All analytics projects have data as their foundation and this data is usually spread across a variety of databases, storage systems and locations. This diverse and complex landscape causes data scientists to spend an inordinate amount of time searching for the right data and preparing this information for analytics. It’s
Often, when a cybersecurity incident occurs, the clues to how it happened and who caused it are hidden in network data. In the example discussed here, data scientists were asked to identify who caused a global internet outage by examining a large graph of network data with data visualization. This
A note from Udo Sglavo: A wealth of connectivity is pervasive in the data we gather across many industries. In other words, networks are all around us. A data science trend you cannot ignore is to organize, learn from, and drive decision-making based on connected data. Network analytics engines provide efficient
Conversational AI can offer a way to provide that always-on 24/7, fast, convenient experience that can go anywhere (phone, computer smart speakers, even your car). It can provide a human-like experience through real-time, personalized interaction with AI running in the background. This technology is being applied across many industries for a variety of use cases (both customer-facing and for internal use).
An analyst report offers an unbiased, side-by-side, third-party evaluation of the technology in the market. These analysts know how to put the vendors through the paces and require proof of any claims that are made.
The first principle of analytics is about bringing the right analytics technology to the right place at the right time. Whether your data are on-premises, in the cloud, or at the edges of the network – analytics needs to be there with it. Being true to this principle means we