This article was written for MarTech by Greg Kihlström. Read the full article here.
We’re experiencing a conflict between the economic pressure to produce quick and cheap research results and the scientific demand for rigor. Hundreds, if not thousands, of lifelike personas can be generated within minutes by vendors promising strong results. But these often operate as methodological black boxes, producing outputs that can’t be validated, may contain hidden bias and can quietly mislead decision-making.
The synthetic data market is growing quickly, with valuations projected to surge from approximately $267 million in 2023 to over $4.6 billion by 2032. Driven by demand for instant insights in an always-on economy, 95% of insight leaders plan to use synthetic data within the next year and the appeal is clear. Speed, scale, cost efficiency and the ability to generate insights from niche audiences are key drivers.
To move synthetic testing from a purely experimental approach to a reliable, scalable practice, organizations need to address these risks directly. Several approaches can help overcome skepticism and create a more sustainable model. It’s important to identify the key problem areas and address them directly.
While cost savings and speed to insights are compelling reasons for adoption, several challenges remain. The most successful organizations understand the strengths and weaknesses of different synthetic tools and when to use them.
This article was written for MarTech by Greg Kihlström. Read the full article here.
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