Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

DataKitchen

OCTOBER 2, 2023

Whether the Data Ingestion Team struggles with fragmented database ownership and volatile data environments or the End-to-End Data Product Team grapples with real-time data observability issues, the article provides actionable recommendations. ’ What’s a Data Journey?

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Monte Carlo

AUGUST 30, 2023

Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. Schedule and automate Just like schema tests, custom data tests in dbt are typically not run just once but are incorporated into your regular data pipeline to ensure ongoing data quality.

SQL 52

SQL Datasets Database High Quality Data 52