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The future of data: A 5-pillar approach to modern data management

CIO

This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. By modern, I refer to an engineering-driven methodology that fully capitalizes on automation and software engineering best practices.

Data 167
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Are you ready for MLOps? 🫵

Xebia

Being ready means understanding why you need that technology and what it is. The time when Hardvard Business Review posted the Data Scientist to be the “Sexiest Job of the 21st Century” is more than a decade ago [1]. In 2019 alone the Data Scientist job postings on Indeed rose by 256% [2].

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Analytics operating system Redbird makes data more accessible to non-technical users

TechCrunch

Data engineers have a big problem. Almost every team in their business needs access to analytics and other information that can be gleaned from their data warehouses, but only a few have technical backgrounds. It is also hard for non-technical users to adopt, a problem that Redbird was created to solve.

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Fundamentals of Data Engineering

Xebia

The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. The authors state that the target audience is technical people and, second, business people who work with technical people.

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The key to operational AI: Modern data architecture

CIO

Adopting Operational AI Organizations looking to adopt Operational AI must consider three core implementation pillars: people, process, and technology. The team should be structured similarly to traditional IT or data engineering teams. To succeed, Operational AI requires a modern data architecture.

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What is data architecture? A framework to manage data

CIO

Many organizations today are looking to modernize their data architecture as a foundation to fully leverage AI and enable digital transformation. Consulting firm McKinsey Digital notes that many organizations fall short of their digital and AI transformation goals due to process complexity rather than technical complexity.

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

Use mechanisms like ACID transactions to guarantee that every data update is either fully completed or reliably reversed in case of an error. Features like time-travel allow you to review historical data for audits or compliance. data lake for exploration, data warehouse for BI, separate ML platforms).

Insurance 165