Michael Schindl
Nov 1 · 2 min read

Quantum leap for Big Data possible ? Is there anybody out there ?

Yuriy, thank you for your highly instructive and precise article ! One could say that Machine Learning + Big Data can produce predictions based on machine-created(non-transparent) assumptions about cause-and-effect-relationships between past activities of the consumer (e.g. someone who reads fitness content online and stops by at ice cream stands may be interested in dietary food). In other words, by interlinking the WHATS of the past it predicts WHAT will happen in the future.

The other approach — that of LIFEWORLDS (understood as the sum of internal drivers) + the context (understood as the sum of external drivers) => together Thick Data, knows the cause-and-effect-relationships (so no assumptions about these have to be made) as these relationships are researched boots-on-the-ground and face-to-face at the consumer’s level. In other words, by applying the WHYs of the past to future situations (e.g. product offers) this approach predicts WHAT will happen in the future.

As opposed to the ML/Big Data approach, the lifeworld research cannot deliver on an industrial scale and still needs well instructed and competent humans as researchers.

The real quantum leap would be to validate or enrich the assumptions made by a ML/Big Data model with the findings of a related lifeworld research — and to enter the result into the Machine Learning circuit again. That would result in a prediction quality unequalled to none !

As we are not competent enough about ML (as Yuriy has well demonstrated yet kindly has concealed) we are looking for people who are, and who work with ML/Big Data models in practice and who want to improve their validity in a given case.

IS THERE ANYBODY OUT THERE ?

Please take contact — we could further develop science and practice together in a really relevant field.

Michael Schindl

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