5 Ways to Fuel Your Big Data Analytics in 2018

The big data analytics market has undergone continuous transformation since its’ inception and continued in 2017 with new innovations and a strong move to the cloud. But from the view of a customer, the world should be getting simpler, not more complex, and they expect products to make deployments faster and easier. Instead of complex, “piece together your own architecture” approaches, 2018 will be a year in which customers can really focus on what’s important – the data and analytics – and not the underlying technologies that support them, whether on-premise, in the cloud, or hybrid.
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OTHER ON-DEMAND WEBINARS

Workshop: Getting started with cnvrg.io data science platform

cnvrg.io is used across industries to help enterprises accelerate AI from research to production by simplifying DevOps and dependencies involved in building production-ready ML. Whether you’ve just begun the free trial, you’re a new user, or you’re interested in looking deeper into cnvrg.io features, we invite you to join us in a live workshop on how to get started using cnvrg.io.
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Expert Panel: What's Ahead in Data Management in 2023

Data management is fundamental to every application. Managing this precious asset is an essential competency in modern businesses of every sort. Innovations in data platforms are being adopted, and data management approaches are evolving rapidly to keep pace. Increasingly, enterprises are converging their data warehouse, data lake, and other data management platforms onto distributed cloud-native infrastructures. As more types of data are consolidated into their platforms, enterprises implement more scalable DataOps pipelines and more comprehensive governance practices to manage it all. Want to learn more? This webinar brings together a panel of experts, moderated by James Kobielus, TDWI’s research director for data management.
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Data Analytics and AI Fraud, Waste, & Abuse

Join Google Cloud, SpringML, and Carahsoft for a webinar as we explore a framework for combating fraud, waste, and abuse. It’s not enough to chase down fraud after it happens — our solution helps you proactively identify fraud, waste, and abuse before it infiltrates your system and doesn’t penalize legitimate claims.
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Keeping the Pulse of Your Data – Why You Need Data Observability to Improve Data Quality

With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
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