New Product Development in the Age of IoT

New Product Development in the Age of IoT

If you’re responsible for new product development in your company, you will be familiar with the several steps of that process. Experts mostly separate the new product development process into seven or eight steps, starting with idea generation and finishing with a post launch review. The fact that more and more things become smart; i.e. they either feature some intelligence or they are connected and controlled through the IoT, has significant implications on new product development, particularly on its very first phases. Traditionally, ideation and screening of first product ideas have focused on research, brainstorming, SWOT analysis, market and consumer trends, and so forth. All these activities imply certain hypotheses and more or less tangible perceptions of products or product components. This works fine, as long as the final product is a one-way product; i.e. once produced and sold it won’t change (other than to age and break, ultimately). However, smart things aren’t on-directional, but bi-directional: they communicate, they change, and therefore their effects on consumers are far more complex and variable than those of their „dumb“ predecessors. The smarter a thing, or a group of things, is, the more complex the situations they will create for their environment and […]

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Big Data Product Development – The Datarella Interview

Big Data Product Development – The Datarella Interview

Today, we speak with Joerg Blumtritt (JB), CEO and Co-founder of Datarella, about product development based on Big Data. Q What is so special about product development based on Big Data? JB Big Data is not so much about technology, it’s more about letting go your traditional business practices: where you used to differentiate between data and meta data or between master data and transitional data, you now just see …. data. If you take Social Media data for example, the old way of analyzing things would have been taking the texts of postings a data and time stamps, geolocation, the profile of the author, etc. as meta data. However, for most contexts, it’s far more valuable to analyze the connections of different authors or it might be even more telling to include the geolocations to reveal the true meaning of the posting without understanding a single word of the language it as written in (BTW: this is how the NSA does Social Media monitoring) The second aspect of this is not to work hypothesis-driven, but in an explorative way: don’t restrict yourself by narrowing the scope – instead analyze all given variables. Q You mentioned Social Media monitoring. In […]

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