Data is only as useful as its accuracy. A small error, say a miscalculation, can make a big difference – impacting your decision-making.
难怪数据质量问题不是在地毯下刷的东西。相反，您需要主动解决质量问题，以便更好，更具数据通知的决策和业务增长。
So, in this soup-to-nuts guide on data quality issues, we’ll bring to light top problems you need to be mindful of and how experts are solving them. In the end, we’ll also share the best solution for resolving data quality issues.
Ready to learn? Here’s the starter, followed by the details:
Why Is Data Quality an Issue?
Essentially, data quality relates to its accuracy, completeness, consistency, and validity.
Now if the quality of data at hand doesn’t align with this definition, you have a data quality issue. For example, if the data sample is incorrect, you have a quality issue. Similarly, if the data source isn’t reliable, you can’t make your decisions based on it.
通过识别数据质量问题并纠正它们，您可以拥有适合使用的数据。如果没有它，您的质量数据差，这些数据比导致更好的弊大于：
- Uninformed decision making
- Inaccurate problem analysis
- Poor customer relationships
- Poor performing business campaigns
然而，百万美元的问题是：数据质量问题是否如此普遍，即他们可以留下这种可怕的影响？
答案：是的。40.7％的专家受访者通过揭示他们经常发现数据质量问题。此外，44.4％偶尔会发现质量问题。只有14.8％的人表示他们很少找到数据质量的问题。
This makes it clear: you need to identify quality issues in your data reporting and take preventative and corrective measures.
Most Common Data Quality Issues in Reporting
我们的专家表示，他们遇到的两大数据质量问题是重复的数据和人为错误 - 每个人的速度为60％。
Around 55% say they struggle with inconsistent formats with 32% dealing with incomplete fields. About 22% also say they face different languages and measurements issue.
With that, let’s dig into the details. Here’s a list of the reporting data quality issues shared below:
- The person responsible doesn’t understand your system
- 人为错误
- Data overload
- Incorrect data attribution
- Missing or inaccurate data
- Data duplication
1. The person responsible doesn’t understand your system
“The most common issue is that the person who created the report made an error because they did not fully understand your system or missed an important filter,” points out Bridget Chebo ofWe Are Working.
Consequently, you are left with report data that is inconsistent with your needs. Additionally, “the data you see isn’t telling you what you think it is,” Chebo says.
As a solution, Chebo advises: “ensure that each field, each automation is documented: what is its purpose/function, when it is used, what does it mean. Use help text so that users can see what a field is for when they hover over it. This will save time so they don’t have to dig around looking for field definitions.”
为此,using reporting templatesis a useful way to help people who put together reports. This kind of documentation also saves you time in explaining what your report requirements are to every other person.
Related:Reporting Strategy for Multiple Audiences: 6 Tips for Getting Started
2. Human error
Another common data quality issue in reports is human error.
为了详细说明，“这是员工或代理制作拼写错误，导致数据质量问题，错误和不正确的数据集，”斯蒂芬咖喱来自CocoSignhighlights.
解决方案？Curry recommends automating the reporting process. “Automation helped me overcome this because it minimizes the use of human effort and can be done by using AI to fill in expense reports instead of giving those tasks to employees. “
Speaking of the potential of automation, Curry writes: “AI can automatically log expenses transactions and direct purchases right away. I also use the right data strategy when analyzing because it minimizes the chances of getting an error from data capture.”
“Having the right data helps manage costs and optimize duty care while having data quality issues make your data less credible, so it’s best to manage them” Curry concludes.
Related:90+ Free Marketing Automation Dashboard Templates
3.数据过载
“我们最常见的数据质量问题具有太多数据，”评论DebtHammer’sJake Hill.
一个沉重的数据量负荷使其无用 - 埋葬所有关键洞察力。要添加，“它可以使其变得非常困难，整理，组织和准备数据，”Notes Hill。
“The longer it takes, the less effective our changing methods are because it takes longer to implement them. It can even be harder to identify trends or patterns, and it makes us more unlikely to get rid of outliers because they are harder to recognize.”
As a solution, the DebtHammer team has “implemented automation. All of our departments that provide data for our reports double-check their data first, and then our automated system cleans and organizes it for us. Not only is it more accurate, but it is way faster and can even identify trends for us.”
