IBM Big Data Hub

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 78

Risk Modeling Management Metadata 78

Alation

OCTOBER 26, 2021

Although operations and sales departments tend to champion the use of data for business insight 3 , we’ve found that finance departments are often the first adopters of the Alation Data Catalog within an organization. This is because accurate data is “table stakes” for finance teams.

Finance 52

Finance Data Transformation Enterprise Metrics 52

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. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

erwin

SEPTEMBER 17, 2020

For example, automatically importing mappings from developers’ Excel sheets, flat files, Access and ETL tools into a comprehensive mappings inventory, complete with auto generated and meaningful documentation of the mappings, is a powerful way to support overall data governance. Data quality is crucial to every organization.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

erwin

APRIL 29, 2020

An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata? Who are the data owners? What are the transformation rules? Data Governance.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

datapine

SEPTEMBER 29, 2022

This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle. These processes could include reports, campaigns, or financial documentation. date, month, and year).

Data Quality 241

Data Quality Metrics Data-driven Management 241

Octopai

DECEMBER 11, 2022

Banks didn’t accurately assess their credit and operational risk and hold enough capital reserves, leading to the Great Recession of 2008-2009. Let’s take a look at several regulatory standards and explore automated data lineage’s role in smoothing and improving data compliance. Data lineage and financial risk data compliance.

Insurance 52

Insurance Risk Metadata Reporting 52