Talking About Data Quality
We’ve all seen it, different data sources imposing their own rules on the same kinds of data. Different abbreviations, different codes, different formats always plague analytics.
Then, there is the question of human induced irregularities such as typos and inconsistencies. What happens when you need to properly combine and aggregate this data to run analytics? Dirty data represents a lot of missed opportunities.
Data cleansing is a critical part of the data preparation process and is necessary to ensure that the data used for reporting, analysis, or modeling is reliable and can lead to accurate and meaningful insights. Automated tools and scripts are often used to streamline and expedite the data cleansing process, especially for large datasets, but human oversight is crucial to making informed decisions about data cleaning strategies and handling special cases.
Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to improve its quality and reliability. This crucial step is essential in data management and analysis to ensure that the data used for decision-making and analysis is accurate, complete, and consistent.
We have the experience you need in handling these issues. We can build an easy-to-manage and sustainable platform for managing your data cleansing and standardization, with varying levels of human involvement depending on your comfort level. We know every platform has different dependencies, our approach is customizable to your needs. Schedule a call and learn what we can do for you – and your data.
All data has limitations, the key is understanding how to manage those limitations and groom your data for success.
Be wise.. Standardize!
Take a patient who speaks Spanish for example. One hospital may mark this down as SPA, while another might have a human spell out the word ‘spanish’. Another might just as easily record it under the ISO code of ES, and yet another hospital could use an internal ID number. These are all different ways of recording the same information. But when you run your Patient Profile report all you want to see is Spanish.
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Each source system can be code data values in different ways. The challenge is to be sure that your data analytics sees only one common value for each non-standard coding. |