Data quality indicates how accurate and reliable a data set is.
Factors like the use case, age, completeness, and consistency of data determine its quality.
Data professionals spend an average of 40% of their workday on data quality, according to a study conducted by Monte Carlo. This focus is because high-quality data can generate impactful insights, while bad data can hinder companies and even actively hurt them.
Businesses need systems to overcome data quality issues to fuel data-driven decision-making. But first, they must understand the data issues they are facing and why they occur.
In this article, we've listed 15 of the most common data problems for organizations and provided five ways to solve them.
A data quality issue is any error that lowers the reliability and accuracy of a data set. Inconsistent formatting and incomplete data cause poor interpretation, hinder analysis, and hurt brands.
Data quality significantly impacts data analysis, business intelligence, and day-to-day operations.
Each data team has its framework for data quality, and problems are unavoidable when data is collected from multiple sources at varying times.
An example of how poor data quality hurts businesses might be a misspelling of a customer's name during data collection. This issue will cause the customer to be addressed incorrectly in calls, emails, and other communications. The business loses revenue when this frustrated customer cancels their orders or services.
Similar inaccuracies or data quality problems can lead to flawed business decisions, lost opportunities, increased operational costs, underperforming machine learning or artificial intelligence, and a lack of competitiveness.
In a 2022 survey by Ataccama, 97% of respondents considered the roles, processes, tools, and platforms for ensuring high-quality data to be important.
Data quality management starts with understanding why data issues occur. Here are the top problems that could corrupt your data.
Businesses collect data from multiple internal applications, customer-facing platforms, and databases. With the number of data sources consistently rising, collecting the same data from separate sources is a common error.
Duplicate data in one crucial database can harm every process associated with it. For example, multiple copies of customer data can skew marketing campaign analytics, sales analytics, customer engagement metrics, and more.
To avoid duplicate data, companies must have a robust data management system that scans new data and alerts engineers when the same data is entered more than once, so they can investigate. Implementing dynamic data integration pipelines can also help.
Employing data matching algorithms in the data ingestion stage can be used to merge existing duplicate records.
Failures during the ETL process can lead to incomplete data sets with missing records in key areas. Human error, offline source systems, and pipeline failures are common scenarios where the data syncing process is incomplete.
Missing data affects all downstream processes and analytics.
To avoid incomplete data collection, you need to set up a monitoring system for your data pipelines that ensure all data is synced and notify data engineers if any issues occur during the integration process.
Given the number of data sources businesses use, many of the data assets they collect will be unstructured. Pictures, documents, videos, and emails are examples of unstructured data.
Unstructured data is not in a pre-defined format and contains many data types stored in different formats. Data analysts can't use this to gain insights.
Data engineers must transform existing data parameters so that data analytics and business intelligence tools can aggregate them.
Inconsistent data results from handling data from each source differently. For example, customer information collected from a CRM and a marketing application is stored differently.
Data inconsistencies can also occur during data migration.
Data teams must implement a uniform data storage and management system across all data pipelines to avoid discrepancies.
While using multiple languages may be great for connecting with customers, it leads to data capture in different languages. This data might not be accurate or useful for overall analytics.
For example, South Korean citizens put their last names before their first names. So, if a U.S. company operating in South Korea has a customer named "Park Jimin," their information must be stored as "Jimin Park" in the global database.
Translation may be another consideration for data teams, as many analytics and business intelligence tools might not recognize multiple languages.
Differences like this degrade data quality and lead to underperforming marketing and engagement initiatives. To mitigate this, data engineers must create models for regional inconsistencies --- a process known as internationalization.
Having too much data to organize and analyze is one of the most common issues businesses face in their data strategy.
When analysts have to sort through large amounts of secondary data to get what they need, analysis is much slower, and companies can fail to capitalize on current trends and patterns.
Key data could also be buried under less vital information, leading to low-value conclusions that don't help organizations.
Building a modern data stack can help with this data quality issue by streamlining an organization's data collection processes and reporting.
Different measurement units that vary from region to region can lead to poor-quality data similar to language inconsistencies.
For example, most countries use the metric system for measurements, while the U.S. does not. So, if inventory records for a warehouse in England are in liters, a U.S. business must convert them to gallons for aggregate analysis.
Data models can be implemented for standardized conversions between imperial and metric values.
