Data is only as useful as its accuracy. A small error, say a miscalculation, can make a big difference – impacting your decision-making.
No wonder data quality issues aren’t things to brush under the rug. Instead, you need to proactively resolve the quality issues for better, more data-informed decisions and business growth.
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?
- Most common data quality issues in reporting
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.
By identifying data quality issues and correcting them, you have data that is fit for use. Without it, you have poor quality data that does more harm than good by leading to:
- Uninformed decision making
- Inaccurate problem analysis
- Poor customer relationships
- Poor performing business campaigns
The million-dollar question, however, is: are data quality issues so common that they can leave such dire impacts?
The answer: yes. 40.7% of our expert respondents confirm this by revealing that they find data quality issues very often. Moreover, 44.4% occasionally find quality issues. Only 14.8% say they rarely find issues in their data’s quality.
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
Our experts say that the top two data quality issues they encounter are duplicate data and human error — a whopping 60% for each.
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
- Human error
- 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 of We 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.”
To this end, using reporting templates is 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.
To elaborate, “this is when employees or agents make typos, leading to data quality issues, errors, and incorrect data sets,” Stephen Curry from CocoSign highlights.
The solution? 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. Data overload
“Our most common data quality issue is having too much data,” comments DebtHammer’s Jake Hill.
A heavy bucket load of data renders it useless – burying all the key insights. To add, “it can make it extremely difficult to sort through, organize, and prepare the data,” 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
PRO TIP: Measure Your Website Content Marketing Performance Like a Pro
To optimize your website’s content for conversion, you probably use Google Analytics to learn how many people are interacting with your site, which pages brought them to the site in the first place, which pages they engage with the most, and more.
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- Sessions by landing page. Which pages do new visitors view first?
- Exits and pageviews by page. Which pages do visitors last view before leaving your website?
Now you can benefit from the experience of our Google Analytics experts, who have put together a plug-and-play Databox template showing the most important metrics for measuring your website content marketing performance. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!
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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 Encharge.
“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.
When looking at our new customers in May, we had 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 Devon Fata struggles similarly. “In my line of work, the issue tends to show up the most in marketing engagement metrics, since different platforms measure these things differently. It’s a struggle when I’m trying to measure the overall success of a campaign across multiple platforms when they all have different definitions of a look or a click.”
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. Missing or inaccurate data
Data inaccuracy can seriously impact decision-making. In fact, you can’t plan a campaign accurately or correctly estimate its results.
Andra Maraciuc from Data Resident 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.”
“The cause for both issues was human error. More specifically, coming from manual data entry errors. We tried to put extra effort into cleaning the data, but that was not enough.
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 $1000, or 1000 USD or USD 1000 or 1000.00 or one thousand dollars, etc.”
So how did they solve it? “We asked our developers to remove ‘free-form fields’ and set the following rules:
- Allow users to only type digits
- Exclude special characters ($,%,^,*, etc)
- Exclude text characters
- Add field dedicated to currency (dropdown menu style)
For the missing data, rules were set to force users to not leave blank fields.”
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
At Cocodoc, Alina Clark writes, “Duplication of data has been the most common quality concern when it comes to data analysis and reporting for our business.”
“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.”
At Edoxi, 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.
Many times, the same records might appear in multiple databases. Duplicate data create different problems that data-driven businesses face, and it can lead to revenue loss faster than any other issue with data.”
The solution? “Investing in a data duplication tool is the only antidote to data duplication,” Clark advises. “If anything, trying to manually eradicate duplicated data is too much of a task, especially given the enormous amounts of data collected these days.
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.”
So while a data analysis tool might be costly, it saves you time and work. Not to forget, it leaves no room for human error and saves you dollars in the long haul by eradicating a leading data quality issue.
Get Rid of Data Quality Issues Today
In short, data inconsistency, inaccuracy, overload, and duplication are some of the leading problems that negatively impact the quality of data reporting. Not to mention, human error can lead to bigger issues down the line.
Want an all-in-one solution that solves these issues without requiring work from your end? Manage reporting via our reporting software.
All you have to do is plug in your data sources. From there, Databox takes on automatic uploading and updating of data from the various sources you’ve linked it to. At the end of the day, you get fresh data in an organized fashion on visually engaging screens.
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- Get buy-in and make data quality an enterprise-wide priority.
- Establish metrics.
- Investigate data quality failures.
- Invest in internal training.
- Establish data governance guidelines.
- Establish a data auditing process.
Mistyping or misspellings are one of the most common sources of data quality errors. Humans are known to make at least 400 errors while doing 10,000 data entries.
Quality Issues are those issues that arise when the Products do not meet the Product specifications and include but are not limited to incorrect packaging, labeling, incorrect revision levels and Purges.
What are Data Quality Solutions? The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing business operations, analytics and emerging digital business scenarios. It covers much more than just technology.
Data that is deemed fit for its intended purpose is considered high quality data. Examples of data quality issues include duplicated data, incomplete data, inconsistent data, incorrect data, poorly defined data, poorly organized data, and poor data security.
Data quality is essential for one main reason: You give customers the best experience when you make decisions using accurate data. A great customer experience leads to happy customers, brand loyalty, and higher revenue for your business.
- Establish a Consistent Reporting Schedule.
- Work on Your Data Visualization.
- Automate Your Data Collection.
- Start With Some Goal Metrics.
- Centralize Your Data.
- Step 1 – Definition. Define the business goals for Data Quality improvement, data owners/stakeholders, impacted business processes, and data rules. ...
- Step 2 – Assessment. ...
- Step 3 – Analysis. ...
- Step 4 – Improvement. ...
- Step 5 – Implementation. ...
- Step 6 – Control.
Relevancy: the data should meet the requirements for the intended use. Completeness: the data should not have missing values or miss data records. Timeliness: the data should be up to date. Consistency:the data should have the data format as expected and can be cross reference-able with the same results.
- Patient frustration and mistreatment. ...
- Employee distrust of critical technology. ...
- Decrease in efficiency and increase in bottlenecks. ...
- Poor and ineffective policy decisions.
Data quality measures the condition of your data, using factors such as accuracy, consistency (in all fields across data sources), integrity (whether the fields are complete), and usability. An exemplary score in all these fields equals high-quality data, the best kind to use for processing and analysis.
- Manual data entry (a.k.a. human error) Manual data entry has little to recommend it. ...
- Lack of complete information. ...
- Transformation errors. ...
- Unmanaged data decay. ...
- Inconsistent data entry standards. ...
- Siloed information/lack of integration. ...
- Poor migration. ...
- Broken processes.