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In my eight years as a Gartner analyst covering MasterDataManagement (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.
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These days, there is much conversation about the necessity of the datamodel. The datamodel has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the datamodel really out of date? And exactly why do we need a datamodel, anyway? […]
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In the first article, I introduced and explained the approach to application development called Domain-Driven Development (or DDD), explained some of the DataManagement concerns with this approach, and described how a well-constructed datamodel can add value to a DDD project by helping to create the Ubiquitous Language that defines the Bounded Context (..)
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Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].
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A NoSQL database is a non-relational database that stores data in a format other than rows and columns. NoSQL databases come in a variety of types based on their datamodel. The main types are: Key-value stores: Data is stored in an unstructured format with a unique key to retrieve values. What is a NoSQL Database?
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Can the responsibilities for vocabulary ownership and data ownership by business stakeholders be separate? I have listened to many presentations and read many articles about data governance (or data stewardship if you prefer), but I have never come across anyone saying they can and should be. Should they be?
The platform leverages a high-performing ETL engine for efficient data movement and transformation, including mapping, cleansing, and enrichment. Key Features: AI-Driven DataManagement : Streamlines data extraction, preparation, and data processing through AI and automated workflows.
In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on MasterDataManagement (MDM), the creation of a single, reliable source of masterdata, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.
AI can also be used for masterdatamanagement by finding masterdata, onboarding it, finding anomalies, automating masterdatamodeling, and improving data governance efficiency. Early research shows that AI could have cost-saving benefits for companies with complex supply chains.
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