One of the surprises about the sudden and widespread interest in Synthetic Data is that it has forced us to revisit things we have been doing for decades. This revisiting of old practices is useful for framing where we think Synthetic Data should go and how it should be evaluated and regulated. In this post I explore why weighting is a form of synthetic data and what that means for researchers.

AI is a hot topic, and in the insight and research ecosystem, the hottest topic is Synthetic Data. In this post, I look at what Synthetic Data means, why it is such a hot topic, some of the key issues, and share some thoughts about Synthetic Data and its use.

The third wave of the NewMR study looking at the state of insights has just closed its data collection and we have the views of 330 insight and research professionals from around the world. A few days ago, I shared a word cloud based on one part of the data. As the next step in looking at the data, I used ResearchWiseAI to produce an automated summary. This was achieved by loading a raw export of the 330 responses into the platform and I asked the following question “What is the state of optimism/pessimism in the research and insights world? What are the main things that researchers are positive about and worried about?”