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Reflections on Strong Momentum and Category Leadership in Data Observability

Monte Carlo

When we launched the data observability category in 2020, we set out to solve a very real problem: data trust. Four years, hundreds of customers, and an entire category later and we’re just getting started. In the preceding months, I met with hundreds of data leaders about what kept them up at night. EMEA, and other markets.

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Healthcare Data Impact Awards finalists shine in Data for Good category

Cloudera

Keck Medicine of USC (Keck) has been nominated in the Data for Good category, which is awarded to organizations that tackle challenging issues affecting society and the planet, helping transform the future. The post Healthcare Data Impact Awards finalists shine in Data for Good category appeared first on Cloudera Blog.

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Unicorns, data mesh, category creation, and more reasons to attend IMPACT: The Data Observability Summit

Monte Carlo

The category. We have a few tricks rolled up our sleeves (no pun intended), so if anything, tuning into IMPACT will be a welcome break from your back-to-back Zoom meetings and WFH routine. If you’re a fan of Will Robins’ Monster Mash parody , you’ll want to stick around after the third session.

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Monte Carlo Expands Leadership Team from Snowflake, Segment to Support Hypergrowth of Data Observability Category

Monte Carlo

When I learned about the emerging discipline of data observability, and how Monte Carlo is pioneering a new product category with such enormous potential, I couldn’t pass up the opportunity to join their incredible team.” “Data quality is a massive challenge for businesses of every size and across every industry,” said Day.

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How to Build Data Experiences for End Users

End users fall into 4 different categories along the data literacy continuum when it comes to their skill level with data: Data challenged: Users have no-to-low levels of analytics skills or data access. Product managers need to research and recognize their end users' data literacy when building an application with analytic features.

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Unlocking the Power of Containers: Exploring the Top 20 Docker Containers for Every Development Need

Analytics Vidhya

This article delves into the top 20 Docker containers across various categories, showcasing their features, use cases, and contributions to streamlining development workflows.

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What are the Commonly Used Machine Learning Algorithms?

Knowledge Hut

The categories are : SUPERVISED UNSUPERVISED SEMI – SUPERVISED In supervised ML algorithms, the user knows both the Input and Output data before applying any algorithm on the data. A ll the algorithms in this category have a probabilistic approach in solving the problems. We can draw probabilistic insights from the data.

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.