Monte Carlo

APRIL 24, 2023

In an evolving data landscape, the explosion of new tooling solutions—from cloud-based transforms to data observability —has made the question of “build versus buy” increasingly important for data leaders. Check out Part 1 of the build vs buy guide to catch up. Missed Nishith’s 5 considerations?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Introduction Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. Examples include regular loading of CRM data and anomaly detection.

Data Ingestion 53

Data Ingestion Transportation High Quality Data Data 53

Monte Carlo

OCTOBER 11, 2022

We’ll then discuss how they can be avoided with an organizational commitment to high-quality data. Imagine this You’re a data scientist with a swagger working on a predictive model to optimize a fast-growing company’s digital marketing spend.

IT 52

IT Datasets Data Warehouse Data Analysis 52

Monte Carlo

JANUARY 25, 2023

There is, however, an added dimension to this relationship: data producers are often consumers of upstream data sources. Data warehouse producers wear both hats working with upstream producers so they can consume high-quality data and producing high-quality data to provide to their consumers.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

DataKitchen

JUNE 19, 2023

Checking data at rest involves looking at syntactic attributes such as freshness, distribution, volume, schema, and lineage. Start checking data at rest with a strong data profile. The image above shows an example ‘’data at rest’ test result. The central value here is ensuring trust through data quality.

Data 40

Data Data Validation Utilities High Quality Data 40