Systems | Development | Analytics | API | Testing

A lot of things got easier in 2021 as technology advanced tremendously to continue making the digital world more accessible and integrated into our everyday lives. But luckily there was something that got harder in 2021: being irresponsible with data. You see, with great power comes great responsibility, and as people’s data gets poured online, more and more restrictions appear to ensure that data is always safe, without exceptions.

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.

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.

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.

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.