AI Success Hinges on Data Preparation, Says RobobAI CTO
TL;DR
RobobAI leverages AI to help organizations ethically transform supply chains, giving them a strategic advantage in the global market.
RobobAI emphasizes the importance of accurate data curation as the foundation for successful AI projects, addressing data deficiencies through AI techniques.
By improving data quality and reducing manual effort, RobobAI is contributing to ethical and commercial management of supply chains, ultimately benefiting global organizations and the world.
RobobAI's innovative use of AI to address data deficiencies and enhance data records with missing attributes is transforming the way organizations manage their supply chains.
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As organizations globally rush to harness the power of artificial intelligence, many are overlooking a crucial step: the accurate curation and preparation of their data. This oversight is emerging as a top trend among global organizations, according to Dave Curtis, Chief Technology Officer at RobobAI, a fintech company specializing in AI-driven supply chain transformation.
Curtis points out that a primary reason for the failure of AI projects is the unexpected costs associated with data collection and rectification. "Accurate and complete data is the foundation of all analytics on which business decisions are made," Curtis explains. He notes that many companies struggle with poor data quality due to multiple sources of truth, lack of automation, and manual entry errors, creating significant obstacles to effective data utilization.
The implications of this trend are far-reaching. Organizations investing in AI without addressing their data foundations may find themselves unable to realize the full potential of their AI initiatives. This could lead to wasted resources, missed opportunities, and a competitive disadvantage in an increasingly AI-driven business landscape.
To combat these challenges, Curtis suggests leveraging automation tools to reduce workload, decrease turnaround time, and prepare data for broader business applications, including AI and machine learning use cases. RobobAI is observing an uptick in the use of AI not just for predictive modeling, but also to address data deficiencies in ways that significantly reduce manual effort.
Curtis emphasizes the importance of maintaining data quality once corrected, noting that many organizations currently dedicate entire teams to data fixes. Companies are now exploring avenues to achieve demonstrable ROI by reducing or eliminating this effort. RobobAI's platforms, for instance, employ AI techniques such as natural language processing and clustering to preprocess data, identify and reduce duplication, and enhance data records with missing attributes.
The message is clear: while there is significant focus on analytics and AI, organizations need to prioritize their data foundations. Curtis advises companies to consider the entire end-to-end model when building a case and understanding potential returns. This approach could be crucial for businesses looking to stay competitive in an increasingly data-driven and AI-enabled business environment.
Curated from 24-7 Press Release
