Deliver Big Data and Databases as-a-Service on Kubernetes

Metadata is more relevant than ever. It continues to be a powerful enabler for high-value data-driven business activities across operations, analytics, and compliance. However, to maintain this relevance, metadata management must deal with the increasing complexity of today’s business use cases and hybrid data environments. Metadata management is notoriously manual which makes it slow in development and error-prone in maintenance. Metadata management needs better tool automation. Metadata management is typically siloed due to users managing metadata per tool or platform. Metadata management needs a unified solution that is suited to sharing, reuse, governance, and comprehensive views of distributed data.

PrecisionProfile – a bioinformatics technology company – focuses on enabling oncologists and research scientists to rapidly analyze genomic profiles and create personalized treatment plans for cancer patients. Like most organizations, PrecisionProfile struggled with the most time-consuming part of every analytics exercise - combing, cleaning, and shaping data into actionable information. With self-service data preparation, they were able to design and develop a platform to accelerate data pipelines, enabling scientists to spend more time analyzing data and formulating how they can leverage it to save lives. View this webcast to learn: Empower researchers and oncologists to spend a fraction of their time restructuring data. Reduced cycle time of a genomic clinical study from 1-3 months to 2-8 hours.

Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.

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.