An important early step to data readiness is to establish the business use case it is intended to serve and align your infrastructure to help deliver this.
But how do enterprises achieve this goal without draining time and resources away from wider business functions?
Even within one organization, there will be multiple stakeholders with different ideas about how machine learning and deep learning can help their organization improve its market position.
Businesses Readying Their Data for AI Are Often Looking to:
1. Manage market disruption; 2. Enhance the customer experience; 3. Boost business efficiency; 4. Improve business insights
Upto
59%
of executives say big data at their company would be improved through the use of AI
Upto
59%
of executives say big data at their company would be improved through the use of AI
Boosting the Retail Customer Experience With Data
Retailers faced with industry disruption are drawing on as much of their data as possible to generate new business insights with machine learning.
Many retailers have focused their data readiness efforts on removing traditional barriers to data access, giving predictive models free reign to generate a wealth of actionable insight. This means customers can be segmented and more accurately targeted, creating tailored shopping experiences and improving customer satisfaction.
This organized data can also be used to better predict customer behavior, allowing for marketing and inventory choices that help retail locations meet demand and present the best possible products at the right times. AI can also track and process data from customer interactions via online portals to develop a better e-commerce strategy.
Meeting Growing Demands for Food Production
Farmers are gathering new data to help them plan for the planet’s future food challenges. According to the United Nations, population growth means food production will need to increase by 50 percent by the middle of the century.
NatureFresh Farms grows vegetables on 185 acres of land in the United States and uses AI to mine previously untapped data sources. Robotic lenses to examine the flower of tomato seedlings and use this data to predict how long it will take for the blossom to become a ripe tomato ready for picking, packing and the produce section of a grocery store or supermarket.
This approach to farming requires considerable processing power, which is why NatureFresh Farms uses Intel® Xeon® processors to power its AI algorithms.
Intel® Xeon® Scalable Processors: Your Data Foundation
From data ingestion and preparation to model tuning, Intel® Xeon® Scalable processors act as a flexible platform for all the analytics and AI requirements in the enterprise data center.
Able to handle scale-up applications with the largest in-memory requirements to the most massive data sets distributed across a myriad of clustered systems, they serve as an agile foundation for organizations ready to begin their AI journeys.