How prepared are businesses to take full advantage of the insights that artificial intelligence affords? The tools may be ready, and talented people may have come onboard, but it’s likely there’s a gap in the data. Yes, there is plenty of data flowing through enterprises, 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 out of Wavestone NewVantage Partners finds. In addition, only 24% of companies report they are doing enough to ensure responsible and ethical use of data within their organizations and the industry. “Becoming data-driven is a long and difficult journey that organizations increasingly recognize playing out over years or decades,” the study’s authors, Tom Davenport and Randy Bean, point out. “Companies continue to fall short in attention and commitment to data ethics policies and practices.”
The data gap is likely the most pressing issue affecting AI success, agrees Mona Chadha, director of category management at Amazon Web Services. “There are issues 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 of AI models depends strongly on the data used to train the models. Poor data quality can result in inaccurate results and inconsistent model behavior, leading to lack of trust from customers and internal stakeholders.”
Continue reading: https://www.forbes.com/sites/joemckendrick/2023/01/22/a-data-gap-continues-to-inhibit-artificial-intelligence/?sh=23d1cb445a84
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 out of Wavestone NewVantage Partners finds. In addition, only 24% of companies report they are doing enough to ensure responsible and ethical use of data within their organizations and the industry. “Becoming data-driven is a long and difficult journey that organizations increasingly recognize playing out over years or decades,” the study’s authors, Tom Davenport and Randy Bean, point out. “Companies continue to fall short in attention and commitment to data ethics policies and practices.”
The data gap is likely the most pressing issue affecting AI success, agrees Mona Chadha, director of category management at Amazon Web Services. “There are issues 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 of AI models depends strongly on the data used to train the models. Poor data quality can result in inaccurate results and inconsistent model behavior, leading to lack of trust from customers and internal stakeholders.”
Continue reading: https://www.forbes.com/sites/joemckendrick/2023/01/22/a-data-gap-continues-to-inhibit-artificial-intelligence/?sh=23d1cb445a84
