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Introduction

Data has become the heart of all businesses across the world. Organizations heavily rely on data assets for the decision-making process but unfortunately “100% clean and accurate data” doesn’t exist. Data is impacted by numerous factors that deteriorate its quality. According to experts, the best way to fight data issues is to identify their root causes and introduce new processes to improve their quality. This article covers the common data quality issues faced by businesses and how can they fix them optimally. Before we dig deeper into this, Let us first understand why the knowledge of these issues is important and what impact it makes on business activities.

Why is Data Quality Important?

What is data quality? Data quality refers to the measurement of the current state of data against traits like completeness, accuracy, reliability, relevance, and timeliness. While the data quality issue indicates the presence of a defect that harms the above-mentioned traits. Data is beneficial only if its of high quality. Some of the consequences of poor-quality data are as follows:

• Poor decision making
• Reduced productivity 
• Inaccurate analysis leading to poor reputation
• Customer dissatisfaction and loss in revenue
• Incorrect business plans

Common Data Quality Issues

1) Human Error

Even with all the automation, data is still typed on various web interfaces. Hence, there is a high possibility of typographical mistakes leading to inaccurate data. This data entry can be done both by the customers and the employees. Customers may write the correct data into the wrong data field. Similarly, employees may make a mistake while handling or migrating the data. Experts recommend automating the process to minimize the involvement of data capture from humans. Some steps that may help in this regard are:

• Real-time validation of forms using data quality tools
• Proper training for the employees
• Using definitive lists to lock down what the customers can enter

2) Data Duplication

Nowadays, data comes from multiple channels giving rise to duplicate data when merged. It results in multiple variations of the same record providing skewed analytical results and incorrect insights.  The budget is also wasted on these duplicate records. You can make use of Data Duplication Tools to find similar types of records and flag them as duplicates. Another technique that may help you out is standardizing your data fields and having some strict validation checks on data entry.

3) Inconsistent Data

Mismatches in the same information across multiple data sources can lead to data inconsistencies. Consistency is important to correctly leverage the data. The inconsistencies may arise from different units and languages. For example, the distance may be expressed in km while m was required. It messes up all the operations of the business and needs to be addressed at the source so that the data pipelines provide trusted data. Therefore, need to make all the desired conversions before the migration and introduce the validity constraints. Constant monitoring of the data quality can also help you identify these inconsistencies.

4) Inaccurate and Missing Data

Inaccurate data can seriously impact the decision-making holding the businesses to achieve their goals. It is tough to identify because the format, unit, and language are correct but there may be a spelling mistake or missing data making it inaccurate. Loss of data integrity and data drift (unexpected changes over time) are also indicative of data inaccuracy. We need to track them down in the early stages of the data lifecycle by employing various data management and data quality tools. These tools should be intelligent enough to spot these issues by automatically excluding incomplete entries and generating an alert. 

5) Using the Wrong Formula 

In practice, many fields in the dataset may be calculated from the other fields to extract meaningful information. These are called computed fields. For instance, age is derived from the date of birth. Whenever a new record is added, these formulas are computed automatically and the use of the wrong formula will make that complete field inaccurate. Violating these rules and logic results in invalid data. Testing your system at various stages can help you fix this issue. 

6) Data Overload 

Overwhelming the system with loads of data buries the key insights and adds in the irrelevant data. The additional overhead of capturing, organizing, and sorting all this data is not only an expensive process but is ineffective too. This load of data makes it difficult to analyze the trends and patterns, identify the outliers and introduce changes due to the ample amount of time that it takes. Data coming from different sources need to be cleaned by filtering out the irrelevant data and organizing it properly. This technique ensures that your data is relevant yet complete.

7) Data Downtime 

Durations when the data is in a partial, erroneous, or inaccurate state, refer to the data downtime. It is extremely costly for data-driven organizations that heavily rely on behavioral data for running their operations. Some common factors that may cause data downtime are unexpected changes in the schema, migration issues, network or server failure, Incompatible data, etc. But, what’s important is to measure the downtime continuously and minimize it through automated solutions. Downtime can be eliminated by introducing Data observability from source to consumption. Data observability is the organization’s ability to understand data health and improve it by employing best practices. Moreover, companies should introduce SLAs to hold the data teams accountable for their actions.

8) Hidden Data

Companies that experience rapid growth also accumulate data rapidly. They only use a portion of their collected data, dumping the remaining one to different data warehouses. It is referred to as hidden data because although it tends to optimize processes and provide valuable insights, it is not used. Most companies do not have a coherent and centralized approach to data collection which gives rise to hidden data. Centralizing your data is the best way to overcome this problem.

9) Outdated Data 

Data can become obsolete very fast and inevitably leads to data decay. The object described by the data changes, but these changes go unnoticed by the computers. For example, if a person has changed his field but the database still shows outdated data. This problem of getting the data out of sync as compared to reality deteriorates the data quality. Set reminders to review and update your data to ensure that it is not old and stale.

10) Data Illiteracy

Despite making all the effort, if the organizational teams are not data literate they will make incorrect data quality assumptions. Understanding data attributes is not simple because the same field may mean differently in various records. This ability to visualize the impact of updates and what each attribute indicates comes with experience. A session on data literacy should be organized for explaining the data to all the teams working on it. 

Conclusion

This article covers the most common data quality issues you can address at its roots to prevent future losses. Always remember that data alone cannot be valuable unless you make it. I hope you enjoyed reading the article. Please feel free to share your thoughts or feedback in the comment section.

 
 
Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.