6 Data Quality Issues in Reporting and Best Practices to Overcome Them | Databox Blog (2022)

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.

6 Data Quality Issues in Reporting and Best Practices to Overcome Them | Databox Blog (2)

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.

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6 Data Quality Issues in Reporting and Best Practices to Overcome Them | Databox Blog (3)

With that, let’s dig into the details. Here’s a list of the reporting data quality issues shared below:

  1. The person responsible doesn’t understand your system
  2. Human error
  3. Data overload
  4. Incorrect data attribution
  5. Missing or inaccurate data
  6. 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.”

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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.

You may have to navigate multiple areas and reports within Google Analytics to get the data you want though. Now you can quickly assess your content performance in a single dashboard that monitors fundamental metrics, such as:

  1. Pageviews by page, city and country. Where are your visitors located?
  2. Goal completions by landing page. Which pages receive the most traffic and convert the best?
  3. Bounce rate by page title. Which pages encourage visitors to read further?
  4. Sessions by landing page. Which pages do new visitors view first?
  5. Exits and pageviews by page. Which pages do visitors last view before leaving your website?

And more…

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:

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  • 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.

So what are you waiting for? Gather, organize, and use data seamlessly – sign up for Databox today for free.

FAQs

What are the 6 dimensions of data quality? ›

Information is only valuable if it is of high quality. How can you assess your data quality? Data quality meets six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Read on to learn the definitions of these data quality dimensions.

What are the data quality best practices? ›

These data quality best practices will help make sure your data stays on the right track:
  • 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.

What are some of the common data quality issues when dealing with data What can be done to avoid them or to mitigate their impact? ›

Here are five common problems you need to look out for, and how you can avoid them.
...
5 Data Quality Problems and their Solutions
  • Duplicated data. Duplicated data is an issue every business will have to deal with. ...
  • Inconsistent formats. ...
  • Incomplete information. ...
  • Multiple units and languages. ...
  • Inaccurate data.
13 Nov 2018

What are examples of data quality issues? ›

In this blog post, let's discuss some of the most common data quality issues and how we can tackle them.
  • Duplicate data. ...
  • Inaccurate data. ...
  • Ambiguous data. ...
  • Hidden data. ...
  • Inconsistent data. ...
  • Too much data. ...
  • Data Downtime.
9 Sept 2022

What is data quality with example? ›

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.

What are the six 6 characteristics that makes a good data model define each? ›

The seven characteristics that define data quality are:
  • Accuracy and Precision.
  • Legitimacy and Validity.
  • Reliability and Consistency.
  • Timeliness and Relevance.
  • Completeness and Comprehensiveness.
  • Availability and Accessibility.
  • Granularity and Uniqueness.
26 Jan 2017

What is data quality and why is it important? ›

Data quality is defined as: the degree to which data meets a company's expectations of accuracy, validity, completeness, and consistency. By tracking data quality, a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

How do you improve data reporting? ›

5 Ways to Improve Your Reporting Performance
  1. Establish a Consistent Reporting Schedule.
  2. Work on Your Data Visualization.
  3. Automate Your Data Collection.
  4. Start With Some Goal Metrics.
  5. Centralize Your Data.
15 Apr 2022

How can you improve the quality of information? ›

There are three key steps you can take to improve the quality of information in your business:
  1. Determine information purpose and priority by aligning information to one or more of the process elements in your core business process. ...
  2. Agree one version of the 'truth'. ...
  3. Define information governance and assign ownership.
5 Feb 2021

How do you ensure data quality and integrity? ›

8 Ways to Ensure Data Integrity
  1. Perform Risk-Based Validation.
  2. Select Appropriate System and Service Providers.
  3. Audit your Audit Trails.
  4. Change Control.
  5. Qualify IT & Validate Systems.
  6. Plan for Business Continuity.
  7. Be Accurate.
  8. Archive Regularly.

What are the most common data quality issues in reporting? ›

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.

What are the different types of data issues? ›

Data quality issues can stem from duplicate data, unstructured data, incomplete data, different data formats, or the difficulty accessing the data. In this article, we will discuss the most common quality issues with data and how to overcome these.

