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A lack of integrated data sources is hampering the adoption of AI technologies, argues a new report from Coresight Research.
The retail- and technology-focused research firm makes the case that most retailers currently separate data by function, such as marketing, demand forecasting, merchandising, and pricing, which may be leaving them at a disadvantage in a rapidly changing marketplace.
“Organizations with siloed data management practices will lag in responding to market changes and customer needs, putting them at a competitive disadvantage,” the report says.
Missing the mark: Coresight’s survey data found that 52% of retailers were not properly executing more than 10% of their promotional campaigns, and that retailers on average misprice 10% of their products in a given selling period.
Now the lack of integrated data is becoming more of a problem, the report argues, due to the fact that “AI models rely on high-quality data (accurate, complete, consistent, timely, and relevant) to make accurate predictions,” and that “poor-quality data hamper model training and performance, leading to incorrect insights and undermining the potential benefits of AI.”
- Meanwhile, the market for AI tools is set to explode; the firm estimates that the combined market for generative AI (GenAI) hardware will rise from $79.8 billion in 2024 to $235.5 billion in 2028.
- Duke University’s Fuqua School of Business and the Federal Reserve Banks of Richmond and Atlanta CFO Survey from June found that 54% of participating firms reported plans to use AI to automate tasks over the next year.
What’s the way forward? Coresight is encouraging companies to invest in new platforms that help harmonize and consolidate data sources that will allow firms to be more agile and responsive to change.
As Parvez Musani, SVP of Walmart Global Tech, told Retail Brew last fall, data is only as good as a company’s ability to actually execute strategies based on it.