The Modern Data Company

FEBRUARY 3, 2023

Get to the Future Faster – Modernize Your Manufacturing Data Architecture Without Ripping and Replacing Implementing customer lifetime value as a mission-critical KPI has many challenges. Companies need consistent, high-quality data and a straightforward way to measure CLV.

Retail 97

Retail Manufacturing High Quality Data Data Architecture 97

The Modern Data Company

FEBRUARY 3, 2023

Get to the Future Faster – Modernize Your Manufacturing Data Architecture Without Ripping and Replacing Implementing customer lifetime value as a mission-critical KPI has many challenges. Companies need consistent, high-quality data and a straightforward way to measure CLV.

Manufacturing 98

Manufacturing High Quality Data Data Architecture Architecture 98

The Modern Data Company

FEBRUARY 2, 2023

Get to the Future Faster – Modernize Your Manufacturing Data Architecture Without Ripping and Replacing Implementing customer lifetime value as a mission-critical KPI has many challenges. Companies need consistent, high-quality data and a straightforward way to measure CLV.

Systems 52

Systems Manufacturing High Quality Data Data Architecture 52

Monte Carlo

MARCH 28, 2024

And this idea of data as a product is kind of a continuum shift to start to change that.” Kyle Shannon , Senior Data & Analytics Engineer at SeatGeek, shared in the same webinar that his company is focusing on scalability due to the rapid growth of their data team.

Data 52

Data High Quality Data Machine Learning Accessible 52

Monte Carlo

MAY 17, 2023

Before data scientists or data analyst can do anything interesting with the data, they often need to spend time verifying the lineage, ensure there aren’t any missing rows, and other general cleaning tasks. Keeping these revenue generators online and accurate is a common data observability use case.

Data 52

Data Data Pipeline Data Engineer Data Engineering 52

Monte Carlo

MAY 17, 2023

Before data scientists or data analyst can do anything interesting with the data, they often need to spend time verifying the lineage, ensure there aren’t any missing rows, and other general cleaning tasks. Keeping these revenue generators online and accurate is a common data observability use case.

Data Pipeline 52

Data Pipeline Data Engineer Data Engineering Data 52