What is data quality solutions? ›

Gartner defines data quality solutions as “the processes and technologies for identifying, understanding and correcting flaws in data that support effective data and analytics governance across operational business processes and decision making.”

What is a quality issue? ›

What is a Quality Issue? Throughout the process of manufacturing, issues may occur that affect production. The end result of these issues are defects, deficiencies, or significant variations in the final product's expected performance or appearance.

What are the issues in data management? ›

Data Management Problems & Solutions
  • Keeping Systems Synced. One of the first problems companies may face regarding data management is keeping different systems synced. ...
  • Comparing Apples to Oranges. ...
  • Duplicate Data Entry & Queries. ...
  • Underutilizing Data. ...
  • Incorrect Data. ...
  • Security Challenges.
1 Dec 2021

What is the impact of poor data quality? ›

These impacts include customer dissatisfaction, increased operational cost, less effective decision-making and a reduced ability to make and execute strategy. More subtly perhaps, poor data quality hurts employee morale, breeds organizational mistrust, and makes it more difficult to align the enterprise.

What is data quality skills? ›

Data Quality Analyst Skills

They use their knowledge of data quality standards, data sources and data structures to identify problems and create solutions. Data analysis requires attention to detail, as data quality analysts must be able to identify and correct even minor issues.

What are the benefits of data quality? ›

Improved data quality leads to better decision-making across an organization. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and can result in consistent improvements in results.

What is the first and most significant part of data quality? ›

The first is completeness or a measure of whether or not we have all the data we expect to have.

What are 6 qualities of information? ›

Good quality information is:
  • Relevant. Information obtained and used should be needed for decision-making - it doesn't matter how interesting it is. ...
  • Up-to-date. Information needs to be timely if it is to be actioned. ...
  • Accurate. ...
  • Meeting user needs. ...
  • Easy to use and understand. ...
  • Worth the cost. ...
  • Reliable.
22 Mar 2021

What are the six characteristics of quality? ›

  • Accuracy.
  • Validity.
  • Reliability.
  • Timeliness.
  • Relevance.
  • Completeness.

What is the most important dimension of data quality? ›

5. Uniqueness. This dimension indicates if it is a single recorded instance in the data set used. Uniqueness is the most critical dimension for ensuring no duplication or overlaps.

Why data quality tools are important? ›

Data Quality tools can help to make data more trustworthy and more manageable. Inaccurate data promotes poor decision-making, missed opportunities, and lower profits. As use of the cloud continues to grow and become more complex, Data Quality has become a critical issue.

What are data quality tools? ›

Data quality tools are the processes and technologies for identifying, understanding and correcting flaws in data that support effective information governance across operational business processes and decision making.

What are the 5 data qualities? ›

There are five traits that you'll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more.

What is accuracy in data quality? ›

Data accuracy refers to error-free records that can be used as a reliable source of information. In data management, data accuracy is the first and critical component/standard of the data quality framework.

Why is data reporting important? ›

Why is data reporting important? Data provides a path that measures progress in every area of our lives. It informs our professional decisions as well as our day-to-day matters. A data report will tell us where to spend the most time and resources, and what needs more organization or attention.

How do you manage a report? ›

Complete steps on how to prepare a Management Report
  1. Step 1: Plan before you start. ...
  2. Step 2: Invest in automated tools. ...
  3. Step 3: Use clear and objective language. ...
  4. Step 4: Tell a story to engage readers. ...
  5. Step 5: Define the metrics and KPIs to be used. ...
  6. Step 6: Establish a point of comparison.
4 Mar 2022

What is a reporting process? ›

Reporting can also be understood as the process of presenting the results of a series of research and analysis. All reports address some specific goal. They are structured to meet reader's expectations and deliver accurate and objective content.

