This session will address how to use large volumes of open-text data, for example the answers of a text question in an employee survey to identify issues, potential solutions and where to focus action. We'll start with an overview of best-practice approaches developed for 'small data' solutions and extend this to how a combination of human & AI can address today's big-data, multi-lingual situations.

The session will finish by discussing how to use text in a mixed-methods approach, and a discussion of strategies to use to effectively use qualitative and quantitative data to solve today's organisational challenges.

People Analytics teams in large organisations are increasingly having to deal with large volumes of text data, but the typical barrier that analyses hit is a 'so what?' from executives and other stakeholders. Most text analyses only scratch the surface of what is necessary to inspire effective change. Analysts too often approach text from a technical perspective, eager to apply the latest library or technique, without considering how skilled researchers have been tackling this challenge manually for decades.

This session will explore:

- The approaches qualitative researchers have developed to make sense of text data
- The history of 'AI' based text analytics
- What makes a good text analysis
- Story telling with text data
- Idea 5: Mixed methods approaches and research strategies.

Learning outcomes:

- Knowledge of pragmatic strategies of how to use text data to drive change
- An understanding of text analysis - how to identify what will be needed
- Knowledge of established techniques, and how to apply AI and machine learning at scale.