Technology has been the driving force behind almost every product and process transformation of our lifetimes. These innovations have changed the way we live, work and experience the world around us. It is difficult to imagine life without constant connectivity and all the efficiencies offered by modern software. Reflecting on how far we have come raises the question, where are we headed next? Some of the most interesting possible answers to that question lie at the intersection of humans and machines.
Today, it is common to read about big data, machine learning and AI. Anecdotes such as “90% of the world’s data was generated in just the last two years” abound. All this commentary is pointing to two major themes that are driving innovation: the amount of data captured and transferred and our ability to process it.
If we look deeper, we see the opportunities data and computational power provide are extremely challenging to unlock. From a dearth of tech talent, to poorly stored and managed data, to a lack of expertise, to outright charlatanism, it is easy to get lost in the hype. Everyone has data, processing power continues to get cheaper and new tools are released every day, but customers are still frustrated. The reason for this frustration is that more records, techniques or platforms aren’t what people need. Finding actionable insights within the data is what truly matters and therefore is key to success. As Clayton Christensen, author of “The Innovator’s Dilemma,” said, “People don’t want to buy a quarter-inch drill. They want a quarter-inch hole.”
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Without a scalable framework for how you extract insights from data, the process can be slow and laborious. Mining, cleaning, transforming and modeling require effort from engineering, product and data science teams. Heavy resource allocation coupled with the notoriously high failure rate associated with machine-learning projects create an environment in which it can be difficult to innovate.