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What is Data Standardization and Why is it Important?
Data standardization involves transforming data from different sources and templates into one consistent format that can be easily understood by software systems, teams, and employees.
Data standardization is important because it develops and provides a structure for establishing and maintaining data quality. It does this by determining how all data should be formatted, eliminating unnecessary or redundant data as well as locating errors in the data.
Complete, accurate, and consistent data is crucial to the effectiveness of any organization as it allows for better, more informed decision-making and reporting. Organizations that standardize data set themselves up to benefit from:
Increase Adoption and Consistency
Members of a maintenance team can focus better on the task at hand if they know exactly what is expected and how to report on the task in terms of the information they collect and/or submit. Having clear expectations and consistently high standards for data can ensure that high-quality work is done throughout a facility. Quick Tip – Utilize drop-down menus whenever possible to further increase adoption and data consistency.
Simpler Collection Process
Standardization of data allows for simpler, more straightforward data collection. Employees know exactly what information needs to be collected and/or submitted, taking away any guesswork while streamlining the collection process, and saving time, effort, and energy.
More Accurate Data
With data standardization, a comprehensive catalog of data is created and maintained. A new standard will ensure that any data needed is collected, while vague or irrelevant data is not. More accurate data results in better reporting, which can allow organizations to make more informed decisions.
Data standardization can help an organization improve processes to be more effective and efficient. If your organization struggles with collecting consistent data, an abundance of inaccurate data, or cumbersome processes, it may be time to consider data standardization.
