🤖 Meet OnCall AI, our observability copilot that makes troubleshooting easy. Read announcement.

Skip to content

10 Most Common Data Quality Issues You Need to Know

Mar 4, 2024 / 15 minute read

Learn about 10 common data quality issues, and how they impact observability practices.


While there's no such thing as perfect and spotless data, certain factors can exacerbate its imperfections. Even with the best tools, you can still encounter data quality issues. According to Gartner, poor data quality costs organizations around $12.9 million annually. Solving these issues is crucial for a business to improve and succeed.

Data quality is a goal any data-driven business or organization tries to achieve. Failing to do so means getting bad data that causes inaccurate analysis. Meanwhile, high data quality means getting reliable insights that can improve operations.

Identifying data quality issues and remedies is the key to the best analysis. Otherwise, you'll waste resources and even cause more data problems for your business.

In this article, we’ll touch on the top data quality issues that impact most analytics teams. In each section, we’ll also drill into specifics of how they affect observability practices.

🔑 Key Takeaways

  • Data quality issues happen for many reasons, but most come from errors, inconsistencies, and uncontrollable events.

  • Poor data quality due to these issues only wastes resources, so businesses must address them to improve analysis and operations.

  • Performing data checks on accuracy, consistency, completeness, and timeliness can help identify common data quality issues.

  • Edge Delta offers a tool with data pre-processing functions to remove noise, aiding in efficiently detecting and fixing data quality issues.

10 Common Issues that Affect Data Quality

Data quality refers to the metric used to assess the current state of data. With high data quality, you can ensure your information is reliable, complete, and accurate. It's crucial for every data-driven business since it's the key to improving operations.

Obtaining data quality involves doing several types of data checks, with the most common being:

  • Accuracy: the correctness of data and being free from errors

  • Consistency: the uniformity of every data in a set

  • Completeness: the details on data and checks on missing values

  • Timeliness: the relevance of the data for a specific time frame

As you perform these checks, you'll start detecting issues that need fixing. Some of the most common problems come from errors, inconsistencies, and uncontrollable events.

Here are ten of the most common data quality problems, how they happen, and how to deal with them:

Human Error Manual Actions Reshape Data Centrally
Duplicate Data Use of Several Resources Group Together Repetitive Data
Inaccurate Data Errors, Obsolescence, and Data Drifts Reshape Data Centrally
Ambiguous Data Large Database Processing Implement Monitoring Rules
Hidden Data Discarding Data to Contain Costs Analyze Data Upstream
Inconsistent Data Inconsistent Data Handling Create Uniform Formats and Standardize Logs
Data Overload Increase in Digital Experiences Use Filtering Functions
Orphaned Data Use of Several Resources Use Data Quality Management Tools
Data Downtime Major Events and Changes Adopt Redundant Analytics Tooling
Outdated Data Lack of Regular Updates Set Reminders or Schedules for Updates

1. Human Error - Due to Involvement of Manual Actions

Some processes, like data collection, quality management, or extraction, still involve manual actions. These actions are also crucial when testing and validating data. While human action is necessary, it also makes data quality error-prone.

Human errors in data quality usually stem from typographical mistakes. These errors can occur from both sides, whether it's the fault of the customers or the employees. Typical situations of these issues are:

  • Customers or clients writing correct data but placing them in the wrong field

  • Employees making mistakes when handling or migrating data

In observability, a common data quality problem is excessive log verbosity. If your teams set log verbosity too high, it can drive up costs and create a lot of noise. Inversely, if your logs don’t contain enough detail, they provide little analytical value.

Even the Smallest Mistakes Can Lead to Bad Data

Any human error can affect an entire data set and cause more errors. It needs immediate fixing, whether a spelling error or misplaced data. If left unchecked, these errors can lead to a poor analysis once processed or analyzed.

💡 Shape and Transform Logs from a Central Location

In the past, optimizing your logs required significant effort across all data sources. Now, teams can adopt an observability pipeline, which provides a central location to shape data before it’s routed to your streaming destinations.

