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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

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Data Swamp, Data Lake, Data Lakehouse: What to Know

Alation

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. Many organizations have built a data lake to solve their data storage, access, and utilization challenges.

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Modern Data Architecture for Telecommunications

Cloudera

But digital transformation programs are accelerating, services innovation around 5G is continuing apace, and results to the stock market have been robust. . Previously, there were three types of data structures in telco: . Entity data sets — i.e. marketing data lakes . The challenges.

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A Guide to Data Analytics in the Travel Industry

Alation

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So Subscribe to Alation's Blog Get the latest data cataloging news and trends in your inbox.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

Data democratization, much like the term digital transformation five years ago, has become a popular buzzword throughout organizations, from IT departments to the C-suite. It’s often described as a way to simply increase data access, but the transition is about far more than that.

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A data strategy checklist for the journey to the data-driven enterprise

BI-Survey

Managers see data as relevant in the context of digitalization, but often think of data-related problems as minor details that have little strategic importance. Thus, it is taken for granted that companies should have a data strategy. But what is the scope of an effective strategy and who is affected by it?