When we think of the various people and teams making use of ML and DBMS, we can place them on a spectrum based on the composition of their work.

It’s hard to believe enterprise BI platforms have been around for three decades. In that time, they have served the purpose of collecting and analyzing large amounts of data to help businesses make more informed decisions. But in today’s data-driven economy, analysts struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret data in real-time.

Machine learning (ML), more than any other workflow, has imposed the most stress on modern data architectures. Its success is often contingent on the collaboration of polyglot data teams stitching together SQL- and Python-based pipelines to execute the many steps that take place from data ingestion to ML model inference.

As the Director of Demand Gen at ThoughtSpot, I’m responsible for optimizing our campaigns and every customer touchpoint. No biggie, right? I’m part of a lean marketing team at a high-growth company. We’re not in the business of generating leads – we’re about creating real demand for our products.