Show notes
Time stamps - Let’s understand what is a data scientist doing in user research operations (2:35) | Creating an in-house repository: Understanding how research ops at IBM work (6:24) | The journey of creating a research repository (9:36) | Hunting for honest textual data (15:30) | Natural Language Processing for data analysis (18:35) | Sentiment analysis (19:40) | Clustering (20:07) | Named entity recognition for competitive analysis (20:52) | Choosing the appropriate source of data (22:24) | Benefits of having a vision for your data and data analysis (23:43) | Human intelligence and artificial intelligence going hand in hand (24:43) | Connecting the dots between qualitative and quantitative data (28:19) | Pitfalls of NOT triangulating (31:05) | Getting more honest data out of surveys (33:05) | Actions vs words (34:10) | Research the researchers: setting researchOps(35:56) | Winning the confidence of your stakeholders (42:55) | Getting the quantitative edge as a user researcher (44:36) | Navigating as a user researcher in the world of numbers (49:12) | The biases of a data scientists and user researchers (50:14) | Grishma’s learnings from the world of user research (54:27) | How lack of team collaborations can negatively affect users (59:28) | The importance of the honest metrics (1:01:50) | How not fatigue your users when it comes to user interviews (1:03:10) | Links of topics discussed in the podcast - Dovetail: https://dovetailapp.com/ | Airtable: https://www.airtable.com/ |
