Data is the most significant currency in the 21st century. Its proper use can bring the brand overnight success, and if it is in the wrong hands, it can cause chaos and confidentiality.
Data quality has been a critical subject in every business sector. Major brands and corporate entities need to improve their data quality; otherwise, it can have detrimental effects on the service standards and the client’s well-being – a missing value or wrong value could lead to severe consequences, even a lawsuit.
There are billions of searches on Google. People looking to buy products use their personal information to help find suitable investments for themselves, trusting brands in that process. However, suppose someone abuses that information. In that case, it could jeopardize the company’s image and have a devastating and detrimental impact on the client’s life.
Large enterprises nowadays use extraordinarily complex IT systems spread across multiple interconnected systems and geographies. In addition to their ERP, they also use software to manage pricing, customer relationships, and demand planning, among other things. Given the complexity of this process, data management has become challenging in recent times as IT systems have become domain-specific and specialized.
We live in an era of unprecedented data abundance and aggregation. The sheer variety of information available in databases, over the internet, and other sources has dramatically changed how leaders conduct business, communicate, and undertake research – and healthcare is no exception.
One of the fundamental questions healthcare leaders seek to answer is how to improve data quality. One fundamental problem leaders in the healthcare sector must address is “dirty data” – inaccurate or missing information in the abundance of data.
You want to improve your operational performance, add value to services, and have reliable access to data to make informed decisions. Avoid these three mistakes to enhance the quality of data.
Mistake #1 Improper Documentation
Incomplete or inaccurate data documentation of medical records is a standard and significant error in corporations. Examples include filing information in the name of the wrong client file or incorrect coding for customers with the same name. Issues like these could raise confidentiality-related issues where personal or sensitive details can be released to others by mistake.
Mistake #2 Not Investing in Better Technology
In 2017, 188 leaders were surveyed to assess data quality in the healthcare sector. The results showed that 66% of respondents believe data entry errors contribute significantly to data redundancies, leading to ill-informed clinical decisions. Another survey conducted in 2018 showed that duplicate medical records and repeated medical care cost an average of $1950 per patient and over $800 per ED visit. The survey noted that the absence or repetition of even a single medication could negatively affect the patient’s health (Al-Noumani et al., 2019).
Mistake #3 Use of Outdated Information
Outdated information automatically populates in various fields because the data was not updated in a timely manner. Not having access to the correct information could lead to wrong consultations and service provisions.
The Canadian Institute for Health Information has implemented a comprehensive program focusing on different dimensions of data quality. Each size aims to rectify a particular aspect of data issue that can improve the overall healthcare standards.
Accuracy
Data accuracy is associated with how well the information reflects the reality it is designed to measure. Accurate data allows organizations to build solid processes for the long term. By doing so, they can develop learnings and speed up their process, consequently improving operational efficiency.
Comparability
Comparability refers to the extent to which databases are consistent over time. It also refers to how well the database adheres to international standards and guidelines.
Usability
Not having access to user data is a significant concern for healthcare practitioners. A notable usability research expert, Jakob Nielsen, said that usability combines five key components: efficiency, learnability, errors, memorability, and Satisfaction. The primary purpose of having usable data is to ensure that information is accessed and understood easily. High data usability supports users, increases acceptance rate and operational efficiency, and decreases faults.
Conclusion
Improving data quality is one of the primary responsibilities of brand leaders, as it leads to dramatic quality improvements. The complexity of modern data management software demands substantial improvements that can effectively address challenges with suitable data practices.
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