February, 2024

Remove high-quality-data-accessibility
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5 Reasons to Use APIs to Unleash Your Data

Precisely

Data is a key ingredient in deeper insights, more informed decisions, and clearer execution. Advanced firms establish practices to ensure data quality, build data fabrics, and apply insights where they matter most. Data quality and contextual depth are essential elements of an effective data-driven strategy.

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5 Skills Data Engineers Should Master to Keep Pace with GenAI

Monte Carlo

If you’re a data engineer experiencing GenAI-induced whiplash, you’re not alone. On one hand, everyone’s talking about whether GenAI’s not-insignificant data engineering skills are going to automate away their jobs. They need robust data pipelines, high-quality data, well-guarded privacy, and cost-effective scalability.

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4 Ways to Tackle Data Pipeline Optimization

Monte Carlo

Just as a watchmaker meticulously adjusts every tiny gear and spring in harmonious synchrony for flawless performance, modern data pipeline optimization requires a similar level of finesse and attention to detail. Learn how cost, processing speed, resilience, and data quality all contribute to effective data pipeline optimization.

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Leveraging Predictive Analytics for Improved Patient Care and Operational Excellence

Striim

Their role in integrating real-time data is crucial, ensuring quality care and operational efficiency, two pillars essential for modern healthcare success. This challenge often stems from limited access to real-time patient data, which hinders the delivery of personalized care and robust patient engagement.

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Maximizing the Value of Your Address Data with Geo Addressing

Precisely

Address data: for many businesses, it falls into one of two categories – a valuable asset, or a major (and costly) headache. What makes address data so challenging for those that fall into the latter category? Identifiers, such as the PreciselyID , play a powerful role in data analysis and enrichment.

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4 GenAI Opportunities from Real Data Teams

Monte Carlo

But, when you find a data leader who’s on the real AI journey first-hand (no, not Midjourney), it’s natural to have a few questions. Data asset optimization is the need of the hour 4. Data asset optimization is the need of the hour 4. Reliable AI needs reliable data. Data observability is the cost of admission.

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GPT-based data engineering accelerators

RandomTrees

GPT-based data engineering accelerators make the working of data more accessible. These accelerators use GPT models to do data tasks faster, fix any issues, and save a lot of time. GPT models change data in simple language and also provide summaries and explanations. One can rely on this information.