The expanded and strategic role of data and analytics in digital transformation is increasing the complexity of data, the number of variables to analyze and types of analysis and speed required for success. At the same time, to have an impact, data and analytics must be pervasive. This complexity is pushing the limits of current approaches and leading to unprecedented cycles of rapid innovation in data and analytics. AI is not the only thing driving rapid change in data and analytics. From the next generation of augmented analytics tools to the use of continuous intelligence to interpret streams of data from IoT or the potential of conversational interfaces, there is rapid evolution in how and where analysis can be deployed. Understanding the business impact of these changes will allow organizations to prioritize the innovations that will drive digital business.

This magic quadrant session discuss key trends and will highlight the results of the Data Science and Machine Learning Magic Quadrant.

Data and analytics is becoming strategic to organizations as they digitally transform. Yet, most organizations don’t have a systematic way to assess and optimize their data and analytics (including AI) investment options. The Return Opportunity Appetite Risk (ROAR) Portfolio Optimization Model is a framework to help CDOs, CIOs and data and analytics leaders across the organization identify, prioritize and select combinations of investments in data and analytics (value streams) that optimize business impact and align to key business initiatives and strategic intent.

This presentation describes how augmented analytics is changing the analytic process to bridge the gap between analytics and BI and DSML.
1.) Why is this considered a collision (as opposed to a convergence) and how do we recognize it?
2.) What are the implications for those that use, support and provide these tools?
3.) How should you respond to the collision in a way that launches analytics programs forward?