When faced with new innovations like GenAI, every company must avoid pitfalls that result in unscalable and unsustainable solutions.

There’s no question which technology everyone’s talking about in retail. Generative AI continues to promote incredible levels of interest with its promise of next-level productivity and new kinds of employee and customer experience. It’s all happening at light speed. When ChatGPT burst onto the scene, it gained hundreds of millions of users in a matter of months.

It’s a new world—again. Data today isn’t what it was five or ten years ago, because data volume is doubling every two years. So, how could ETL still be the same? In the early ‘90s, we started storing data in warehouses, and ETL was born out of a need to extract data from these warehouses, transform it as needed, and load it to the destination. This worked well enough for a time, and traditional ETL was able to cater to enterprise data needs efficiently.

Future-proofing your analytics tech stack is essential for ensuring the longevity and success of your software applications. As the final stage of the data journey, analytics transforms raw data into actionable insights that directly impact business decisions and customer satisfaction. To effectively fulfill this role, analytics systems must possess a high degree of flexibility and scalability, seamlessly integrating with diverse applications and data sources.