8 Common Data Quality Issues & Expert Solutions to Overcome Each

How a Data Quality Issue Impacts Businesses?

We all know the consequence of poor-quality data is reduced operational efficiency. However, how specifically do data quality issues affect businesses? There are many types of data quality issues. That is also the reason why the negative impact of each data case is different. Below are some common consequences that every data team suffers from miss data management, resulting in data decay.

• Missed business opportunities: A business misses potential sales opportunities by accessing an already outdated customer file, for example.
• Increased costs: The costs of data issue solving, rework processes, redo projects, customer complaints, legal action, etc., can all create a financial loss.
• Poor decision-making: Data issues cause businesses to extract unactionable insights, thereby resulting in misguided strategies and poor business decisions.
• Compliance risks: Inaccurate data may affect user privacy and even violate CCPA regulations and NDA agreements on confidential information.
• Dissatisfied customer experiences: Customers receive different pricing information from different sources, for example, which may cause frustration.

8 Common Data Quality Issues and Their Solutions

Collected data is never perfect. No matter how advanced data quality tools and automated solutions are used, technology alone cannot thoroughly solve your company’s data quality problem. Surely in data pipelines, businesses have at least once encountered the data quality issues listed below. Let’s review the top eight data matters and see how experts fix data quality issues.

Duplicate Data

Duplicate data reflects a specific system or database that stores multiple variations of the same data record or same information. Some common causes of data duplication include data being re-imported multiple times, data not being properly decoupled in data integration processes, gaining data from multiple data sources, data silos, etc.

For instance, if an auction item is listed on an auction website twice, it may negatively affect both potential buyers and the website’s credibility. If duplicate records exist, this issue can lead to wasted storage space and increase the probability of skewed analytical results.

Here are some expert solutions for the issue:

• Establish data governance frameworks which may include data entry and storage guidelines.
• Use data validation checks before entering data sets into the system.
• Use unique identifiers to data elements like customers, items, products, etc.
• Use data duplication software to identify quality issues and remove them from systems.
• Involve in data cleaning processes manually.

Irrelevant Data

Many organizations believe that capturing and storing every customer’s data will benefit them at a certain point in time. However, that’s not necessarily the case. Because the amount of data is massive and not all are useful immediately, businesses may face the irrelevant data quality issue instead. If stored for a long time, irrelevant data will quickly become outdated and lose its value while burdening IT infrastructure and consuming the management time of data teams.

For example, relevant data such as job titles or marital status do not provide any valuable insights into a company’s product sales trends; otherwise, it is distracting in the process of analyzing critical data elements.

Here are some expert solutions for the issue:

• Define data requirements, such as data elements, sources, etc., for a project.
• Use filters to remove irrelevant data from large data sets.
• Select and use the right data resources that are related to the project.
• Use data visualization to highlight relevant patterns.

Unstructured Data

Unstructured data can be considered a data quality issue due to many factors. As unstructured data refers to any type that does not organize to a particular data structure or model, such as text, audio, image, etc., it can be challenging for businesses to store and do data analysis.

As with other types of raw information, unstructured data comes from multiple sources and can include duplicates, irrelevant, or error information. Extracting unstructured data into meaningful insights is not easy as they require specialized tools and integration processes. This is no longer a matter of cost but a matter of both expertise and data analyst hiring.

Businesses can prioritize structured data over unstructured ones if there are not enough necessary capabilities and resources to handle such potential data. However, before removing unstructured data assets from the database, carefully calculate the difference between the investment costs and the hidden benefits.

Here are some expert solutions for the issue:

• Leverage automation and technologies like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP).
• Hire and train personnel with specialized skills in data management and analysis.
• Establish data governance policies to guide data management practices across the company.
• Use data validation checks to limit the entry of unstructured data.

Data Downtime

Data downtime refers to the period when data is not ready or even unavailable and inaccessible. When data downtime occurs, organizations and customers lose the ability to connect to the information they need. This inadvertently disrupts the needs of these audiences and leads to poor analytical results and customer complaints.

Some common factors that cause data downtime can vary due to the state of the management system, such as unexpected changes in schema, migration issues, technical problems, and network or server failures. To get data back to the system and avoid data downtime, a data engineer needs to spend time updating and assuring the quality of the data pipelines. The longer it takes to maintain and store data, the more potential resources the business spends, which negatively affects customer trust.