Using this tool, you can omit unneeded fields to reduce the verbosity of loglines. You can also capture the most relevant parts of your log data and summarize massive volumes. As a result, you can reduce the cost of your observability platform and remove noise.

2. Duplicate Data - Because of Having Several Sources

Data analysis provides better results when users combine data from several reliable sources. However, the merging process also exposes your data set to the risk of duplicates.

Duplicate or redundant data is typical when merging data from many sources. It usually happens during data collection since many sources offer the same data.

In observability, data sources frequently emit repetitive loglines, communicating the same behavior again and again. Redundant log entries are a problem because they consume storage space unnecessarily and make it more challenging to locate relevant information.

Duplicates Can Be a Big Issue for Certain Types of Analysis

Duplicate data doesn't seem as serious as inaccurate or wrong data. However, it can lead to many variations of the same data. This can lead to significant noise within your analytics platform.

💡 Group Together Repetitive Logs into Patterns

A reliable way to reduce duplicate data is pattern analytics. By clustering similar and repetitive loglines together, you can dramatically reduce storage consumption while also retaining tons of analytical value. Moreover, patterns can help you understand the volume of data, whether it's problematic, and more.

3. Inaccurate Data - from Errors, Obsolescence, and Data Drifts

Accuracy is the most crucial aspect to look for when analyzing data. After all, any type of data will only be helpful as long as it's accurate. Otherwise, it will only lead to miscalculations and poor decisions.

Detecting inaccurate data is challenging since it can follow the correct format. However, it can lead to more errors due to false, misspelled, or missing parts. Such a data quality issue comes from these common factors:

  • Human Error - Errors caused by human actions

  • Data Drift - Unexpected or unrecorded change in data structure

  • Stale Data - Obsolete data

Within logging, it’s common to experience configuration errors. For example, you may omit important log events or misconfigure log levels. This results in incomplete or inaccurate data.

Inaccurate Data Leads to Many Potential Problems

If left unchecked, inaccurate data can lead to errors and failures. Even the tiniest of mistakes can cause miscalculations and poor analysis.

The inaccuracy leads to bad data and more problems for the users. It destroys business intelligence efforts and wastes resources spent on the process.

💡 Correct Configuration Errors Centrally

Many engineering teams don’t have time to re-configure the log data on the server side. That’s why observability pipelines can be so helpful in solving this challenge. Pipelines provide a central pane to reshape data, fixing any configuration errors before the data hits your observability platform.

4. Ambiguous Data - Caused by Handling Large Databases

Despite implementing a tight monitoring process, ambiguous data can still slip through. It's a common issue when dealing with large databases or data lakes where streaming is fast.

The issue can happen in the column labels, formatting, and spelling. Due to the high data volume, these issues can continue without anyone noticing.

Unresolved Ambiguous Data Always Results in Poor Analysis

Data ambiguity can affect the quality as it leads to incorrect analysis results. It can also result in wrong findings if ignored. The longer the issue remains, the more errors will come to the data set and the analysis.

💡 Implement Rules in Monitoring Systems to Detect and Resolve Data Ambiguity

The best solution when dealing with ambiguous data is to create and apply rules to detect any issue. You can create auto-generated rules, providing better data pipelines. This method ensures accuracy in real-time analysis and reliable results. You can also use predictive data quality, which works when the issue arises.

5. Hidden Data - When Dealing with Large Data Volumes

Hidden data is a typical data quality issue in organizations that use data silos. These organizations or businesses receive massive amounts of data quickly. Since it's too heavy to process, they only use parts of the collected data for analysis.

Such data can improve business decisions through valuable insights. However, companies don't use them. IBM says around 80% of all data is hidden data.

Moreover, in a recent study, we found that 82% of observability practices limit log data ingestion either often or all the time. In observability, the root cause is the high cost of analytics platforms, causing teams to neglect data that may be helpful in fixing performance or health issues.

