While cloud services offer powerful computing resources, hasty migrations often neglect essential data infrastructure. Without this foundation, businesses struggle to unlock the full potential of the cloud, including technologies like artificial intelligence.
Traditionally, to implement AI, businesses invested heavily in on-premises server infrastructure, data warehousing, and dedicated teams. However, cloud computing simplified access to such capabilities. Yet, many realized they still had work to do. This involved laying engineering foundations, changing data processing methods, and adopting data lakehouses to store vast amounts of structured and unstructured data.
The shift from an ETL (extract, transform, load) approach to an ELT (extract, load, transform) approach became significant. The ELT approach prioritizes working with raw data, especially with the cloud’s power and scalability. However, data now must be structured before use. The Medallion Architecture’s gold, silver, and bronze categories exemplify this, with bronze representing raw data that requires structure.
This structured data fork leads in one direction toward analytics, insights, and business reporting and in another toward predictive models and AI. However, organizations must pave the initial road; both analytics and AI rely on the same data, but they must load it to serve both ecosystems.
Beyond structuring data, data sources must be consolidated. Organizations must transform data and ensure that data lakehouses incorporate information from various business systems, such as finance and sales. A consolidated data source enables comprehensive, real-time business reports and answers to questions without involving multiple departments or applications.
AI’s use cases include generative models for natural language processing, such as ChatGPT, and predictive analytics to identify trends and predict future outcomes. Implementing AI correctly begins with a well-architected and well-implemented foundation.
Businesses may not immediately see a return on investment, but laying the right IT infrastructure and data structures paves the way for future use cases, ensuring they can harness the full potential of AI as new opportunities arise. This strategic approach is well-understood by CIOs, CTOs, and IT departments.
