Do you know that IBM’s bad data cost around $3.1 trillion dollars every year?
Such a big loss it is!
It’s all because of data inaccuracies, which clarify how precious high-quality data is. Therefore, it’s a must to identify, segment, and fix typos, and duplicates, and fill in missing details so that data analysts can draw feasible strategies or business ideas.
Let’s talk about the most common data quality issues that are no less than big challenges.
Most Common Data Quality Issues
• Segmenting Semi-Structured and Unstructured Data
Fortunately, we have technologies and data management tools that make it easier to create a centralized database. But, this fortunate value for nothing when data warehouses or servers prove inefficient in effectively dealing with relational datasets.
It’s because of different data qualities, which can be good and bad, structured and unstructured big data. So, data managers should emphasize the structuring of unstructured and semi-structured databases.
Furthermore, artificial intelligence and machine learning applications add more difficulties in improving data quality. They collect real-time data from streaming platforms that continuously add more data over and over. As a result, the large volumes turn larger, which makes it harder to process, manage, and cleanse them.
These days, stringent data privacy and protection laws like GDPR are adopted by various countries. This happening helps people to avoid the misuse of their personal and sensitive data from the collection. Therefore, companies and organizations have to manage all datasets accurately and effectively.
• Filtering Quality Issues is a Challenge
However, there is a hierarchy of quality managers, analysts, data governance managers, and data engineers that consistently fixes quality issues like typos, missing details, inconsistencies, abnormal data, duplicates, unrelated entries, etc. These top-down professionals hire a data entry specialist for error free and quality data entry. He works on quality and error analysis for fixing them immediately in databases.
Here, they need technically sound and logical data scientists, stakeholders, and matter experts who can help in frequently defeating quality issues in database management systems. There is another option to end this struggle at the entry-level. The training program on how to introduce quality in the data and the must-follow practices for the best quality in databases can guide end users on how to prevent any errors and improve quality.
• Confusing Data Quality with Their Integrity
However, data quality and data integrity are used interchangeably. But, integrity does not represent data quality. It’s broader than that, which is a good combination of quality, governance, and security mechanisms to fix inaccuracies, inconsistencies, and data security.
Simply put, it covers both, logical and physical verticals. Logical integrity refers to quality measures and various characteristics, like referential integrity. It enables analysts to find related data elements in different databases and determine their validity. On the other hand, physical integrity is concerned with access controls, such as defining who can access and what measures to take for avoiding data from being corrupted. It also involves regulating measures like scheduling backups consistently and preventive measures to defeat any disaster via disaster recovery measures.
