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The key to operational AI: Modern data architecture

CIO

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

In 2025, insurers face a data deluge driven by expanding third-party integrations and partnerships. Many still rely on legacy platforms , such as on-premises warehouses or siloed data systems. Step 1: Data ingestion Identify your data sources. First, list out all the insurance data sources.

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Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

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How AI orchestration has become more important than the models themselves

CIO

Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, weve already seen the launch and subsequent upgrades of dozens of competing models. From Llama3.1 to Gemini to Claude3.5 In fact, business spending on AI rose to $13.8

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

All industries and modern applications are undergoing rapid transformation powered by advances in accelerated computing, deep learning, and artificial intelligence. The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data.

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

CIO

In todays economy, as the saying goes, data is the new gold a valuable asset from a financial standpoint. A similar transformation has occurred with data. More than 20 years ago, data within organizations was like scattered rocks on early Earth.

Data 167
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5 machine learning essentials nontechnical leaders need to understand

TechCrunch

We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” Consider upskilling your current team of software engineers into data/ML engineers or hire promising candidates and provide them with an ML education.