Monte Carlo

OCTOBER 5, 2023

Ensure data quality Even if there are no errors during the ETL process, you still have to make sure the data meets quality standards. High-quality data is crucial for accurate analysis and informed decision-making. increased vigilance in maintaining thorough documentation and metadata.

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

FEBRUARY 8, 2024

GigaOm GigaOm’s Data Observability Radar Report covers the problem data observability tools look to solve saying, “Data observability is critical for countering, if not eliminating, data downtime, in which the results of analytics or the performance of applications are compromised because of unhealthy, inaccurate data.”

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Inconsistent data: Inconsistencies within a dataset can indicate inaccuracies.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Monte Carlo

AUGUST 30, 2023

A passing test means you’ve improved the trustworthiness of your data. Schedule and automate You’ll need to run schema tests continuously to keep up with your ever-changing data. If your datasets are updated or refreshed daily, you’ll want to run your schema tests on a similar schedule.

SQL 52

SQL Datasets Database High Quality Data 52

Monte Carlo

JUNE 20, 2023

So, just to be safe you take the extra time to recreate the dataset. Data products and trustworthiness According to Zhamak Dehgahi, data products should be discoverable, addressable, trustworthy, self-describing, interoperable and secure. In our experience, most data products only support one or two use cases.

Building 52

Building Data Datasets High Quality Data 52

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52