When incremental improvements to your machine learning model don't move the needle enough, consider ways to capture new data points through product features and website checkout processes.

Some SaaS metrics are commonly used without questioning how they optimize results. Consider KPIs relevant to your business and how to implement a BI metrics layer that serves the entire company.

When existing machine learning models perform well with structured data, incorporating deep learning models from unstructured data can be easier than expected, and improve outcomes along the way.

A common theme in Data Science and Analytics is how challenging it is to get a project across the finish line. Successful projects require Operations, Product Management, and a good Data leader.

Optimize opportunity win-rates with a machine learning approach to lead scoring, while avoiding the pitfalls of wrong incentives and arbitrary thresholds.

Being data driven only brings value if your datapoints are optimized to solve business problems. Understand the relationship between data and outcome.

The path to becoming a Data Scientist has evolved over time, with a DIY education changing to degree based programs, as well as a narrowing of skills in the specialization of data professions.

There are three ways a product analytics team adds meaningful value in an organization, from individual product feature metrics, to overall product engagement metrics, through to customer success and business value.

When hiring a Data Scientist it might be helpful to consider all current strengths on the team and balance them with other skills. Identify differences in strengths between math and stats, coding, and domain expertise.