Large databases can encounter errors through ambiguous or unclear data. A simple spelling error or poorly-labeled column can lead to incorrect analysis and disrupt data science findings when undetected.
Ambiguous data can become an overwhelming problem when unsupervised in high-speed real-time data ingestion use cases, like data streaming.
Clarity is crucial for accurate data management. Data engineers can create rules and monitoring systems to ensure clear and accurate data sets.
Data silos in large organizations can lead to hidden or orphaned data. This leads to missed opportunities and poor analysis.
For example, data collected from the sales team about Customer A is not present in the CRM system. In this scenario, the data pipelines from these two systems will have varying information leading to an incomplete customer profile.
Spotting hidden data errors requires implementing a data quality rule that checks for consistency during ingestion.
Inaccurate data can derail business intelligence efforts. Accuracy is the foundation of all data engineering.
Many factors, including stale data, human error, and data drift, can cause inaccuracies. Most of these problems can be resolved via monitoring and data quality tools.
Data downtime is when one or more data sets cannot be accessed or are inaccurate. This typically happens for short durations during data migration, infrastructure upgrades, mergers, and acquisitions.
When data is unavailable, it could result in complexities like data not being loaded or incorrect analysis due to missing data.
Automated ETL pipelines with custom connectors can accelerate data migration and reduce downtimes. Setting up monitoring mechanisms can also be used to track and fix this data problem.
Human mistakes such as typos, using different formats, and incomplete or incorrect data entry can lead to inaccurate data that can result in erroneous conclusions.
The best way to avoid human error is to identify and correct the root cause. One way to do this is with data validation. For example, if employees consistently enter currencies in different formats, you can create data collection forms that only accept one format.
Data validation is the process of checking source data for accuracy, completeness, and quality before accepting the data in the first place. It involves creating a system of rules that help cleanse data before it is used.
With a proactive data validation system, data teams can deal with recurring data problems that add to their workloads and take their focus away from crucial tasks.
Data collected and stored in many formats can lead to problems during aggregation and analysis. Storing different formats can lead to errors in data warehouse storage.
Without uniform data format guidelines, the information ingested into data pipelines will include different types of data and conflicting formats that can halt analysis.
Standardizing data within the data pipeline, or at least before analysis, can help resolve this issue.
A major data quality issue is data records being attributed to the wrong source, user, or target persona. It can lead to confusion or inaccurate results.
If a marketing team wrongly attributes conversion rates to Group B instead of Group A, it could ruin future campaigns as it alters how they target different buyer groups.
Incorrect attribution also affects overall sales and marketing analytics, negatively impacting the organization.
Data management and organization can prevent wrong attributions and incorrect analyses.
You can solve data problems by cleaning existing data. You can also implement additional standards, validations, and guidelines to improve the data as it's collected and sanitized moving forward.
Here are five steps to resolve data issues:
Implement a data governance framework with clear and uniform guidelines related to data policies, data quality, and overall data standards.
Use this framework as a single source of truth for your data team and help them navigate data problems from different sources.
Auditing or "profiling" examines data and identifies inconsistencies at the data source or during the ETL process.
Cleansing applies changes to address the issues found during profiling.
Standardizing the data and eliminating inconsistencies can significantly improve data quality.
Data validation checks for completeness and any factors affecting accuracy and reliability.
Data validation processes can inspect data types, codes, format, uniqueness, and consistency.
Including a validation process in your automated data pipelines can speed up time-to-insights and reduce errors.
Include a monitoring and recovery system in your ETL pipelines to quickly spot and address data problems.
A monitoring process can immediately alert data engineers when an error occurs. Depending on your data integration platform and the specific issue, monitoring, and automation can be used to solve problems in a few clicks.
Data monitoring systems can be automated to create self-healing data pipelines that solve problems, at least minor ones, without human intervention.
Since many data problems result from human error, training users to improve their data literacy can boost quality.
When users understand how a current data process works, the tools, the formats to be used, attribution, and more, they are more likely to know how their contribution impacts all stakeholders.
Training and education can encourage users to be more careful during data entry and modification.
Data quality is essential for businesses that rely on big data. Better-quality data can improve insights and help deliver changes that move data-driven organizations closer to their goals.
Most data quality problems can be prevented or reduced using proactive measures like validation, standards, and adequate training when human error is involved.
If you see data quality issues due to needing to collect from the most critical data sources, Portable can help. Set up automated data pipelines easily between 300+ data sources and popular data warehouses.