Data has long been a critical asset for businesses like yours to understand customers, operate more efficiently, inform go-to-market strategies, and retain your best employees. In a digital world, capturing and creating data-driven insights provides a major competitive advantage for those who can turn insights into action.

Data governance is a complex topic. In a nutshell, it refers to the aspect of data management concerning an organization's ability to ensure (A) that high data quality exists throughout the complete data lifecycle, and (B) that sufficient data controls are in place to support business objectives. In practice, data governance is the collection of processes, roles, policies, and standards that ensure a balance between access and control for information throughout an organization.

In part one and two, we introduced Iguazio's feature store and discussed the benefits of using one in the ML workflow. Additionally, we ingested and transformed the data that we will be using to train our model. In this blog, we will do the following.

The start of a new year is a perfect time to reflect on what was accomplished and look forward, re-evaluate what we can do better. Change, although difficult at first, can also be very rewarding. That’s why I was excited to see similar sentiments shared at Thoughtspot beyond.2021 to move beyond the traditional dashboards of the past.