What is AI data?
AI models work with inputs to create outputs. The fundamental input is the data by which the model is trained. AI can only be as smart as the data used to train it.
By now, it's no secret that artificial intelligence (AI) can be a vital tool for the enterprise. That's because it can pull out hidden gems of information from extreme amounts of seemingly unrelated data. But early AI adopters are coming to realize simply throwing random data at AI is a recipe for failure.
Indeed, data quality is emerging as an important success factor when it comes to training AI models. With quality data, the enterprise can improve its AI strategy's success, lower costs and push more AI-driven applications into production faster.
AI is the ideal tool for data quality management (DQM) because, within most business models, it's the only tool that can handle the volume and complexity of data required without bursting your IT budget. As well, AI can directly impact some of the key characteristics of data quality, such as accuracy, completeness, reliability and relevance. Developing each of these areas requires substantial analysis, which AI can achieve at greater scale and at a faster pace, not to mention less cost, than an army of analysts.
Data sets contain multiple properties -- like accuracy, relevance and validity.
Each data set is viewed differently by each department that interacts with it.
Thus, improving data quality requires myriad processes, including:
Setting up data profiling measures encompassing the type of data, where and how data is stored, the applications it serves and the stakeholders who use it.
Considering the data quality reference store that maintains the metadata and validity rules necessary for external processes.
Some of these processes are accounted for in data-centric AI, a current hot topic which prioritizes data quality over quantity -- especially for business applications of artificial intelligence. Automation can help ensure the process pipeline can continuously validate data and update the rules that establish its quality.
The Challenges of AI-Driven Data Quality Management
The Data Quality Paradox
It can be difficult to use AI to improve data quality because you need to train the AI itself with high-quality data. In other words, your AI solution needs to be trained on high-quality data before it can identify high-quality data.
One potential solution comes from Patrick McDonald, director of data science at Wavicle Data Solutions. McDonald suggests the first step to AI-driven data quality management is to establish a solid foundation of data governance and stewardship, preferably under an in-house manager's leadership, and then link that to a thorough data monitoring program.
The master data store is a good place to start, since this is the easiest to control and often most critical to the business model.
The Observability Conundrum
The ability to not only “see” data in the pipeline, but to track its movement and evolution, can have a dramatic impact on the resulting AI models' performance, Arize’s Krystal Kirkland explains. This is particularly important for emerging machine learning operations (MLOps) environments.