Article Data Architecture Data Governance Data Quality 
IBM Big Data Hub
AUGUST 4, 2023
When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” ” through a truly data literate organization. What is data democratization?
TDAN
JULY 2, 2019
The third and final part of the Non-Invasive Data Governance Framework details the breakdown of components by level, providing considerations for what must be included at the intersections. The squares are completed with nouns and verbs that provide direction for meaningful discussions about how the program will be set up and operate.
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TDAN
APRIL 16, 2019
The phrase “data architecture” often has different connotations across an organization depending on where their job role is. For instance, most of my earlier career roles were within IT, though throughout the last decade or so, has been primarily working with business line staff.
Octopai
APRIL 19, 2021
BI teams will have a better handle on their data’s history, its current status, and any changes it may have undergone. Without organized metadata management, the validity of a company’s data is compromised and they won’t achieve adequate compliance, data governance, or generate correct insights. Forbes Technology Council.
CIO Business Intelligence
NOVEMBER 29, 2022
A sea of complexity For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives. Layering technology on the overall data architecture introduces more complexity.
Cloudera
SEPTEMBER 15, 2022
The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.
TDAN
MAY 17, 2022
One of the greatest contributions to the understanding of data quality and data quality management happened in the 1980s when Stuart Madnick and Rich Wang at MIT adapted the concept of Total Quality Management (TQM) from manufacturing to Information Systems reframing it as Total Data Quality Management (TDQM).
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