1、Custom content for Alteryx and Databricks by studioIDImproving Data Quality in the Age of Generative AIData fuels artificial intelligence(AI),shaping the future of your business.The quality,integrity and availability of that data are pivotal in determining the success of generative AI initiatives.72

2、%Within a few months of ChatGPTs launch,generative AI was elevated to boardroom conversations across every industry.Businesses that hope to stay relevant are already investing in the technology.For chief information officers(CIOs),the imperative is not only to adopt these solutions but also to ensur

3、e that the data that feeds generative AI models is accurate and reliable.Good models cannot overcome bad data.The adage“garbage in,garbage out”is particularly relevant here.Outputs will inherit any flawed or biased input,resulting in unreliable models and misinformed decisions.Some estimates place t

4、he failure rate of AI projects as high as 80%,with a lack of data quality and availability among the underlying causes.1of business leaders said that data problems were more likely than other factors to jeopardize their achievement of AI goals.2Databricks Global CIO Survey on AI Adoption by 2025The

5、Data Quality Challenge2CIOs also face pressure to support data democratization by strategically deploying technologies that bridge the gap between complexity and business insight.For that to happen,data insights must be readily available to those who need them.However,when talking about democratizat

6、ion,we are also talking about scale.Data quality becomes incredibly important at scale because the more people who touch the data,the more trust people need to have in it.The Need for DemocratizationMaintaining high-quality curated data will get only harder as organizations scale AI adoption.AI has