​In my last post, I highlighted the importance of pairing data platforms with the right expertise to solve business challenges. Today, I’d like to share a practical example of how I helped a business optimise its high-speed packing operations by tackling a key bottleneck: packing lane reconfigurations during run changes. Using predictive models and custom analytics, I empowered operators to make faster, data-driven decisions in this area. This reduced downtime and improved their ability to adapt to shifting priorities in real time. 

The explosion of data and analytics technologies has opened the door to immense opportunities for businesses of all sizes. Platforms like Snowflake, Databricks, and BigQuery simplify data access, speed up retrieval, and scale effortlessly. When paired with AI and machine learning (ML), these tools unlock the power to discover patterns, make predictions, and drive data-driven decisions.

These companies often tout claims of up to a 3x return on investment, using powerful marketing to make it seem like success is guaranteed once you subscribe to their services. But the reality is much more nuanced. For every company that achieves these results, many others struggle to see the promised benefits. The bold promises can quickly give way to frustration as organisations face the harsh truth: without a solid understanding of data science, they can't get the results they were hoping for from these tools.

So, what separates the successful companies from the rest?