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Data Quality Best Practices for AI

Data Quality Best Practices for AI

Experts agree Artificial Intelligence (AI) and Machine Learning will revolutionize how we live and conduct business. The key to realizing this vision is data. Data is the foundation for building robust and functional AI solutions.

Because of the important role data plays, the quality of data is critical. Data must be accurate, relevant, complete, timely, and consistent for it to be of value for AI purposes.

The following are eight steps to ensuring high quality data for AI solutions:

  1. Quality control of data. As data is often produced by outside sources or applications, organizations may have limited control of the data generation process. As such, thorough quality assurance may be the best option for ensuring data quality. Because of the volume of data included in AI datasets, the process to ensure data integrity should be automated so resources can be focussed on addressing issues as opposed to identifying them.

  2. Data governance. It is important to establish data management procedures that recognize and proactively address issues which can introduce errors into the data. The scope of data governance should include the collection, storage and use of data and pay close attention to prevent user and applications from unintentionally altering the data by overwriting it or introducing duplicate information.

  3. Data accuracy. Another important aspect of ensuring quality data for AI is ensuring the data accurately explains the behaviors or activities that it is intended to inform. The approach taken should be scenario-based and incorporate the operational scenarios envisioned for the AI.

  4. Data Labelling. Data labels not only allow an AI to understand the data it is consuming, it is also invaluable when investigating data anomalies. Datasets with proper meta data facilitate easier investigation of data problems allowing them to be resolved more quickly.

  5. Data Traceability. Another important requirement for resolving data anomalies is access to source data. Being able to track data to its source records may sometimes be the only way to reliably resolve issues identified in data sets.

  6. Noisy Data. Noisy data is data that cannot be understood by machines. It is often meaningless or corrupted data. Noisy data often causes machine learning or artificial intelligence algorithms to miss data patterns thereby impairing their ability to learn tasks. Data cleansing process need to identify and remove noise.

  7. Data Augmentation. Some AI models require a higher volume of data to perform optimally. It may be necessary to supplement data sets with additional data to properly support AI algorithms. This can sometimes become an issue when dealing with noisy data.

  8. Feature Engineering. When the dataset is not able to inform the AI algorithm, feature engineering can be used to supplement the data with additional variables or by creating features in the data to overcome the shortfall. This is best undertaken by engaging subject matter experts.

A comprehensive data quality strategy is critical to increase accuracy, reduce the cost, and speed AI implementation.