How do you know if data is accurate? ›

How to measure accuracy and precision
  1. Average value = sum of data / number of measurements.
  2. Absolute deviation = measured value - average value.
  3. Average deviation = sum of absolute deviations / number of measurements.
  4. Absolute error = measured value - actual value.
  5. Relative error = absolute error / measured value.
29 Sept 2021

How do you ensure accuracy reporting? ›

How to Ensure Your Business Has Accurate Reporting
  1. Eliminate Inaccurate Data Sources. ...
  2. Improve Data Accuracy by Setting Data Quality Goals. ...
  3. Avoid Overloading Data Entry Pressure. ...
  4. Review Data Analytics. ...
  5. Improve Data Quality by Automating Error Reports. ...
  6. Ensure Data by Adopting Accuracy Standards.

What are rules that help ensure the quality of data? ›

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.

What is data quality analysis? ›

Data quality analysis is the final step in the data understanding stage in which the quality of data is analyzed in the datasets and potential shortcomings, errors, and issues are determined. These need to be resolved before analyzing the data further or starting modeling efforts.

What is data quality solutions? ›

Gartner defines data quality solutions as “the processes and technologies for identifying, understanding and correcting flaws in data that support effective data and analytics governance across operational business processes and decision making.”

How do you implement data quality? ›

Data Quality – A Simple Six-Step Process
  1. Step 1 – Definition. Define the business goals for Data Quality improvement, data owners/stakeholders, impacted business processes, and data rules. ...
  2. Step 2 – Assessment. ...
  3. Step 3 – Analysis. ...
  4. Step 4 – Improvement. ...
  5. Step 5 – Implementation. ...
  6. Step 6 – Control.
6 Mar 2017

What are some data quality issues in healthcare? ›

To be useful, data must be correct, complete, reliable, and accurate. Flawed data leads to errors in decision-making, lethal mistakes in patient care (such as making a wrong diagnosis, or making a correct diagnosis on the wrong patient), skewed numbers in research, and other critical problems.

Why is data quality so important? ›

Data quality is important because we need: accurate and timely information to manage services and accountability. good information to manage service effectiveness. to prioritise and ensure the best use of resources.

What is data quality tools? ›

Data quality tools are the processes and technologies for identifying, understanding and correcting flaws in data that support effective information governance across operational business processes and decision making.

How is data quality measured? ›

So, how do I measure data quality?
  1. Completeness. Completeness is defined by DAMA as how much of a data set is populated, as opposed to being left blank. ...
  2. Uniqueness. This metric assesses how unique a data entry is, and whether it is duplicated anywhere else within your database. ...
  3. Timeliness. ...
  4. Validity. ...
  5. Accuracy. ...
  6. Consistency.

How do you improve data reporting? ›

5 Ways to Improve Your Reporting Performance
  1. Establish a Consistent Reporting Schedule.
  2. Work on Your Data Visualization.
  3. Automate Your Data Collection.
  4. Start With Some Goal Metrics.
  5. Centralize Your Data.
15 Apr 2022

What are the most common data quality issues in reporting? ›

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.

Why do we need to maintain the quality of data in healthcare? ›

Why does the quality of hospital data matter? Regardless of how the data is used, the better the data, the more valuable it is. Good-quality data not only helps patients receive better care, it makes for better research and analysis too.

Why is quality data so important in healthcare? ›

In the primary healthcare setting, poor quality data can lead to poor patient care, negatively affect the validity and reproducibility of research results and limit the value that such data may have for public health surveillance.

What methods do you use to check data for accuracy and avoid errors? ›

Following that, here are some suggestions to ensure data entry accuracy:
  1. Concentrate on Data That Is Useful. ...
  2. Examine and Analyze Errors. ...
  3. Standardize Processes. ...
  4. Introduce Smart Automation tools like Machine Learning. ...
  5. Observe and Provide Feedback.
10 Jul 2021

Why is data quality important in discovering new knowledge and decision-making? ›

Improved data quality leads to better decision-making across an organization. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and can result in consistent improvements in results.

What is the impact of poor data quality? ›

These impacts include customer dissatisfaction, increased operational cost, less effective decision-making and a reduced ability to make and execute strategy. More subtly perhaps, poor data quality hurts employee morale, breeds organizational mistrust, and makes it more difficult to align the enterprise.

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