Notes about Cutting-Edge Technologies and Everything

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly. This Magic Quadrant evaluates vendors of data science and machine learning (DSML) platforms. Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists. Coherent integration means that the core product and supporting portfolio provide a consistent “look and feel” and create a user experience where all components are reasonably interoperable in support of an analytics pipel

In a business context, capability-based planning is an approach that ensures that changes in an organization are aligned to the overarching strategic vision. The approach has its origins in defense and military planning by the likes of the US, UK, Australia and Canada. More recently, it has become popular in the business domain, particularly for developing systems and IT-related strategies. The overall theory is that those organizations with superior capability to execute strategy will win – whether they are the armed forces or a business. Whilst capability-based planning has long been the tool of trade for military planners and strategists, consultants and enterprise architects, it hasn’t yet been widely understood or adopted in the broader community. What is a Capability? A capability describes what the business does and should not be confused with just the skills or competencies of the people (which the HR function also refers to as a capability). So, a business capability is descri

While traditional data warehouses often took years to build, deploy, and reap benefits from, today's organizations want simple, agile, integrated, cost-effective, and highly automated solutions to support insights. In addition, traditional architectures are failing to meet new business requirements, especially around high-speed data streaming, real-time analytics, large volumes of messy and complex data sets, and self-service. As a result, firms are revisiting their data architectures, looking for ways to modernize to support new requirements. DMA is a modern architecture that minimizes the complexity of messy data and hides heterogeneity by embodying a trusted model and integrated policies and by adapting to changing business requirements. It leverages metadata, in-memory, and distributed data repositories, running on-premises or in the cloud, to deliver scalable and integrated analytics. Adoption of DMA will grow further as enterprise architects look at overcoming data challeng