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Human or machine? It’s not either or, it’s and and

In most processes imagination and curiosity comes either before or after data validation. Brains first, heart later or the other way around. We have gut-feelings, vision, and ideas that we seek affirmation for in data, most often after we implement, the initiatives decided. And then we use data to find out how to optimize the performance in hindsight. Or we mine data to find a need that isn’t catered to at all or that competitors fail to meet to the full satisfaction of the customers in the market. 

The trouble is that when we separate the two, the value creation is dependent on the interpretations of one or few individuals. Especially when data comes before imagination and creation. The data we get – being historical – does not say anything about how consumers would react to any proposition that is new. So, the people looking at the data has to guess, which insights show a potential to create new demand and which just provide a better understanding of customers. Some insights will be about upper funnel perspectives on how to create attention and engagement and some will be related to lower funnel conversions and customer experience. But looking at the broader perspective across data sets it can be hard to separate them.

When data comes late to the party the issues, we face are the opposite. Since we already made key decisions and by that excluded many of the paths within the realm of the possible all data can help us do is sub-optimize. We can get lots of detail and granular insights on how to optimize the initiatives taken to market. But what data doesn’t tell us is whether there are one or more initiatives we could have taken, that would lead to significantly more rewarding outcomes. And in many cases, we probably don’t even want to know, because at that point the level of time and resources put behind the chosen path has reached a level, where the waste would make any team look careless in the spend of company resource. 

The alternative has been created years ago by the school of design thinking. In many ways design, as a consequence of digitalization, has infiltrated many processes in modern businesses. But for some reason, in most cases, not how we develop growth strategies. 

The approach is prototyping. The notion that you test your ideas for viability be doing early stage prototypes of your concept to find out how it actually works. And to do this before you finalize any details or start creating or producing. One famous quote on prototyping goes: “Learn how to fail fast”. This rightly presumes, that more often than not, we get it wrong. Despite our best efforts to be thorough there are variables or dynamics we haven’t considered in develop and leads us to solutions that aren’t viable. It of course also embraces as part of the process, as being inevitable, instead of being a force to avoid. 

Now, thanks to digitalization, we can embrace failing more than ever to find the path to success. Leveraging the power in our computers, we can prototype at speeds and at a volume that makes it much more accessible for everyone than ever before. We can harness the imagination across disciplines, experiences, languages, cultures, and even internally in organizations by the power of crowdsourcing. And now we can attribute data to validate the strength and attractiveness of all the outcomes. Imagine that, if you seek out the potential across more than for instance 50 different paths you could take, and you just need to find one success. Then suddenly you have man and machine working together instead of in compartmentalized silos. Which ironically means that the less you worry about being wrong, the more chances the machine has for getting it right. You have a significantly improved likelihood for success. But you also have a process, that’s significantly more fun and playful to be part of.

Factive is committed to making change in the world removing doubts as the barrier for doing. We want to challenge the current realities of the status quo with imagination powered by technology and data to create more meaningful and useful relationships between people and brands that lead to growth. We help business grow by creating dynamic and predictive datasets, that are more accurate, more cost effective and faster processed than any other approach. 

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