Here are some expert solutions for the issue:

• Implement redundancy and failover mechanisms, such as backup servers, load balancing, etc., to ensure critical data is always available.
• Conduct regular maintenance and updates before data downtime occurs.
• Monitor data pipeline performance, and network bandwidth, for example, to identify potential issues.
• Automate data management process by implementing validation and verification process, etc.

Inconsistent data

Because data is gained from many different sources, mismatches in the same information across sources are inevitable. This condition is collectively known as “inconsistent data.” The data inconsistencies arise due to many factors like manual data entry errors by human error, inefficient data management practices, etc. Among them, there is a reason you may not have thought of - unit and language differences.

The way a date is represented is a close example. Depending on the format requirements of that source, a date can be expressed in many different ways, such as April 14, 2023, 14/04/2023, 04-14-2023, etc. There is no wrong date format in this case. However, it seriously affects the data quality.

Regardless of the cause and format, inconsistent data leads to a decline in data health and destroys the inherent value of data when messing up all business operations.

Here are some expert solutions for the issue:

• Establish data governance policies to make sure the formatted consistent across sources.
• Apply technologies like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate and find out and correct inconsistent data.
• Regularly verify and clean data systems.
• Automate the data entry process by using drop-down menus or data picklists.

Inaccurate data

Inaccurate data is data that contains errors that affect its quality and reliability. Since it is a fairly broad concept, other data quality issues such as incomplete, outdated, inconsistent, or typographical errors and missing or incorrect values are also considered inaccurate data.

To produce actionable insights, it is necessary and sufficient that the data collected is highly accurate and reflects the real-world picture. However, under the influence of many external and internal factors, such as human error in the data entry process and data drift, the information cannot keep its inherent accuracy as it was at the time of data entry. Incorrect data causes businesses to make wrong decisions and disappoint customers with the information provided.

For example, incorrect data about customer information in a CRM database leads to underperforming marketing campaigns, lost revenue, and customer dissatisfaction.

Here are some expert solutions to this problem:

• Establish data governance policies by guiding data quality standards.
• Use data cleaning techniques such as data normalization to eliminate errors.
• Automate data quality processes using data profiling software and data validation frameworks.
• Regularly review and clean the data system.

Hidden data

Enterprises extract and analyze data for operational efficiency. However, with today’s huge amount of data, most organizations only use only part of them. The remaining unused or missing data in data silos are referred to as hidden data. More specifically, hidden data can be valuable but unused and stored within other files or documents or invisible information to customers, such as metadata.

For instance, a company’s sales team has data on customers, while the customer service team doesn’t. Without sharing the needed information, the company may lose an opportunity to create more accurate and complete customer profiles.

Hidden data should either be used or deleted. Having this data quality issue present is not only a way to waste resources; hidden data can even result in privacy monitoring and compliance violations if they are sensitive data within datasets.

Here are some expert solutions for the issue:

• Invest in data catalog solutions.
• Use data masking to replace sensitive data with fictitious data while retaining the original data format.
• Use machine learning algorithms to identify hidden data.
• Limit access to certain data types based on employee roles and responsibilities.

Outdated Data

Collected data can become obsolete quickly and inevitably lead to data decay through the development and modernization of human life. All information that is no longer accurate or relevant in the current state is considered outdated data. Information about a customer, such as name, address, contact details, etc., is a good example that needs to be constantly updated so as not to miss opportunities to consult about the company’s services and promotions.

The problem of old data is not only a concern about accuracy, but it also reflects the delay and lack of investment and interest of enterprises in database management systems. The consequences of outdated data can extend to incorrect insights, poor decision-making, and misleading results.

Here are some expert solutions for the issue:

• Regularly review and update data.
• Establish a data governance strategy to effectively manage data.
• Use outsourcing services in data management if managing data in-house is not feasible.
• Use machine learning algorithms to identify outdated data.

Regardless of the type, data quality issues all harm business operations. Orient Software has provided you with valuable expert solutions to limit or even eliminate such problems through this article. However, human is the core value of every organization. There is no better solution to completely solve data quality issues than training and improving the awareness and professional skills of the company’s personnel.