Purani mentioned the following challenges related to AI adoption: “AI projects are not always aligned with business strategy,” “failing to consider what tangible capabilities AI projects need,” “misunderstanding the probabilistic nature of AI,” and “articulating what business value they want AI to create, but not recalibrating organizational behavior in ways that will deliver value with the humans who interact with AI.”
Adopting AI impacts multiple business capabilities/operations.
These business operations include a business strategy, operating russia whatsapp number data model, enterprise architecture, engineering and operations, and change management. It also impacts people, processes, and technology.
Sonny Rivera of TIFIN AG believes, “We need to stop micro-optimizing archaic processes and start reimagining them in a GenAI world – across the whole data-to-insight value chain.”
Other Data Management Capabilities
Even when discussing fancy staff about AI, we must still come down to earth and focus on applying and improving foundational data management (DM) capabilities.
Let me share with you some takeaways from the conference that relate to core DM areas of expertise.
Data Quality (DQ)
According to C. Lwanga Yonke of Padouk Consulting, LLC, the key challenges to improving data or information quality include inefficient collaboration between data producers and consumers, decentralized data administration, and DQ being considered IT tasks. As Teri Hinds of First San Francisco Partners stated, some other reasons can be that DQ has different meanings to various people.