Digital image representing Informatica data quality.

Seth Rao

CEO at FirstEigen

10 Common Data Quality Issues (And How to Solve Them)

Table of Contents
    Add a header to begin generating the table of contents
    Table of Content

      Data quality is essential for any data-driven organization. Poor data quality results in unreliable analysis. High data quality enables actionable insights for both short-term operations and long-term planning. Identifying and correcting data quality issues can mean the difference between a successful business and a failing one.

      What data quality issues is your organization likely to encounter? Read on to discover the ten most common data quality problems—and how to solve them.

      Quick Takeaways

      • Data quality issues can come from cross-system inconsistencies and human error
      • The most common data quality issues include inaccurate data, incomplete data, duplicate data, and aging data
      • Robust data quality monitoring can solve many data quality issues 

      1. Inaccurate Data

      Gartner says that inaccurate data costs organizations $12.9 million a year, on average. Inaccurate data is data that is wrong—customer addresses with the wrong ZIP codes, misspelled customer names, or entries marred by simple human errors. Whatever the cause, whatever the issue, inaccurate data is unusable data. If you try to use it, it can throw off your entire analysis.

      How can you solve the problem of inaccurate data? The first place to start is by automating data entry. The more you can automate, the fewer human errors you’ll find.

      Next, you need a robust data quality monitoring solution, such as FirstEigen’s DataBuck, to identify and isolate inaccurate data. You can then try to fix the flawed fields by comparing the inaccurate data with a known accurate dataset. If the data is still inaccurate, you’ll have to delete it to keep it from contaminating your data analysis.

      2. Incomplete Data

      Another common data quality is incomplete data. These are data records with missing information in key fields—addresses with no ZIP codes, phone numbers without area codes, and demographic information without age or gender entered.

      Incomplete data can result in flawed analysis. It can also make daily operations more problematic, as staff scurries to determine what data is missing and what it was supposed to be.

      You can minimize this issue on the data entry front by requiring key fields to be completed before submission. Use systems that automatically flag and reject incomplete records when importing data from external sources. You can then try to complete any missing fields by comparing your data with another similar (and hopefully more complete data source).

      3. Duplicate Data

      When importing data from multiple sources, it’s not uncommon to end up with duplicate data. For example, if you’re importing customer lists from two sources, you may find several people who were customers of both retailers. You only want to count each customer once, which makes duplicative records a major issue.

      Identifying duplicate records involves the process of “deduplication,” which uses various technologies to detect records with similar data. You can then delete all but one of the duplicate records—ideally, the one that better matches your internal schema. Even better, you may be able to merge the duplicative records, which can result in richer analysis as the two records might contain slightly different details that can complement each other.

      4. Inconsistent Formatting

      Much data can be formatted in a multitude of ways. Consider, for example, the many ways you can express a date—June 5, 2023, 6/5/2023, 6-5-23, or, in a less-structured format, the fifth of June, 2023. Different sources often use different formatting, so these inconsistencies can result in major data quality issues.

      Working with different forms of measurement can cause similar issues. If one source uses metric measurements and another feet and inches, you must settle on an internal standard and ensure that all imported data correctly converts. Using the wrong measurements can be catastrophic—as when NASA lost a $125 million Mars Climate Orbiter because the Jet Propulsion Laboratory used metric measurements and contractor Lockheed Martin Astronautics worked with the English system of feet and pounds.

      Solving this issue requires a data quality monitoring solution that profiles individual datasets and identifies these formatting issues. Once identified, it should be a simple matter of converting data from one format to another.

      5. Cross-System Inconsistencies

      Inconsistent formatting is often the result of combining data from two different systems. It’s common for two otherwise-similar systems to format data differently. These cross-system inconsistencies can cause major data quality issues if not identified and rectified.

      You need to decide on one standard data format when working with data from multiple sources. All incoming data must then convert to that format, which can require using artificial intelligence (AI) and machine learning (ML) technologies that can automate the matching and conversion process.

      Wish to transform how your organization trusts and acts on data?

      6. Unstructured Data

      While much of the third-party data you ingest will not conform to your standardized formatting, that might not be the worst problem you encounter. Some of the data you ingest may not be formatted at all.

      Key differences between structured and unstructured data.

      Image Source: Internet

      This unstructured data can contain valuable insights but doesn’t easily fit into most established systems. To convert unstructured data into structured records, use a data integration tool to identify and extract data from an unstructured dataset and convert it into a standardized format for use with your internal systems.

      7. Dark Data

      Hidden data, sometimes known as dark data, is collected and stored by an organization that is not actively used. IBM estimates that 80% of all data today is dark data. In many cases, it’s a wasted resource that many organizations don’t even know exists—even though it can account for more than half of an average organization’s data storage costs.

      Dark data costs more to store than regularly-used data.

