Databand.ai

AUGUST 30, 2023

Data cleansing: Implement corrective measures to address identified issues and improve dataset accuracy levels. Data validation: Ensure new database entries adhere to predefined rules or standards to maintain dataset consistency. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

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. Data accuracy vs. data quality Data accuracy and data quality are related concepts but they are not synonymous.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Precisely

MARCH 14, 2023

Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?

Data Integration 52

Data Integration High Quality Data BI Data 52

DataKitchen

JUNE 19, 2023

The Data Journey, in this sense, provides transparency about the status and outcomes of individual tasks, offers insights into potential bottlenecks or inefficiencies in the sequence of operations, and helps ensure that scheduled tasks are executed as planned. Consider a data pipeline orchestrated by Airflow.

Data 52

Data Data Validation Utilities High Quality Data 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

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

DataKitchen

SEPTEMBER 21, 2023

Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data 70

Raw Data Data Business Intelligence High Quality Data 70

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

Airbnb Tech

NOVEMBER 28, 2023

Enable full visibility into the quality of our offline data warehouse and individual data assets. Composing the Score Before diving into the nuances of measuring data quality, we drove alignment on the vision by defining our DQ Score guiding principles. whereas others were more like proxies for quality (e.g.,

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Airbnb Tech

NOVEMBER 24, 2020

Consistency : certified data and metrics represent the single source of truth for key business concepts across all teams and stakeholders at the company. Timeliness: certified data has landing time SLAs , backed by a central incident management process. Fig 7: Example pipeline overview section from a Midas design spec.

Data Warehouse 52

Data Warehouse Certification Data Pipeline Data 52

Ascend.io

APRIL 27, 2023

Going into the Data Pipeline Automation Summit 2023, we were thrilled to connect with our customers and partners and share the innovations we’ve been working on at Ascend. The summit explored the future of data pipeline automation and the endless possibilities it presents.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 52