erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.

Data Governance 40

Data Governance Risk Metadata Management 40

CIO Business Intelligence

MARCH 4, 2024

These domain data leaders often cite the diminishing returns and significant effort of central data team engagement. Additionally, data silos and fragmentation often occur inorganically as in the case of merger or acquisition scenarios.

Insurance 78

Insurance Strategy Data Governance Data Integration 78

erwin

APRIL 29, 2020

Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances. Data lineage offers proof that the data provided is reflected accurately.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Alation

NOVEMBER 22, 2022

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. Data integrity risks.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

AWS Big Data

JULY 20, 2023

Orca Security is an industry-leading Cloud Security Platform that identifies, prioritizes, and remediates security risks and compliance issues across your AWS Cloud estate. Moreover, running advanced analytics and ML on disparate data sources proved challenging. This ensures that the data is suitable for training purposes.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 77

Cost-Benefit Metadata Optimization Management 77

Octopai

JANUARY 19, 2021

With Octopai’s support and analysis of Azure Data Factory, enterprises can now view complete end-to-end data lineage from Azure Data Factory all the way through to reporting for the first time ever.

Metadata 52

Metadata ROI Optimization Data Quality 52

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. So, how can you quickly take advantage of the DataOps opportunity while avoiding the risk and costs of DIY?

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

erwin

MARCH 4, 2021

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

Jet Global

AUGUST 24, 2023

Reasons for Lingering On-Premises Many companies are willing to experiment with the cloud in other parts of their business, but they feel that they can’t put the quality, consistency, security, or availability of financial data in jeopardy. Thus, finance data remains on-premises.

Finance 52

Finance Reporting Data Integration Risk 52

Alation

OCTOBER 28, 2021

Leaders are asking how they might use data to drive smarter decision making to support this new model and improve medical treatments that lead to better outcomes. Yet this is not without risks. To make good on this potential, healthcare organizations need to understand their data and how they can use it.

Data Governance 52

Data Governance Measurement Metrics Modeling 52

DataKitchen

DECEMBER 9, 2022

In addition to speeding up the development and deployment of data-driven solutions, DataOps automation also helps organizations to improve the quality and reliability of their data-related workflows. The data scientists and IT professionals were starting to get frustrated, when suddenly, a magical fairy appeared out of nowhere.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

Jet Global

MAY 28, 2019

A data warehouse is typically used by companies with a high level of data diversity or analytical requirements. The only downside about a data warehouse is that, historically, it has a reputation for being complex, time-consuming, and expensive to build and maintain. Enhancing a Data Warehouse with Cubes.

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85