Data Quality: How to Assess and Address Quality Issues

Did you know that the data cleansing tools market was worth $2,497.7 million in 2023? It is likely to inflate up to $6,043.4 million by 2030, and the CAGR is going to be 13.32% from 2024 to 2030, as per a report. 

What’s the reason for this much growth? It’s absolutely necessary to improve the quality of data using automatic tools and advanced technologies. This is simply because clean or hygienic data consists of valuable insights into customers’ preferences, intents, requirements, pricing, and opportunities. These are key values that can take a business ahead and lead the competition. Certainly, this clean data-driven insight leads to leadership, which will bring a way bigger market exposure.

So, what is quality, and what is the secret to achieving the best quality?

What is data quality?

Data quality represents the actual condition of the datasets, which is based on many factors. These factors or metrics can be accuracy, completeness, consistency, reliability, and validity. Measuring it can be a delightful experience for organizations because it filters out errors and inconsistencies in their databases. Also, this assessment guides you to determine if the hygienic data fits your goals or purposes. 

For this reason, organizations have started focusing on the quality of information that they have in their databases. Considering the significance of accurate data in business operations and advanced analytics, organizations are increasingly recognizing the role of data-driven decisions and the need for outstanding data entry quality. This is why data governance strategy has quality management as a key role.

Understanding the governance of data ensures that the data is stored, handled, protected, and shared according to governance protocols like GDPR. 

Now, let’s discover the crucial elements that represent quality. 

Major Components of Data Quality

Majorly, these components are six. These elements guide how to professionally deal with low-quality data. If they remain untreated, the unhygienic data can lead to misleading details in operations. Also, the results would be faulty after deep analysis. This is why such discrepancies, typos, inconsistencies, and incomplete datasets need to be identified so that data entry specialists can fix them. This cleansing provides business executives, analysts, and owners with accurate information.

Let’s start exploring these elements.

• Accuracy

Accuracy represents the correct dataset. Data entry specialists verify it at the point of entry so that the accuracy can be verified from trusted sources. 

• Consistency

The next one is consistency, which refers to the uniformity of data across systems and databases. It is also assured that there will be no conflicts between similar values in different systems.

• Validity

Validity means data that conforms to defined protocols and regulations for data management. By doing so, organizations can properly structure all crucial details that are valuable and insightful.

• Completeness

Completeness refers to the values and types that the database is expected to contain. They must include complete details and accompanying metadata.

• Timeliness

Timeliness represents the status of its freshness. A data specialist checks if the data is current and resonates with specific requirements.

• Uniqueness

Uniqueness is all about containing unique records within a database. This element helps in kicking out duplicate values.  

All these elements invoke reliability and trustworthiness.

Auditing Data Quality

Quality assessment is significant, especially when you are likely to benchmark or set the foundation of your study. Measuring the relative accuracy, uniqueness, and validity of each record establishes the benchmark, which can be used for comparing the data continuously. It can highlight the existing gaps to be filled and determine quality issues.   

Various companies and some organizations, which take quality work seriously, have worked on defining the quality auditing process. Considering the example of the IMF, or International Monetary Fund, which is a global entity for borrowing funds to come out of economic crises, it focuses on accuracy, reliability, consistency, and other attributes of quality management.

Quality Issues: How to Address?

In the very beginning, you should focus on the stakeholders, or people who are concerned with the quality of the data. They can be analysts, engineers, and data quality managers. Their role is to introduce accuracy by removing errors or quality issues.

Quality can be assured through data cleansing methods, which stakeholders can use to fix erroneous data and establish quality. They have to collectively work and find errors or bad data in databases. If these specialists are inaccessible in-house, one can hire data management professionals on a contract, provided that they are aware of all quality metrics and compliance.

1. Determine Stakeholders

While initiating, businesses should involve analysts, data scientists, and stakeholders so that quality concerns can be fixed in no time. However, they all may not be required, but they can participate in parts. However, companies understand the power of insightful and fresh data. They continue to provide training to end users about how to use or introduce the best quality practices.

2. Drafting Rules

These best practices can be blueprinted by drafting a set of data quality rules based on business requirements. They can resonate with operations and analysis teams. These rules create a baseline of requisite data quality levels and how the hygiene process should go on for standardizing and establishing accuracy, consistency, and other quality attributes. 

3. Quality Assessment

Once the rules are outlined, data specialists should typically audit the quality, documenting errors and other problems. This is not a one-time effort. It should be continuous so that the highest quality can be introduced. It can also help in establishing performance targets so that quality can be improved.

Quality Improvement Process

The next step is to design and execute a specific quality process, which can have various segments reserved for data scrubbing, correcting, and enriching datasets by integrating missing values or updated information. 

Monitoring

Finally, the assessment results are verified against the performance targets. If there emerges any inadequacy in the quality, the next round of improvement should be adopted. This is how the overall data quality process continuously takes place.

Conclusion

Data quality is significant, as it is the baseline for some crucial decisions and strategies. There are certain steps that can help in establishing quality in the records, which can be to define stakeholders, rules, and the process to fill the gaps in their quality. These key steps help in the quality improvement of corporate data.