It's one of the common data quality problems you must fix. Otherwise, you're missing plenty of opportunities.

Hidden Data Hinders Organizations from Getting the Best Analysis

By having hidden data, an organization misses plenty of opportunities. The quality of the analysis they get still has room for improvement when they analyze all data. As such, they can't discover more ways to improve their service, products, and processes.

💡 Begin Analyzing Data as It’s Created at the Source

You can begin analyzing data upstream – before it’s ingested into your observability platform – in order to gain visibility into new datasets. Distributed machine learning algorithms enable you to derive insight from your data before it leaves your environment. In doing so, you can ingest lightweight analytics in your observability platform versus massive amounts of raw data.

6. Inconsistent Data - Through Having Various Data Processes

Inconsistency is a typical data quality issue, especially when data comes from various sources. Around 55% of data management teams struggle with inconsistent formats. This issue usually happens due to having various ways of handling data. It's also a typical issue encountered during data migration and initial data gathering.

This challenge is also very common in observability. In the absence of clear logging standards and conventions, different developers or teams may adopt their own approaches to log formatting and content. This results in inconsistencies across log entries, which can make it harder to investigate issues later

Inconsistency Creates Challenging Conditions for Analysis

Due to incorrect transformations, some data sets encounter analyzing errors. The lack of uniformity is a must-fix since automated analysis needs to be consistent to run.

Not following a specific format or method when handling data can lead to poor analysis. It will also be challenging to get good results from automated processes. Any script or command will only apply to some data and won't cover the entire set you want to analyze.

💡 Create Specific Rules and Formats to Get Consistent Data

By having one rule and format when gathering data, you'll have a uniform data set. This way, you can easily automate, run scripts, and pass data through tools. This method will also prevent discrepancies in your data.

Make all the necessary transformations before any process and migration. It's also best to do regular checks to find inconsistencies and prevent poor analysis.

When it comes to observability, you should also structure all your logs in a consistent schema for easier analysis downstream.

7. Data Overload - When Analyzing Too Much Data

While having more data means a more accurate analysis, it can sometimes become an issue. The process becomes heavier to run, and it also becomes a burden to analyze.

Data overload means involving large data sets. These data sets usually contain irrelevant, redundant, and unnecessary data or noise. In observability, the pain points caused by data overload range from excessive costs to poor performance.

Data Overload Causes Heavy Strain and Contains Noise

Handling a large data set is overwhelming. It tends to bury crucial insights and mix irrelevant data due to all the noise. It's a heavy task, which can be expensive and ineffective.

Besides the heavy process, finding the necessary information is also challenging. It takes time to analyze the trends and patterns. Moreover, it stops you from detecting outliers and making changes due to the lengthy process.

💡 Use Filters to Remove Noise and Improve Data Analysis

To see better results, you clean your data set by filtering out irrelevant data. You'll also have a more efficient analysis when you organize the filtered set. With this process, you can ensure a complete and relevant set to give you a more accurate analysis.

Additionally, if you begin analyzing data upstream, you can populate your dashboards without ingesting complete datasets. This results in massive cost reduction and better performance.

8. Orphaned Data - Because of Having Several Data Sets

Orphaned data refers to data that fails to present value. You can consider data orphaned if your system fails to support it. It's also orphaned if you can't transform it to be usable in your process.

An example of orphaned data is a record that exists in one database but doesn’t in another. This data type becomes useless as there is no reference for the other database to replicate.

Orphaned Data Skews Data Analysis and Lowers Insight Quality

Orphaned data can become a noise affecting your analysis if you leave it on your dataset. Since it doesn't represent any value, it can skew your process and lower the quality of insights you'll get.

If you want to maximize data, transforming orphaned data takes time. Moreover, the chances of it being usable are low, so it's not worth doing.

💡 Use A Data Quality Management Tool to Detect Orphaned Data

Dealing with orphaned data is accessible with reliable data quality management tools. These tools can detect any discrepancy in a dataset, allowing you to find orphaned data. From there, you can correct the format or remove it if it's impossible to transform.

