Data Scientist Workbench Accelerates Predictive Analytics

Relational databases are old technology, right? Thirty years is a long time for a technology foundation to be as active as relational databases, but, like NFL coaches, we must “tolerate them until we can replace them.” Are their replacements here? In this webinar, we say no. Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage options like a kid in a candy store? We’ll discuss Hadoop’s continued potential relevance and the cloud storage option that seems vital. Use what when? This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.

Analytics-Enabled Data Quality and Data GovernanceJoin us for a FREE 1 hour webinar where we will review how statistical controls, machine learning and other analytical approaches can validate data and enforce governance in smarter ways, thus allowing for a more proactive approach to trusting and understanding data across the enterprise. We will also provide a demonstration of how this can be achieved quickly to achieve quick wins.

See in action, using a real-world use case from manufacturing, the latest toolkit for Process Excellence, powerful analytics and machine learning.How to use the Minitab toolkit to manage projects and analyse data following the Lean Six Sigma DMAIC structured approach.Discover how to power and digitalise your improvement projects and data analysis with best practice methods combined with intuitive tools.Explore how to solve problems and suggest innovations with whatever data you currently have available.Includes helpful examples of the capabilities and uses for Minitab Statistical Software, Companion by Minitab for project execution and tracking Continuous Improvement, and Salford Predictive Modeler Software for predictive analytics and machine learning.

Getting insights from observability data such as logs and metrics is essential for managing cloud services reliably. However, managing massive observability data volumes can be expensive and complex. Luckily observability data pipelines can help IT handle large data volumes and reduce costs by processing, routing, and filtering data across teams, tools, and storage options. Should you build your own or buy?