First, Behavioral data is primarily sequential and constantly evolving, rather than static and fixed – and with its thousands of data points per individual, there is a sheer unlimited number of potential temporal interdependencies and contextual correlations to look for. To say it simply: It’s a fundamentally different category beast than what is being taught at Statistics 101. Existing business intelligence tools, as well as regression or tree-based models struggle in making sense of this type of data at scale. Thus it is no surprise that only the most data-savvy organizations turn up on the winning side by knowing how to leverage their immense behavioral data assets to effectively gain a competitive edge with hyper-personalized customer experiences.

The second obstacle is that behavioral data remains primarily locked up. Because with thousands of available data points per customer the re-identification of individual subjects becomes increasingly easy. Existing anonymization techniques (e.g. data masking), that have been developed to work for a handful of sensitive attributes per subject, stand no chance in protecting privacy while retaining the utility of this type of data at a granular level. A disillusion that is by now also broadly understood and recognized by the public.

As it turns out, these are two reinforcing effects. Without safe data sharing, you can’t establish data literacy around behavioral data. Without data literacy, you will not see the growing demand for behavioral data in your organization. However, only some companies will remain stuck in their inertia, while others are able to identify and thus address the dilemma by turning towards synthetic data, which allows them to offer smart, adaptive, and data-driven services to win the hearts of the consumers (as well as the markets).