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

DataKitchen

OCTOBER 2, 2023

This lack of control is exacerbated by many people and/or automated data ingestion processes introducing changes to the data. This creates a chaotic data landscape where accountability is elusive and data integrity is compromised. The Hub Data Journey provides the raw data and adds value through a ‘contract.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

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

Precisely

MARCH 4, 2024

As you navigate the complexities of integrating AI into your operations, it’s essential to understand data integrity – data with maximum accuracy, consistency, and context – and its pivotal role in ensuring AI reliability. Next, you’ll see valuable AI use cases and how data integrity powers success.

Amazon Web Services 72

Amazon Web Services Data Integration High Quality Data Government 72

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 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

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

Databand.ai

JANUARY 24, 2023

Supporting all of this requires a modern infrastructure and data architecture with appropriate governance. DataOps helps ensure organizations make decisions based on sound data. Previously, organizations have grabbed their full dataset across multiple environments, put it all into a data warehouse, and surfaced information from there.

Retail 52

Retail Banking Government Manufacturing 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. Conduct Pre-Mortems 38.

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. Conduct Pre-Mortems 38.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52