Article Data Architecture Data Management Data Quality 
Dataversity
AUGUST 19, 2022
How can your company redesign its data architecture without making the same mistakes all over again? The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all data architecture, and […].
Dataversity
DECEMBER 15, 2023
As we near the end of 2023, it is imperative for Data Management leaders to look in their rear-view mirrors to assess and, if needed, refine their Data Management strategies.
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Dataversity
MAY 16, 2022
In the context of a large system integration project, we are talking about awareness of: 1) Data Quality expectations and metrics, 2) Enterprise Data Management plan, 3) Data Governance best practices, 4) data risk factors, 5) Data Governance framework, 6) data owners/data consumers, 7) Data Architecture principles, 8) […].
Dataversity
MAY 9, 2022
Data fabric is redefining enterprise data management by connecting distributed data sources, offering speedy data access, and strengthening data quality and governance. This article gives an expert outlook on the key ingredients that go into building […].
Dataversity
MARCH 21, 2022
While data lakes and data warehouses are both important Data Management tools, they serve very different purposes. If you’re trying to determine whether you need a data lake, a data warehouse, or possibly even both, you’ll want to understand the functionality of each tool and their differences.
Sisense
AUGUST 28, 2020
However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. In this article, we’ll dig into the core aspects of data integrity, what processes ensure it, and how to deal with data that doesn’t meet your standards.
Dataversity
JULY 17, 2023
For data-driven organizations, this leads to successful marketing, improved operational efficiency, and easier management of compliance issues. However, unlocking the full potential of high-quality data requires effective Data Management practices.
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