Related:Cleanup Your Bad CRM Data Like the Pros Do
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4. Incorrect data attribution
“As someone with experience in the SaaS space, the biggest data quality issue I see with products is attributing data to the wrong user or customer cohort,” outlines Kalo Yankulov from升级.
“For instance, I’ve seen several businesses that attribute the wrong conversion rates, as they fail to use cohorts. We’ve made that mistake as well.
当看着我们的新客户,我们有22 new subscriptions out of 128 trials. This is a 17% trial to paid conversion, right? Wrong. Out of these 22 subscribers, only 14 have started a trial in May and are part of the May cohort. Which makes the trial conversion rate for this month slightly below 11%, not 17% as we initially thought,” Yankulov explains in detail.
Pixoul’s德文郡法达同样挣扎。“在我的工作中，这个问题往往会出现最多的营销订婚指标，因为不同的平台不同地测量这些东西。当我试图在多个平台上衡量广告系列的总体成功时，这一切都有不同的定义，这是一场斗争。“
Now to resolve data incorrect attribution and to prevent it from contributing to wrong analysis in the future, Yankulov shares, “we have been doing our best to implement cohorts across all of our analytics. It’s a challenging but critical part of data quality.”
Related:What Is KPI Reporting? KPI Report Examples, Tips, and Best Practices
5.缺少或不准确的数据
Data inaccuracy can seriously impact decision-making. In fact, you can’t plan a campaign accurately or correctly estimate its results.
andra maraciuc从数据居民shares experience with missing data. “While I was working as a Business Intelligence Analyst, the most common data quality issues we had were: inaccurate data [and] missing data.”
“这两个问题的原因是人为错误。更具体地，来自手动数据输入错误。我们试图额外努力清洁数据，但这还不够。
The reports were always leading to incorrect conclusions.”
“The problem was deeply rooted in our data collection method,” Maraciuc elaborates. “We collected important financial data via free-form fields. This allowed users to type in basically anything they like or to leave fields blank. Users were inputting the same information in 6+ different formats, which from a data perspective is catastrophic.”
Maraciuc adds: “Here’s a specific example we encountered when collecting logistics costs. How we wanted the data to look like: $1000 The data we got instead: 1,000 or $1000, or 1000 USD or USD 1000 or 1000.00 or one thousand dollars, etc.”
So how did they solve it? “We asked our developers to remove ‘free-form fields’ and set the following rules:
- Allow users to only type digits
- 排除特殊字符（$，％，^，*等）
- Exclude text characters
- Add field dedicated to currency (dropdown menu style)
对于缺少的数据，规则被设置为强迫用户不留空字段。“
The takeaway? “Any data quality issue needs to be addressed early on. If you can fix the issue from the roots, that’s the most efficient thing long term, especially when you have to deal with big data,” in Maraciuc’s words.
Related:Google Analytics Data: 10 Warning Signs Your Data Isn’t Reliable
6. Data duplication
AtCocodoc，Alina Clark写道，“数据的重复是在数据分析和我们的业务报告时最常见的质量问题。”
“Simply put, duplication of data is impossible to avoid when you have multiple data collection channels. Any data collection systems that are siloed will result in duplicated data. That’s a reality that businesses like ours have to deal with.”
AtEdoxi., Sharafudhin Mangalad shares they see the same issue. “Data inconsistency is one of the most common data quality issues in reporting when dealing with multiple data sources.
多次，多个数据库中可能出现相同的记录。重复数据创造了数据驱动的业务面部的不同问题，它可能导致收入损失比任何其他问题更快。“
解决方案？“投资数据复制工具是唯一的数据复制的解毒剂，”Clark建议。“如果有的话，试图手动根除重复的数据是过于多大的任务，特别是考虑到这些天收集的数量的数据。
Using a third-party data analytics company can also be a solution. Third-party data analytics takes care of duplicated data before it lands on your desk, but it may be a costly alternative compared to using a tool on your own.”
因此，虽然数据分析工具可能是昂贵的，但它可以节省您的时间和工作。不要忘记，通过消除领先的数据质量问题，它不会为人类错误留下人类错误的空间，并在长时间节省美元。
Get Rid of Data Quality Issues Today
简而言之，数据不一致，不准确，过载和重复是对数据报告质量产生负面影响的一些主要问题。更不用说，人为错误会导致更大的问题。
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