A Data Gap Continues To Inhibit Artificial Intelligence

How ready are businesses to take full advantage of the insights that artificial intelligence provides? The tools may be ready, and talented people may have come on board, but there is probably a gap in the data. Yes, there is a lot of data flowing through businesses, but harnessing it in a productive and unbiased way is another story.

At this point, only 24% of organizations consider themselves to be data driven, and only 21% have what can be considered “data cultures,” a new survey of senior data and analytics executives from Wavestone NewVantage Partners finds. Furthermore, only 24% of companies report that they are doing enough to ensure responsible and ethical use of data in their organizations and the industry. “Becoming data-driven is a long and difficult journey that organizations are increasingly recognizing takes years or decades to play out,” note the study’s authors, Tom Davenport and Randy Bean. “Companies continue to lack attention and commitment to data ethics policies and practices.”

The data gap is probably the most pressing issue affecting AI success, agrees Mona Chadha, director of category management at Amazon Web Services. “There are things that companies need to be aware of, such as poor data quality, unfair bias and lax security to name a few,” she states. “Quality of predictions from AI models depends heavily on the data used to train the models. Poor data quality can result in inaccurate results and inconsistent model behavior, leading to a lack of trust from customers and internal stakeholders.”

Data bias and security are also issues that need to be addressed in AI, Chadha continues. “It’s easy to get caught up in the assumption that AI can make decisions more impartially than humans. Unfair biases, present in the data used to train AI models, can result in discriminatory behavior that can put businesses at risk. Attackers are constantly trying to exploit AI vulnerabilities. Businesses must ensure that AI systems are protected from adversarial attacks through their data and algorithms.”

When it comes to data quality, organizations need to focus on the processes used to control their data assets. “Existing data often resides in multiple databases and data warehouses, which often contain duplicates, outliers and irrelevant data points,” Chadha states. “There are also gaps in the existing datasets. Organizations need better tools to clean and label the data. Poor data quality can result in inaccurate results and inconsistent model behavior, leading to a lack of trust from customers and internal stakeholders.”

Once the data gap is closed, businesses can start building their business cases for advancing AI. “As AI gains traction, a number of business use cases, across industries, are seeing the results,” Chadha recounts. “Examples include driving product innovation by accelerating drug discovery and training autonomous vehicles to navigate complex traffic scenarios. AI supports risk mitigation by helping to combat financial fraud and reduce unplanned downtime for industrial equipment. Consumers are also seeing an improved user experience with AI driving content engagement through recommendation engines or improving customer service by using AI to assist human agents. Finally, AI is making great strides in overall efficiency and safety improvements by assisting the manufacturing sector with computer vision.”

(Disclosure: Over the past year I have done project work for AWS, mentioned in this article, in my capacity as a freelance analyst.)


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