<\/p>\n
Companies need to be ready with the right data architecture, and the next few months \u2014 years, at most \u2014 will be critical, says Irfan Khan, president and chief product officer of SAP Data & Analytics.<\/p>\n
“The only prediction anybody can reliably make is that we don’t know what’s going to happen in the years, months \u2014 or even weeks \u2014\u00a0ahead with AI,” he says. “To be able to get quick wins right now, you need to adopt an AI mindset and … ground your AI models with reliable data.”
While data has always been important for business, it will be even more so in the age of AI. The capabilities of agentic AI will be set more by the soundness of enterprise data architecture and governance, and less by the evolution of the models. To scale the technology, businesses need to adopt a modern data infrastructure that delivers context along with the data.<\/p>\n
Traditional views often conflate structured data with high value, and unstructured data with less value. However, AI complicates that distinction. High-value data for agents is defined less by format and more by business context. Data for critical business functions \u2014 such as supply-chain operations and financial planning \u2014\u00a0is context dependent. While fine-grained, high-volume data, such as IoT, logs, and telemetry, can yield value, but only when delivered with business context.<\/p>\n
For that reason, the real risk for agentic AI is not lack of data, but lack of grounding, says Khan.<\/p>\n
\u201cAnything that is business contextual will, by definition, give you greater value and greater levels of reliability of the business outcome,\u201d he says. “It\u2019s not as simple as saying high-value data is structured data and low-value data is where you have lots of repetition \u2014\u00a0both can have huge value in the right hands, and that\u2019s what\u2019s different about AI.”<\/p>\n
Context can be derived through integration with software, on-site analysis and enrichment, or through the governance pipeline. Data lacking those qualities will likely be untrusted \u2014 one reason why two-thirds of business leaders do not fully trust their data, according to the Institute for Data and Enterprise AI (IDEA). The resulting “trust debt” has held back businesses in their quest for AI readiness. Overcoming that lack of trust requires shared definitions, semantic consistency, and reliable operational context to align data with business meaning.<\/p>\n
Over the past decade, the most important shift in enterprise data architecture has been the separation of compute and storage, cloud-scale flexibility, says Khan. Yet, that separation and move to cloud also created sprawl, with data housed in multiple clouds, data lakes, warehouses, and a multitude of SaaS applications.<\/p>\n<\/p><\/div>\n
#Building #strong #data #infrastructure #agent #success<\/p>\n","protected":false},"excerpt":{"rendered":"
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