How Modern Data Stacks Hinder Experimentation and Agility

Your modern data stack is not designed for the experimentation and agility required by artificial intelligence (AI) projects. It’s time to rethink and rebuild data stacks with AI in mind.

A modern data stack brings together an array of tools, technologies, and platforms to help organizations manage, process, analyze, and visualize vast amounts of data effectively. However, many of these tools and practices are rooted in traditional business intelligence (BI) architectures, which may not be well-suited for AI applications.

While stability and reliability are essential, data stacks and business processes that prioritize stability can hinder data scientists from creating successful AI applications. The thing is, AI is fundamentally different from BI. According to the no free lunch theorem, it’s impossible to determine the ideal algorithmic and data solution in advance. As a result, data scientists must experiment with new data sources, data manipulations, and machine-learning algorithm combinations.

Moreover, AI systems are not static solutions. They demand that your data stack supports rapid iterations, experiments, and quick time-to-market. Machine learning models, which form the backbone of modern AI, depend on historical data patterns to make future predictions. When these patterns change, models can break, leading to “model drift” and potential failures. For instance, in 2021, a telco realized that their AI-powered network routing needed urgent upgrades after failing to meet contractual service standards for their customers. The rise of remote work and videoconferencing during the COVID-19 pandemic caught their AI system off guard. Machine learning models require regular retraining and updating, which might necessitate frequent modifications to the data pipeline. A modern data stack built for stability will not be agile enough to keep up with the rapid iterations needed to maintain AI systems.

To harness the full potential of AI projects, it is crucial to move beyond traditional data stacks and adopt a more agile, AI-centric approach that enables rapid experimentation, iteration, and adaptation to ever-changing data patterns.