Any AI algorithm is only as good as the data you feed it, highlighting the need for a centralized, unified data architecture.
Artificial intelligence encompasses a wide range of data practices, including predictive modeling, generative AI, and agentic AI capable of formulating and executing multi-step operations. Use cases for AI include everything from decision support, such as forecasting, to copiloting creative and intellectual work, and automating complex business processes.
The main obstacle to all AI is data readiness. Most business AI use cases require a combination of comprehensive, up-to-date access to structured data produced by business operations as well as unstructured data contained in knowledge bases, correspondence, and other media. The age-old principle of “garbage in, garbage out” applies with full force – no matter how clever the algorithm, an AI model is only as good as the data it can access.
AI can’t succeed if the data it depends on is siloed and scattered across disparate data platforms, operational systems, and applications. Many organizations maintain separate environments for analytics and AI-driven products. They may also integrate data using engineering-intensive methods that not only consume engineering resources but may suffer reliability and governance issues.
The solution to this data readiness gap is first to recognize that AI is inseparable from data. One of our customers once asked their board for funding to build a data platform. They were told, “We have no money for data projects, but we have unlimited funds for AI innovation.” So, the customer changed the proposal title and returned to the board saying, “I need to build an AI platform” – and immediately gained the funding. The board’s attitude was emblematic of a broader failure to recognize the essential connection between AI and a mature data architecture.
Second, with this recognition, data leaders must develop a data strategy to centralize all data in a unified data architecture using automated data integration. Analytics and AI should not operate on separate tech stacks with separate datasets, methodologies, missions, and mandates. A mature, unified data architecture eliminates redundancy and duplicated governance and security expenses, ensures a single source of truth, and allows all projects to benefit from the full spectrum of an organization’s data sources. Automated data integration ensures that a data architecture can be sustained with modest overhead and that engineering resources can be redirected to high-value projects. Companies that have met these challenges see real results – faster insights, higher efficiency, and more AI innovation.
Saks, HubSpot and others demonstrate success
One of our customers, Saks, used the newfound flexibility and scalability of their unified data ecosystem to support a growing roster of data-driven products. Most notably, this included personalized, high-touch shopping experiences provided by customer service agents who can access AI-provided recommendations and sentiment analysis drawn from messages and calls.
Another customer, HubSpot, built a centralized data lake that now grants generative AI access to employee evaluations. This has led to projects to evaluate qualitative, text-based pre- and post-hire employee performance data, as well as planned projects to evaluate the fairness of interviews and performance reviews.
National Australia Bank, a leading financial institution, was similarly able to combine unified data with generative AI for several initiatives, including a banker chat assistant, fraud detection system, personalized communications with customers, and document review and summary.
Turn AI from concept to reality
Across each of these case studies, the takeaway is the same: a unified data architecture supported by automated data integration not only reduces the cost, complexity, and engineering overhead of all data-related projects but, more importantly, creates space to innovate, including AI initiatives that actually deliver.
Each hype cycle in the data industry – big data, machine learning, and now generative AI – has offered considerable but often unrealized promise. Organizations must recognize AI problems as data problems and develop a strategy to create a unified data architecture supported by automated data integration.
Fivetran’s mission aligns closely with this solution: empowering organizations to automatically integrate and centralize data in a unified architecture. By facilitating a single source of truth and flexible data infrastructure capable of supporting the full range of AI use cases, Fivetran is helping organizations worldwide turn AI from prototype and concept to production and reality.
Learn more about how Fivetran can help you get your data AI-ready. Visit: https://www.fivetran.com
