Businesses Must Address Data Quality for AI Success

According to a new survey of senior data and analytics executives, a significant gap in data is hindering businesses from fully utilizing the potential of artificial intelligence (AI). The study, conducted by Wavestone NewVantage Partners, found that only 24% of organizations consider themselves to be data-driven, and only 21% have what can be considered “data cultures.” Additionally, only 24% of companies reported that they are doing enough to ensure responsible and ethical use of data within their organizations and the industry.

The survey’s authors, Tom Davenport and Randy Bean, stated that “becoming data-driven is a long and difficult journey that organizations increasingly recognize playing out over years or decades.” They also highlighted that companies continue to fall short in attention and commitment to data ethics policies and practices.

Mona Chadha, director of category management at Amazon Web Services, agrees that the data gap is likely the most pressing issue affecting AI success. She states that issues such as poor data quality, unfair bias, and lax security are key concerns that companies need to be aware of. Chadha stresses that the quality of predictions of AI models depends strongly on the data used to train the models, and that poor data quality can result in inaccurate results, inconsistent model behavior, and a lack of trust from customers and internal stakeholders.

Chadha also highlights that data bias and security are crucial issues that need to be addressed in AI. Unfair biases present in the data used to train AI models can result in discriminatory behavior that can put businesses at risk. Furthermore, businesses must ensure that AI systems are protected against adversarial attacks across their data and algorithms.

In conclusion, it is clear that businesses need to prioritize closing the data gap in order to fully take advantage of the benefits of artificial intelligence. The quality of predictions from AI models heavily depends on the data used to train them. Without proper data governance processes in place, organizations risk poor data quality, biased decisions and security vulnerabilities.

To address these issues, companies must focus on improving their data management processes, including better tools for cleaning and labeling data, and ensuring data quality and security. Only then can they truly reap the rewards of AI, such as driving product innovation, reducing financial fraud and improving customer service. It’s important to note that it’s not easy task, it requires commitment and attention, but it worth the effort in the long run.