DvSum blog

How important is a metadata catalog for managing big data?​

After businesses have realized the value of big data, data generation has been a forefront agenda. However, generating large quantities of data comes with its…

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Are Data-driven + fast decisions even possible?

In a fast-paced data-driven world, every business leader is under pressure to make quick business decisions and to make those decisions using data. However, for…

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3 ways data catalogs drive data migration

CIOs and CDOs are in the hot seat to deliver data and self-service capabilities to businesses. A part of that journey often requires leveraging data in the Cloud. But many Cloud Migrations have ended up with a Data Swamp, or have blown their delivery timelines and budget. A key reason is not being able to effectively inventory and prioritize data to migrate. In this post, we’ll talk about how Data Catalog combined with the Data Ranking technique can allow you to plan how you should sequence your data migration.

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4 steps to speed up Data Discovery and expand Self-Service Analytics

ata Discovery is a necessary step in Self-Service Analytics to find, prepare and analyze data to generate insights. However, it is slow and complex,  which results in analysts not being able to deliver timely insights or for business teams to be able to actually do self-service analytics.  In a previous article, I discussed this as a key challenge identified by Analytics leaders. You can read that post here.  In this article we talk about 4 steps to add Data Intelligence to your data using a centralized Data Catalog that will make Data Discovery faster and smarter.  

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Playing offense with Data Catalog to score on self-service analytics goal

Business Teams have access to Data?, self-service analytics platform and tools?, and have hired data analysts and scientists?‍?. But decision-makers are drowning in data and starving for insights. The biggest hurdle is the speed of Data Discovery by Analysts and Scientists.  Data Catalogs can help address this challenge by adding definition, context, profile, quality of data. It helps make data easy to find, understand, and trust. In turn, allow Analysts to build and ship analytics faster and deliver timely insights to the business.  

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