&rsquo;s say specific public data was gathered a year ago. Since it is very likely that new insights were already produced during that time, this data can be labeled as untimely and would need to be updated. </span></p><p class="c12"><span class="c7">Another essential component of timeliness is how quickly the data was made available to the stakeholder. So, even if it is up-to-date within the warehouse but cannot be used on time, it is untimely. </span></p><p class="c12"><span class="c7">It is extremely important that this dimension is constantly tracked and maintained. Untimely information can lead to wrong decisions and cost businesses time, money, and reputation. </span></p><h3 class="c18" id="h.x68v8rhvut23"><span class="c6">Uniqueness</span></h3><p class="c12"><span class="c7">The information can be considered unique when it appears in the database only once. Since it is not rare to see data being duplicated, it is essential to meet the requirements of this dimension by reviewing the data and ensuring none of it is redundant.</span></p><h2 class="c14" id="h.dwxmcu2y7nb9"><span class="c17">Data quality metrics you should measure and track</span></h2><p class="c1"><span class="c9">Let&rsquo;s agree &ndash; understanding the dimensions of data quality doesn&rsquo;t seem that hard. However, having this knowledge is still not enough to adequately track and measure the quality of your data. </span><span class="c9 c15">While dimensions give us a general idea of </span><span class="c9 c15 c26">why</span><span class="c9 c15">&nbsp;they are important, data quality metrics define </span><span class="c9 c15 c26">how</span><span class="c9 c15">&nbsp;specifically each of the dimensions can be measured and tracked over time. Thus, </span><span class="c9">t</span><span class="c9">he six dimensions should be instantiated as metrics, also referred to as database quality metrics or objective data quality metrics, that are specific and measurable.</span></p><p class="c1 c19"><span class="c7 c15"></span></p><p class="c1"><span class="c7">For instance, a typical metric for the completeness dimension is the number of empty values. This data quality metric helps to indicate how much information is missing from the data set or recorded in the wrong place. </span></p><p class="c1 c19"><span class="c7"></span></p><p class="c12"><span class="c7">Talking about the accuracy dimension, one of the most obvious data quality metrics is the ratio of data to errors. This metric gives businesses an opportunity to track the number of wrong entries, such as missing or incomplete values, in relation to the overall size of the data set. If you find fewer data errors while your data size grows, it means that the quality of your data is improving. &nbsp;</span></p><p class="c1"><span class="c9">Check out this table for </span><span class="c22"><a class="c11" href="https://www.google.com/url?q=https://www.metaplane.dev/blog/data-quality-metrics-for-data-warehouses&amp;sa=D&amp;source=editors&amp;ust=1680255359093566&amp;usg=AOvVaw20i2nV1a7ASXuVOT7BbrlP">more examples of data quality metrics</a></span><span class="c7">&nbsp;for each of the six dimensions:</span></p><p class="c1 c19"><span class="c2"></span></p><a id="t.cb37e2c24da03414953ebf11c40c1ecd528db020"></a><a id="t.1"></a><table class="c20"><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c2">Dimension</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c2">Sample data quality metrics</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Completeness</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Number of empty values, number of satisfied constraints</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Accuracy</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Ratio of data to errors, degree to which your information can be verified by a human</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Consistency</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Number of passed checks to the uniqueness of values or entities</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Validity</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Number of data violations, degree of conformance with organizational rules</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Timeliness</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Amount of time required to gather timely data, amount of time required for the data infrastructure to propagate values</span></p></td></tr><tr class="c8"><td class="c21" colspan="1" rowspan="1"><p class="c1"><span class="c9">Uniqueness</span></p></td><td class="c13" colspan="1" rowspan="1"><p class="c1"><span class="c9">Amount of duplicated information in relation to the full data set</span></p></td></tr></table><p class="c12 c19"><span class="c7"></span></p><p class="c12"><span class="c7">Keep in mind: data quality metrics that will be the most suitable for your use case will depend on the specific needs of your organization. The essential thing is to always have a data quality assessment plan to make sure your data fits the needed quality standards.</span></p><h3 class="c18" id="h.j7ktoqcjgldy"><span class="c6">Putting data quality metrics into practice</span></h3><p class="c12"><span class="c7">A typical data quality assessment approach might be the following:</span></p><ul class="c3 lst-kix_qxy6lvu5zv36-0 start"><li class="c5 li-bullet-0"><span class="c7">Identify which part of the collected public data must be checked for data quality (usually, information critical to your company&#39;s operations).</span></li><li class="c5 li-bullet-0"><span class="c7">Connect this information to data quality dimensions and determine how to measure them as data quality metrics.</span></li><li class="c5 li-bullet-0"><span class="c7">For each metric, define ranges representing high or low-quality data.</span></li><li class="c5 li-bullet-0"><span class="c7">Apply the criteria of assessment to the data set.</span></li><li class="c5 li-bullet-0"><span class="c7">Review and reflect on the results, and make them actionable.</span></li><li class="c5 li-bullet-0"><span class="c7">Monitor your data quality periodically by running automated checks and having specific alerts in place (e.g., email reports).</span></li></ul><h2 class="c14" id="h.9iuri0a5m1b9"><span class="c17">How web scraping can ensure data quality</span></h2><p class="c12"><span class="c7">As you might know, web scraping is the ultimate way of gathering the needed public data in large volumes and at high speed. But scraping is not only about collecting. It is also about verifying, choosing the most relevant data, and making the existing data more complete. </span></p><p class="c12"><span class="c7">So, how exactly does web scraping ensure data quality?</span></p><p class="c12"><span class="c9">When performing web scraping with high-quality scraping tools, users get the possibility to retrieve timely and accurate public data even from the most complex websites. For instance, </span><span class="c22"><a class="c11" href="https://oxy.yt/upji">Oxylabs&rsquo; E-Commerce Scraper API</a></span><span class="c7">&nbsp;is known for its AI &amp; ML-driven built-in features. These specifications allow the scraper to adjust to website changes automatically and, eventually, gather the most up-to-date data almost effortlessly.</span></p><p class="c12"><span class="c9">Additionally, </span><span class="c22"><a class="c11" href="https://oxy.yt/upji">reliable scraper APIs</a></span><span class="c7">&nbsp;are also powered by proxy rotators, giving you a chance to prevent unwanted blocks, which significantly increases your likelihood of getting all the public data you need and, in turn, satisfying the completeness dimension. </span></p><p class="c12"><span class="c7">Other benefits of web scraping that help improve data quality include:</span></p><ul class="c3 lst-kix_y6mpooj96d66-0 start"><li class="c5 li-bullet-0"><span class="c7">Request tailoring on country or city-level</span></li><li class="c5 li-bullet-0"><span class="c7">Delivering clean and structured data you can rely on</span></li><li class="c5 li-bullet-0"><span class="c7">Collecting data from thousands of URLs for a complete dataset </span></li></ul><h2 class="c14" id="h.ospq23td5pr9"><span class="c17">Let&rsquo;s wrap up</span></h2><p class="c12"><span class="c7">Data is undoubtedly one of the most valuable resources for today&rsquo;s businesses. It presents actionable insights, provides new opportunities, and, if used by companies correctly, allows them to stay on top of the competition. However, data is only useful when it is of high quality. This means that businesses should start paying more attention to tracking the quality of information they use by constantly having a data quality strategy in place. </span></p><p class="c12"><span class="c9">In today&rsquo;s blog post, we provided a detailed explanation of the six data quality dimensions that together define the overall value of assessed data, as well as listed a number of data quality metrics that can be used to measure and track the quality of this data. </span></p></body></html>