As artificial intelligence (AI) continues to reshape industries, large organizations are increasingly recognizing the importance of properly curating and preparing their data before embarking on AI initiatives. Dave Curtis, Chief Technology Officer at RobobAI, a global fintech company specializing in AI-driven supply chain transformation, identifies this as a top trend facing global organizations today.
Curtis points out that a primary reason for the failure of many 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 adds that AI predictive models, in particular, require substantial historical information to forecast future outcomes effectively.
The RobobAI CTO highlights a common challenge among companies: poor data quality. This issue stems from various factors, including multiple sources of truth, lack of automation or validated sources, and manual data entry errors. These data quality problems create significant obstacles to utilizing information for decision-making purposes.
Curtis emphasizes the potential of automation tools in addressing these challenges. When applied correctly, these tools can reduce the workload of business users, decrease turnaround time, and enable more efficient use of underlying data for various business applications, including AI and machine learning use cases.
Interestingly, RobobAI has observed an increase in organizations using AI not just for predictive modeling, but also to address data deficiencies. This approach significantly reduces the manual effort required to deliver high-quality data. Curtis notes, "It also means addressing how you keep the data right once corrected. Many organizations we encounter have whole teams working exclusively on data fixes."
The implications of this trend are significant for businesses across industries. Companies are now exploring ways to achieve demonstrable return on investment (ROI) by reducing or eliminating the effort required for data correction and maintenance. This shift in focus could lead to more efficient resource allocation and improved data-driven decision-making capabilities.
RobobAI's platforms exemplify this approach, utilizing AI techniques such as natural language processing (NLP) and clustering to preprocess data, identify and reduce duplication, and enhance data records with missing attributes from other sources. This innovative use of AI for data preparation showcases the technology's potential beyond its traditional applications.
Curtis stresses the importance of considering the entire end-to-end model when building a case for AI implementation and understanding potential returns. He warns that while there is significant focus on analytics and AI, many organizations are overlooking the foundational aspects of data quality and preparation.
This insight from RobobAI's CTO serves as a crucial reminder for businesses eager to leverage AI technologies. It underscores the need for a comprehensive approach to data management as a prerequisite for successful AI deployment. Organizations that prioritize data quality and preparation are likely to see more successful outcomes in their AI initiatives and gain a competitive edge in their respective industries.
As AI continues to evolve and become more integral to business operations, the emphasis on data quality and preparation is likely to grow. Companies that heed this advice and invest in robust data management practices will be better positioned to harness the full potential of AI technologies, driving innovation and efficiency across their operations.
