New tools to improve performance
Masooma Memon on June 21, 2023 • 13 minute read
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:
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:
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.
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 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
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
“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
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“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
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:
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
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.
Hidden data refers to valuable information that is not being harnessed by the organization. This information often holds the potential to optimize processes and provide insights, yet remains unexploited, typically due to a lack of coherent and centralized data collection strategy within the company.
The accumulation of hidden data isn’t just a missed opportunity – it also reflects a lack of efficiency and alignment in a company’s data strategy. It can lead to inaccurate analytics, misguided decisions, and the underutilization of resources, impacting the overall data quality and integrity.
The best way to fix this problem is to centralize data collection and management. By bringing all data together in one place, a company can fully use its data, improving its quality and usefulness and helping to make better decisions.
Data, much like perishable goods, has a shelf-life and can quickly become obsolete, resulting in what is known as ‘data decay’.
Having data out of sync with reality can compromise the quality of the data and lead to misguided decisions or inaccurate predictions.
The problem of outdated data isn’t just an accuracy concern – it also reflects a lack of routine maintenance in a company’s data management strategy. The consequences can extend to poor strategic planning, inefficient operations, and compromised customer relationships, all of which can negatively impact business outcomes.
The most effective solution to prevent data decay is regularly reviewing and updating your data. By setting reminders to perform routine data checks, you can ensure your data remains fresh and relevant, enhancing its reliability and usefulness in decision-making processes. This proactive approach to data management can significantly improve data quality, leading to more accurate insights and better operational efficiency.
Incomplete data refers to data sets that lack specific attributes, details, or records, creating an incomplete picture of the subject matter.
For a company’s data to provide genuine value, it must be accurate, relevant, and most importantly, complete. Ensuring data completeness requires a robust data management strategy that includes meticulous data collection, regular validation, and constant upkeep.
By implementing these practices, you can maintain the quality of your data, resulting in more reliable insights and effective decision-making.
Ambiguity in data refers to situations where information can be interpreted in multiple ways, leading to confusion and misunderstanding. This often occurs when data is improperly labeled, unstandardized, or lacks context, causing it to be unclear or open to various interpretations.
Companies should strive for clarity and consistency in data collection and management practices. This involves ensuring data is accurately labeled, properly contextualized, and adheres to standardized formats. Regular data audits can also be helpful in identifying and rectifying instances of ambiguity.
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.
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