what is data design?

Data design, simply explained is the practice of using data techniques to enrich traditional design disciplines. It is the marriage of the analytical (data, algorithms, technology) and empathetic (behaviors, experiences, empathy) approaches to problem solving. You can read some more about it here.
In general terms, data science provides information on what is happening, and design methods help us to understand why those things happen, and what we should do about it. 
Data designers pull the strings that allow large, complex data sets to speak through computerized visualizations. While also using design tools to help identify strategic insights and functional requirements.

Designers ask: How can experiences be enhanced by data

While machine learning and data science bring sophisticated models to the table a design thinking approach uncovers many different contexts of use – making data more digestible and usable from an end user perspective.

Data scientists ask: How can patterns in data support experiences  
Why is it important? Data design allows us to maximise the value of data while enabling predictive & prescriptive analytics through real-time service augmentation and enriching qualitative insights with quantitative findings. These elements allow us to make the most of our data and uncover overlooked insights.