Artificial intelligence (AI) and data management have a symbiotic relationship. When embedded into data management tools, AI can simplify, optimize, and automate processes related to data quality, governance, metadata management, main data management, and enterprise data analytics. However in all irony, effective data management is critical to enterprise AI adoption. Without a strong data management infrastructure and strategy in place, AI development efforts will not succeed. Information Week brought this news to our attention in their article, “AI Set to Disrupt Traditional Data Management Practices.”

While strong data management has long been a foundational practice for business intelligence and analytics, enterprise organizations will need to update what they do to meet the needs of growing advanced analytics implementations such as machine learning and other AI in the years ahead.

The challenge is most organizations have little knowledge on how AI systems make the decisions they do. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Data Harmony, harmonizing knowledge for a better search experience.