Data visualization process yields 360 AI-driven analytics view

Data visualization tools are sometimes seen as entry-level analytics. Most vendors boast easy-to-use tools that can be picked up by line-of-business users who lack formal analytics training. In most minds, AI and machine learning exist on totally different planes due to their complexity and the skill level they demand.

But there's not as much of a sharp dividing line as you may think. Increasingly, data scientists and AI developers are incorporating the data visualization process into their routine, using tools like Tableau or Qlik to visually explore their data sets and get a sense of what kind of features they contain. These tools also benefit the back end. Once a model has crunched the numbers and made a recommendation, data visualization tools can interpret the findings and present them in a way that's more interpretable than an equation or line of code would be.

Gartner's latest Hype Cycle report on data science and machine learning tools points out that we're already seeing the merging of AI and data visualization through augmented analytics, which enables users to visually explore large data sets and build complex models that utilize natural language processing and other aspects of machine learning -- all with little or no coding. The analyst firm expects these tools to reduce analytical errors and afford front-line workers access to more contextualized information.

This handbook examines some of the ways enterprises are using a machine learning-driven data visualization process to map out complex business processes, how visualizations can help tell complicated data stories and what kind of features buyers should look for in visualization tools.

By

• Ed Burns