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AI Adoption in the Enterprise 2021

O'Reilly on Data

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. The second-most significant barrier was the availability of quality data. Relatively few respondents are using version control for data and models. Respondents.

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Why optimize your warehouse with a data lakehouse strategy

IBM Big Data Hub

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Several factors are driving the adoption of knowledge graphs.

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What is a customer data platform? A unified customer database

CIO Business Intelligence

Customer data platform defined. A customer data platform (CDP) is a prepackaged, unified customer database that pulls data from multiple sources to create customer profiles of structured data available to other marketing systems. Customer data platform benefits. Types of CDPs.

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The Rising Need for Data Governance in Healthcare

Alation

Healthcare is changing, and it all comes down to data. Data & analytics represents a major opportunity to tackle these challenges. Indeed, many healthcare organizations today are embracing digital transformation and using data to enhance operations. In other words, they use data to heal more people and save more lives.

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. And what are the commercial implications of semantic technologies for enterprise data? Source: tag.ontotext.com.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy. Discover why.