We live in an era of unprecedented data abundance and aggregation. The sheer variety of information available in databases, on the Internet, and from other sources has dramatically changed the way leaders conduct business, communicate, and conduct research.
One of the fundamental questions business leaders seek to answer is how to improve data quality. One serious problem that leaders in the business sector need to address is “dirty data” – inaccurate or missing information that resides in the abundance of data.
Poor data quality leads organizations to lose a lot of money. Research states that bad data or poor-quality data costs organizations nearly 30% of their revenue, which is a staggering number considering the investments.
The bigger your organization, the more data it will generate from different departments and sectors. The more information you handle, the more likely you are to make mistakes. However, you want to improve your operational performance, add value to services, and have reliable access to data to make informed decisions.
Avoid these mistakes to improve data quality.
Wrong Assumptions
The first mistake organizations tend to make with data is the assumption that their current data and information are accurate and clean and that there is no need to fix that. However, organizations are often oblivious that there is no problem with their existing data flowing between the businesses. Unfortunately, that is not the case at all.
Improper Documentation
Incomplete or inaccurate data documentation of client records is a standard and significant error in the business world. Examples include adding or updating information in the wrong client’s name or inaccurate coding for customers with the same name. Additionally, these issues could raise confidentiality-related issues, as personal and private details can be released to others by mistake.
Not Investing in Better Technology
In 2017, 188 leaders were surveyed to assess the business sector’s current state of data quality. The results showed that 66% of respondents believe data entry errors contribute significantly to data redundancies, leading to ill-informed business decisions. Another survey conducted in 2018 showed that duplicate customer records and repeated product serving cost an average of $1950 per customer and over $800 per website visit. The survey noted that the absence or repetition of even a single product could negatively affect business performance.
There is no denying that data quality has and will always be affected by one major issue – human error. Human error makes it easy for data to be repeated or duplicated. Data duplication is a significant and relatively common problem in all business sectors. All organizations face this issue at some point, but that doesn’t mean it should be left unaddressed, thinking it will get solved independently. Data duplication can jeopardize data analysis and reporting, making it difficult to cleanse the data. Regardless of what the circumstances may be, it’s something that needs organizations to be proactive.
Use of Outdated Information
Outdated customer information automatically populates in various fields because the data was not updated promptly. Not having access to the correct information could lead to wrong service and product recommendations.
Department Isolation
The fifth mistake that organizations tend to make is isolating all their departments. When all your departments start working in isolation without having any synergy or sync, it creates departmental silos. Once this happens, all your departments interpret data differently, without coherence. Consequently, data redundancies, conflicts between teams, and data reconciliation become difficult between groups and applications.
Poor Data Governance
Poor data governance can also be one of the significant reasons why data quality issues occur in organizations. Organizations invest substantial capital in improving their data quality measures, which can lead to poor data quality if the governance is inappropriate. Without proper procedures and policies, it’s easy to assume that bad practices in any organization can be so prevalent.
Conclusion
Improving data quality is one of the primary responsibilities of organizational leaders, as it leads to dramatic quality improvements. However, issues with data quality can sometimes be hard to spot. Therefore, your organization must follow all the protocols mentioned above.
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