      Image Source: Internet

      Dark data should either be used or deleted. To do either requires identifying this confidential data, evaluating its usability and usefulness, and making that data visible to key stakeholders in your organization.

      8. Orphaned Data

      Orphaned data isn’t hidden. It’s simply not readily usable. In most instances, data is orphaned when it’s not fully compatible with an existing system or not easily converted into a usable format. For example, a customer record that exists in one database but not in another could be classified as an orphan.

      Data quality management software should be able to identify orphaned data. Thus identified, the cause of the inconsistency can be determined and, in many instances, rectified for full utilization of the orphaned data.

      9. Stale Data

      Data does not always age well. Old data becomes stale data that is more likely to be inaccurate. Consider customer addresses, for example. People today are increasingly mobile, meaning that addresses collected more than a few years previous are likely to reflect where customers used to live, not where they currently reside.

      Older data needs constant culling from your system to mitigate this issue. It’s often easier and cheaper to delete data past a certain expiration date than to deal with the data quality issues of using that stale data.

      10. Irrelevant Data

      Many companies capture reams of data about each customer and every transaction. Not all of this data is immediately useful. Some of this data is ultimately irrelevant to the company’s business.

      Capturing and storing irrelevant data increases an organization’s security and privacy risks. It’s better to keep only that data of immediate use to your company and either delete or not collect in the first place data of which you have little or no use.

      Identify and fix 95% of data errors before they impact your business

      Use DataBuck to Solve Your Data Quality Issues

      When you want to solve your organization’s data quality issues, turn to FirstEigen. Our DataBuck solution uses AI and ML technologies to automate more than 70% of the data monitoring process. DataBuck identifies and fixes inaccurate, incomplete, duplicate, and inconsistent data, which improves your data quality and usability.

      Contact FirstEigen today to learn more about solving data quality issues.

      Check out these articles on Data Trustability, Observability & Data Quality Management-

      FAQs

      What are common data quality issues that organizations face?

      Common data quality issues include inaccurate data, incomplete data, duplicate records, inconsistent formatting, cross-system inconsistencies, unstructured data, dark data, orphaned data, stale data, and irrelevant data. Each of these issues can impact data accuracy and reliability, leading to challenges in making data-driven decisions.

      How can data quality issues affect business performance?

      Poor data quality can lead to incorrect analysis, resulting in misguided decisions, operational inefficiencies, and lost revenue. It also impacts customer satisfaction and compliance efforts, especially in industries that require precise data for regulatory reporting and strategic planning.

      How do you identify data quality issues in a dataset?

      Identifying data quality issues involves using data quality monitoring tools that can detect inconsistencies, missing information, duplicate records, and outdated entries. Profiling datasets and setting up regular audits are also effective ways to spot and resolve data quality issues early.

      Can automation help prevent data quality issues?

      Yes, automation is highly effective for reducing data quality issues. Automated data entry minimizes human error, while machine learning (ML) and artificial intelligence (AI) solutions like FirstEigen’s DataBuck can monitor data quality continuously, identifying and correcting issues before they impact analysis.

      What causes data quality issues in an organization?

      Data quality issues often arise from human errors, system incompatibilities, outdated entries, inconsistent formatting, and data integration challenges when importing from multiple sources. Lack of data management protocols and automated monitoring further contributes to data quality degradation.

      What are examples of data quality issues?

      Examples include misspelled customer names (inaccurate data), missing ZIP codes (incomplete data), customers listed multiple times in a CRM (duplicate data), various date formats in a dataset (inconsistent formatting), and old addresses that no longer apply (stale data).

      How can an organization fix data quality issues?

      Organizations can fix data quality issues by implementing data governance policies, automating data entry, using data quality monitoring solutions like DataBuck, and establishing standardized data formats. Regular data audits and deduplication can also address quality issues, improving overall data accuracy.

      Why is data monitoring important for maintaining data quality?

      Data monitoring is essential to detect errors, inconsistencies, and outdated information early. Automated tools monitor data quality in real time, identifying issues like inaccuracies, duplications, and stale data, which helps maintain clean and usable data for analysis and decision-making.

      Discover How Fortune 500 Companies Use DataBuck to Cut Data Validation Costs by 50%

      Recent Posts

      Data Quality with DataBuck
      Seamless Teradata to Databricks Migration: How to Tackle Challenges and Ensure Data Quality With DataBuck
      Data migration is one of those projects that often sounds straightforward—until you dive in and start uncovering ...
      Data Trustability Shapes Acquisition Outcomes
      How Data Trustability Shapes Acquisition Outcomes: The Veradigm Deal
      In recent reports, McKesson (NYSE: MCK) and Oracle (NYSE: ORCL) have emerged as key players in the ...
      Challenges With Data Observability
      Challenges With Data Observability Platforms and How to Overcome Them
      Core Differences Between Data Observability Platforms and DataBuck Many organizations that initially embraced data observability platforms are ...

      Get Started!