erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

AWS Big Data

APRIL 19, 2023

The goal of a data product is to solve the long-standing issue of data silos and data quality. Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights.

Technology 78

Technology Data-driven Machine Learning Sales 78

Cloudera

AUGUST 30, 2022

It outlines a scenario in which “recently married people might want to change their names on their driver’s licenses or other documentation. That should be easy, but when agencies don’t share data or applications, they don’t have a unified view of people. Data quality issues deter trust and hinder accurate analytics.

Data Architecture 77

Data Architecture Data Lake Metadata Data Warehouse 77

erwin

SEPTEMBER 19, 2019

These questions are the same ones we ask in data governance. They involve impact analysis, ownership and accountability, control and traceability – all of which effectively documented and managed business processes enable. Data sets are not important in and of themselves.

Data Governance 108

Data Governance Cost-Benefit Enterprise Modeling 108

Peter James Thomas

JANUARY 8, 2020

If you find the articles published on this site interesting and relevant to your work, then perhaps – like Neal Analytics – you would consider commissioning us to write a White Paper or some other document. If so, please just get in contact , or simply schedule an introductory ‘phone call.

Consulting 61

Consulting Data-driven Publishing Data Strategy 61

Alation

MAY 31, 2023

This means that specialized roles such as data architects, which focus on modernizing data architecture to help meet business goals, are increasingly important to support data governance. What is a data architect? Their broad range of responsibilities include: Design and implement data architecture.

Data Governance 52

Data Governance Metadata Data Architecture Data Quality 52

CIO Business Intelligence

FEBRUARY 20, 2023

A candidate who earns the certification will have proved their ability to build bundle configurations, pricing, output documents, and renewals and amendments. The certification emphasizes testing, governance, and integration with external systems within an organization’s infrastructure.

Consulting 69

Consulting B2B Cost-Benefit Sales 69

Ontotext

JANUARY 10, 2024

As a start, such a platform would need to support two major design patterns: semantic knowledge hub and semantic data fabric. We call knowledge hub the pattern of using knowledge graphs to better manage documents and unstructured content. This helps improve the way relevant documents are found, recommended, and so on.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

CIO Business Intelligence

JANUARY 30, 2024

“Always the gatekeepers of much of the data necessary for ESG reporting, CIOs are finding that companies are even more dependent on them,” says Nancy Mentesana, ESG executive director at Labrador US, a global communications firm focused on corporate disclosure documents.

Reporting 113

Reporting Data Quality Strategy Data-driven 113

erwin

OCTOBER 24, 2019

Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Cloudera

SEPTEMBER 7, 2023

The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.

Insurance 80

Insurance Risk Data-driven Finance 80

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Andrew White

JANUARY 9, 2022

Is there a good map that shows the connections between data, advanced analytics, digital, innovation, etc. I would have to admit that there are few documents that talk about all the connections across any set of topics. Most of D&A concerns and activities are done within EA in the Info/Data architecture domain/phases.

Analytics 64

Analytics Measurement Modeling Data-driven 64

DataKitchen

AUGUST 8, 2023

On 20 July 2023, Gartner released the article “ Innovation Insight: Data Observability Enables Proactive Data Quality ” by Melody Chien. It alerts data and analytics leaders to issues with their data before they multiply. It alerts data and analytics leaders to issues with their data before they multiply.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

CIO Business Intelligence

NOVEMBER 7, 2023

Opting for a centralized data and reporting model rather than training and embedding analysts in individual departments has allowed us to stay nimble and responsive to meet urgent needs, and prevented us from spending valuable resources on low-value data projects which often had little organizational impact,” Higginson says.

Data Warehouse 142

Data Warehouse Digital Transformation Reporting Data-driven 142

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Alation

OCTOBER 12, 2021

.” Gilbert further breaks down the data challenges Cbus faced, saying: “[Another] challenge was around understanding the data. We might have found some data but what does it mean? There was a lack of documentation and a very heavy reliance on IT and business SMEs. Evaluate and monitor data quality.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

BI-Survey

DECEMBER 22, 2020

Providing an easily accessible description of individual data sources and their dependencies and processing flows is an important step. Data catalog platforms are ideal for this purpose. The usability of data documentation for business users stands and falls with an overarching business glossary.

Data-driven 52

Data-driven Data Strategy Strategy Enterprise 52