Data hygiene

What is data hygiene?

Data hygiene refers to all processes conducted by an organization to ensure its data is “clean,” or error-free. “Dirty” data can be caused by duplicate records, outdated information, incomplete entries or the improper parsing of record fields from different systems.

Some essential steps involved in implementing a data hygiene program include:

  • Completing an audit: Review your data to determine its overall hygiene and what areas need the most attention. Determine what is useful and what is not for your business to avoid information overload and reduce the possibility for dirty data. Audits should be completed on internal systems as well as all external platforms used for data collection.
  • Standardize your data: Analyze all input fields and decide which should be standardized to prevent dirty data.
  • Automate your processes: Cleansing data manually can be extremely time consuming. Automated data cleansing systems use algorithms to detect anomalies, duplicate records and identify other errors.
  • Keep data up-to-date: Third-party tools that update records in real-time can be used to avoid data decay — the deterioration of data quality.

Benefits of data hygiene

A properly implemented data hygiene strategy provides the following benefits for artificial intelligence (AI) and machine learning (ML) applications:

  • Improved accuracy: If the data being used to train algorithms is dirty, this will be reflected in the quality of the application's output. Ensuring data is clean is an integral part of developing effective algorithms.
  • Reduces bias: AI systems trained using incomplete data can result in skewed responses.
  • Saves time and money: Training algorithms using dirty data can be costly, as it is likely to churn out unsatisfactory results which would require extra time and resources to correct. Implementing a robust data hygiene strategy from the start of a project can help brands avoid these extra expenses.
  • Improved customer experience: AI and ML algorithms that are trained using high-quality, clean data have a better chance of functioning properly and delivering the desired results, which leads to a better user experience.

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