9. Data Downtime - Caused by Major Events or Changes

Data Downtime happens when data becomes unreliable or unready. It's a problem for companies and organizations relying on data for their operations. This downtime usually happens during events such as:

  • Mergers & Acquisitions

  • Reorganizations

  • Infrastructural Changes

  • Data Migrations

This accessibility issue usually comes in short durations. However, companies still want to prevent it as much as possible. In observability, it’s common for providers to experience outages as well. When this happens, you cannot access your telemetry or monitor your workloads, introducing significant risk.

Downtime Creates Unreliable or Inaccurate Data

Though usually quick, downtime makes data unreliable, erroneous, or inaccurate. It can also cause you to miss or get inaccurate data, leading to poor analytical results and insights. It may also lead to customer complaints.

Data downtime can lead to missing or inaccessible data due to unexpected changes in the network or server. As a result, it is crucial to fix these downtimes as soon as possible and take precautionary measures.

💡 Introduce Redundancy at a Low Cost

You can avoid downtime by introducing secondary tools to analyze your observability data. Here, it’s critical to adopt tools that are low-cost and require little effort to set up. As a result, you can tap into the data you need without introducing complexity.

10. Outdated Data - When Lacking Regular Updates

While many things age well, data isn't one of them. Most data can quickly become outdated, and it's a typical issue in data quality. Analysis will only be relevant and helpful when using up-to-date data.

According to Gartner, at least 3% of data globally decays monthly. Outdated data is a typical issue for businesses with poor strategies and operations. What makes it challenging is that the changes are something only some tools can detect.

Data Decay Causes Misguided Insights and Inaccurate Predictions

When outdated data becomes part of an analysis, it can provide poor insights. Having no updates on data sets will only skew any process you'll do, leading to poor analysis. Later on, this problem will cause incorrect predictions.

It's a crucial issue that needs immediate solutions, especially for data-driven operations. Otherwise, it can cause significant business errors and even affect the customers.

💡 Regular Updates and Data Checks Through Notifications and Reminders

The best way to solve outdated data is through constant updating and reviewing. You can always use tools that can set reminders to do data checks. With such notifications, replacing outdated data with new ones is possible. This way, you can ensure all data you use retains its relevance.

How Edge Delta Can Help in Solving Data Quality Issues

Resolving these common data quality problems is essential for improving data quality. It's part of filtering your data for more efficient observability. Detecting and fixing these issues results in efficient and cost-effective data processing pipelines.

While you can find many tools to detect and fix these issues, Edge Delta offers an effective solution. It has a filter function to help remove noise from your data sets. As a result, you can enjoy a higher data quality. Additionally, Edge Delta runs AI and distributed machine learning on your data at the source. As a result, it pre-sorts the “needles” from your “haystack,” reduces noise, and helps you derive analytical value from all data.

FAQ on Data Quality Issues

What are the common data quality issues in machine learning?

Inaccurate data is the most common data quality issue encountered by machine learning. It involves missing, incorrect, imbalanced, noisy, and overfitting data. These issues affect machine learning, leading to incorrect or poor analysis. Such issues need an immediate fix. Otherwise, they can skew your analysis and give you poor insights.

What three main issues can directly affect data quality during data collection?

Errors are common during data collection. These errors include inaccurate, missing, inconsistent, and redundant data. When your data collection process encounters these issues, your data set becomes unreliable. You must resolve these issues by organizing, cleaning, and proper formatting.

What is the symptom of a data quality problem?

Several signs can help you detect a data quality problem. For instance, some data sets have poor coordination, while some have blank spots. Sometimes, you'll get issues in processing the data or results that seem off. The best way to detect these problems is to use monitoring tools to detect any anomaly. These tools will alert you of issues in your data.

Stay in Touch

Sign up for our newsletter to be the first to know about new articles.