The Data Administration Newsletter
JUNE 20, 2023
These days, there is much conversation about the necessity of the data model. The data model has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the data model really out of date? And exactly why do we need a data model, anyway? […]
The Data Administration Newsletter
JULY 6, 2021
Larry Burns’ latest book, Data Model Storytelling, is all about maximizing the value of data modeling and keeping data models (and data modelers) relevant. Larry Burns is an employee for a large US manufacturer.
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Dataversity
JULY 12, 2021
In my eight years as a Gartner analyst covering Master Data Management (MDM) and two years advising clients and prospects at a leading vendor, I have seen first-hand the importance of taking a multidomain approach to MDM. Click to learn more about author Bill O’Kane.
The Data Administration Newsletter
APRIL 18, 2023
The foundation of a business’s digital transformation is effective data management. Master data management services allow you to effectively utilise the new currency of data and effectively collaborate between different functional verticals, departments, and stakeholders for better productivity, efficiency, and […]
The Data Administration Newsletter
AUGUST 16, 2022
In the first article, I introduced and explained the approach to application development called Domain-Driven Development (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed data model can add value to a DDD project by helping to create the Ubiquitous Language that defines the Bounded Context (..)
Astera
JULY 13, 2023
One of the key benefits of a data lake is that it can also store unstructured data, such as social media posts, emails, and documents. This makes it a valuable resource for organizations that need to analyze a wide range of data types. Data Mapping and Transformation: Data mapping bridges data elements from diverse sources.
Astera
JANUARY 2, 2024
Today, this means they should have the necessary resources and infrastructure to be able to deal with big data—large volumes of structured and unstructured data—efficiently. This also includes maintaining data quality while ensuring easy access to the needed data.
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