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The Data Race


Companies that are willing to implement machine learning in their business operations are finding, with not much surprise, that acquiring the algorithms that use to make machines intelligent about a problem may just be the easy part.

In fact, countless companies are developing and selling advanced algorithms capable to churn out highly valuable insights, and with the Bottos model marketplace it may become even easier to get the hands upon sophisticated AI models, to the extent that they’re mostly becoming commoditized.

What’s not becoming commoditized, though, is data, and is indeed emerging as the key differentiator in the machine learning race, mainly because good data is uncommon.


Data is becoming a key advantage because many companies don’t have the data they need. They, as well as researchers, usually find that good quality data is out of reach, most of the time isolated in data silos or in the hands of a few data providers that have sent to the moon the prices.

This situation may slow down the right adoption of AI in all industries together with poor data strategies drawn by the same companies. Data marketplaces like Bottos are needed to make the situation much better and to speed up the right implementation of AI models in all the industries.


Machine learning systems must be taught about any topic in order to get really smart, and they need a lot more data than humans do. So, while there is an evident race as companies bring on machine learning coders and begin AI initiatives, there is also a behind-the-scenes race for new and high-quality data.


Data creation is more complex than simply aggregating point-of-sale, customer or location information and putting it into a database.

Differentiated data is gradually becoming one of the most important thing to run a successful AI algorithm. It would be almost pointless and unfruitful trying to uncover anything new working with the same data that all competitors have. But, by looking internally and identify what the organization uniquely knows and understands, and create or acquire data sets using the Bottos data marketplace can give a strong advantage and enable the real value creation of the combination of data+AI.

Machine learning applications require high quantities of data, but this doesn’t mean the model has to consider a too wide range of features. Focus is most of the times needed since meaningful data is better than comprehensive data. And this can very well be accomplished on the Bottos platform, where data will be cleaned and tagged with clarity.


Companies and researchers that best use machine learning should start with a unique insight about what matters most to them for making important decisions. This must tell them about what data to gather, as well as what technologies to employ, knowing that Bottos may satisfy every step they make.