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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.

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5 Ways Data Engineers Can Support Data Governance

Alation

These data requirements could be satisfied with a strong data governance strategy. Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. How can data engineers address these challenges directly?

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Of Muffins and Machine Learning Models

Cloudera

Model interpretability is one of five main components of model governance. In this article, we explore model governance, a function of ML Operations (MLOps). Each workspace is associated with a collection of cloud resources. The complete list is shown below: Model Lineage . Model Visibility. Model Explainability.

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Driving Business Value and ROI from a Hybrid Cloud Data Lake

Alation

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. With an on-premise deployment, enterprises have full control over data security, data access, and data governance. Data that needs to be tightly controlled (e.g. The Problem with Hybrid Cloud Environments.

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

IBM Big Data Hub

When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” ” through a truly data literate organization. What is data democratization?

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How to Build a Customer Centric Business: The Complete Guide

Alation

Customer centricity requires modernized data and IT infrastructures. Too often, companies manage data in spreadsheets or individual databases. This means that you’re likely missing valuable insights that could be gleaned from data lakes and data analytics.