Data Quality

Data quality is a measure of how consistent and representative of real-world circumstances the data is. Ensuring data quality makes the data set suitable for its intended purpose, such as training a machine learning model.

Why Data Quality Management Will Always Be a Component of AI Development

The raw data that organizations gather is rarely useful for analysis or AI model development without proper cleansing procedures. “Raw” data often comes from separate sources, all of which have their own gathering procedures and formats. Other sources like customer surveys can contain mistakes, typos, duplicates, and non-applicable data as well.

And because business environments are always changing, data quality will always be an important consideration, both for machine learning model developers and business strategists.

Data quality metrics help with AI in both building models and scoring them. Self-driving vehicles have been building image recognition algorithms by tagging certain images, such as whether an image contains a stop sign or motorcycle. Adding those tags is ultimately a manual process, and ensuring no errors pass through is part of data quality assurance.

Once the driving algorithm receives those images, verifying whether its conclusions are correct is also part of data quality management. For example, if the model claims that an image does not contain a motorcycle, you still need human intervention to check whether there truly isn’t a motorcycle or the model can’t see one in the background.

Data Quality Dimensions To Gauge Quality Standards

How can data scientists judge quality in varying data sets? Data quality assessment generally looks for the following standards.

  • Accuracy– Mistakes in data collection can result in the data points not correctly representing conditions in the real world, which can lead to inaccurate predictions when that data becomes the input for a machine learning model.
  • Consistency– Part of ensuring data accuracy is preventing data points from contradicting one another. Identify inconsistencies between different internal data sets to reduce potential confusion.
  • Specificity– Even if your data is correct, it’s not always clear whether you’re gathering enough information for your machine learning model to be useful. For example, a customer database might store contact information but also might lack purchase history data, which sales teams would need to build effective algorithms.
  • Formatting– Whether they’re dates, phone numbers, or measurements, choose a single standard for data formatting and align all your data sets with it. Formatting makes it easier for machines to parse the data and simplifies data management in general.
  • Integrity– Compromised data is just as detrimental to AI development as incorrect data. Check whether any values are missing or whether any data sets are noncompliant with your standards.
  • Age– A significant issue facing AI development today is model drift. As the input data ages, it slowly loses relevance. Always train with new data and check whether your older models trained with historical data are still as accurate in their predictions as they should be.

Data quality assurance allows machine learning developers to work with reliable data sets and build more impactful models.

Current Challenges to Data Quality in AI

AI’s heavy reliance on data puts quality assurance at the forefront of any machine learning development team’s mind. Data quality is a necessary consideration throughout model creation and deployment.

  • Data preparation– Convert raw data into usable data through a preliminary data cleansing procedure. While it may take some human intervention, investing in high-quality data from the start gives data scientists and machine learning developers a solid foundation from which to start.
  • Model training data– Quality assurance doesn’t end at the cleansing stage. While developers train models, they still need to look for inaccuracies and gaps in the training data that could be holding back model performance. Once again, this process takes manual intervention.
  • Post-deployment quality assurance– Even after deployment, AI models will still take in new input data that requires some degree of cleansing. Model drift is a phenomenon where future data diverges from the historical data used to train the model. The resulting predictions are no longer accurate, and continuous maintenance will be necessary to keep a model operational.

It’s no wonder why engineers spend 40% of their working hours improving data quality. Its impact on model performance and subsequently the bottom line cannot be ignored.