Why is data quality essential? In the age of big data, ensuring the quality and reliability of data is non-negotiable. Explore the reasons why data quality is essential for unlocking the full potential of your data assets.

While the quote “Your data ages like fine wine, whereas your software applications age like fish” may sound amusing, it fails to capture the reality. Data quality is not something that improves with age, unlike fine wine. In fact, without proper measures in place, data tends to deteriorate over time, much like poorly stored wine.

What causes data decay?

Data quality can deteriorate over time due to various factors. Here are some common reasons:

Incomplete or outdated information:

As time passes, data may become incomplete or outdated. For example, contact details of customers may change, employees may leave a company, or product information may become obsolete. Without regular updates, the data loses its accuracy and relevance.

Data entry errors:

Data can be entered incorrectly or inconsistently at the time of collection. Over time, these errors can accumulate and lead to data quality issues. For instance, if a customer’s address is mistyped initially and not corrected, it can affect the accuracy of future analyses or communications.

Data integration issues:

Organizations often collect data from multiple sources and systems. As new systems are introduced or existing ones are updated, data integration challenges may arise. Incompatible data formats, inconsistent data structures, or data mismatches can occur, resulting in poor data quality.

Data duplication:

Duplicated data can emerge over time through various channels. For instance, when merging data from different sources, duplicate records may be created. These duplicates can lead to inconsistencies, redundancy, and inaccuracies in the dataset.

Data decay:

Some data naturally deteriorates over time due to changing conditions. For example, market trends, customer preferences, or demographic information can evolve. If the data is not regularly updated, it loses its relevance and effectiveness in decision-making processes.

Lack of data governance:

Data governance refers to the overall management and control of data within an organization. Without proper governance practices, data quality can suffer. Inadequate data standards, inconsistent data definitions, and a lack of data stewardship can contribute to data degradation over time.

Technical issues:

Technical problems such as hardware or software failures, data corruption, or data loss can also impact data quality. If data is not backed up or protected adequately, it can become compromised or inaccessible, leading to deterioration.

The Impact of Data Decay

Outdated or incorrect data can hurt businesses. It wastes time and resources on ineffective marketing campaigns, targeting the wrong audience or disinterested customers. It also damages customer relationships by providing irrelevant or wrong information

The customer database

To illustrate these points, consider an example of a retail company that has a customer database. Over time, customers change their addresses, phone numbers, or email addresses. If the company fails to update this information regularly, their database will contain outdated contact details, reducing its accuracy and affecting communication efforts. Studies indicate that customer data can deteriorate at a rate of 20% to 40% per year. Furthermore, the addition of new data exacerbates the problem. Unless we actively ensure the quality of incoming data, the overall information quality within any system will decline over time.

The data integration problem

Another example involves a financial institution merging multiple databases during a system upgrade. If the data integration process is not carefully managed, duplicate customer records can be created, resulting in data redundancy and potential errors in customer analysis or reporting.

Consider the challenges that arise when consolidating or migrating data from multiple systems, such as during the implementation of a new CRM or MDM platform. Poor data quality is often blamed for delays and failures in such projects.

The Statistics: Data Quality

Data quality deterioration over time is a common phenomenon that affects all types of data. Here are some statistics and insights on how data quality can deteriorate over time:

• Approximately 40% of email users change their email addresses every two years, leading to outdated and inaccurate contact information.
• Gartner estimates that approximately 3% of global data is lost every month3.
• Data quality can deteriorate as data travels across systems, potentially leading to data integrity issues.
• According to the DAMA (Data Management Association), organizations spend between 10% – 30% of sales on poor data quality issues.
• Poor data quality can result in lost income, with organizations losing an average of $15 million per year due to poor-quality data.
• Data inaccuracies can be caused by human error, data drift, and data degradation.
• Data quality problems can arise from manual data entry errors, OCR errors, lack of complete information, and other factors.

These statistics highlight the importance of maintaining data quality and the potential negative impacts of poor data quality on businesses, including poor customer relations, inaccurate analytics, bad decisions, lost income, and fines2. Organizations need to prioritize data governance, continuous monitoring of data pipelines, and implementing best practices to ensure data quality and mitigate the risks associated with data deterioration over time3

Bad data is persistent

It is crucial to acknowledge that your data will likely outlive your applications. While you may choose to upgrade your ERP with a new user-friendly interface or switch to a different platform altogether, your existing master and transactional data will remain. However, some adjustments might be necessary to make it compatible with the new environment.

How to avoid data quality errors in Excel: Gain insights into avoiding data quality errors in Excel and ensure accurate data management with these helpful tips.

Conclusion

Data quality is an ongoing journey that requires proactive management. Taking steps to maintain data quality as it ages is the only way to ensure its continued value and reliability.

By implementing effective data management practices, such as regular data maintenance, data cleansing, and effective data governance practices, you can safeguard the integrity of your data, prevent its deterioration, and unlock its true potential.

How does metadata enhance data quality? Explore the critical success factors for useful metadata in enhancing data quality and its relevance in various data management processes.