Opinion: Akerman's Melissa Koch explains why the quality of data in legal artificial intelligence matters more than the ...

The micro stage might not generate headlines, but it generates trust—and trust is the currency of effective AI. When done well, it ensures your models aren’t being sabotaged by hidden errors or ...

In an enterprise world drowning in dashboards, one truth keeps surfacing: Data isn’t the problem—product thinking is.

How to Make Data AI-Ready AI applications require good data to be effective. Consequently, universities and colleges are fine-tuning their data governance strategies and technologies to support their ...

Even the best tools and architecture won’t prevent data quality degradation without governance. Governance connects policies to practice, aligning standards, roles and responsibilities.

We talk to Cody David of Syniti about how to ensure data quality in datasets for AI, why a ‘data-first’ attitude is key, and the quick wins an organisation can gain in data quality.

Figure 2: Data governance model modified and extended from the data quality within a lifecycle approach model (taken from reference 2, Figure 15). The crucial concept is that data quality is only ...

Some of the report’s conclusions may seem counterintuitive at first. For instance, the study, which is based on a survey of 565 data and analytics leaders, found that 76% of organizations say ...

The more high-quality data is fed into these models, the better their outputs generally are. For years, A.I. developers were able to gather data fairly easily.

Navigating data integration complexities and challenges with AI solutions Data integration is a crucial process for enterprises that combines and consolidates data from various sources into a unified ...

Data governance committee: Monitors the implementation process and provides the top-down stamp of authority to facilitate data governance policies. Data steward: Oversees the quality of individual ...