My predictions next year are centered around DATA.
Computer science started with systems of records – databases, analysis and software that stores data. Then the rise of Internet encouraged the creation of system of engagement – websites, mobile applications and software that enables a large number of people to access data. Today, I would like to call it system of analysis which goes beyond exposing data at large scale to collecting information about services and offering insights about that data. This turns the focus from data to metadata. Successful companies look beyond transactions themselves to the context of that transactions and how can optimizations be better applied to maximize business results.
As a result, many tools such as machine learning (ML), deep learning and natural language processing (NLP) start to mature. I predict that these toolchains will become even more powerful and much easier to use in the coming year. Companies will double down on the investment on AI tools. It used to be that it requires a PhD and very deep math background. Now it’s much more accessible to domain experts who understand a particular market or are familiar with a particular set of training data to leverage these ML tools and derive value.
With these two major trends coming next year, companies are presented with both opportunities and threats depending on how much data driven they are. Can they collect and capture data that is most impactful to their business? Can they incorporate data into their DNA? Can they do deep analysis to ensure that all their decisions on product and services are based on their data? Technology companies have an advantage since massive data has been the core of their business. Other traditional industries and verticals need to catch up at a faster pace to disrupt their own business or to be disrupted.
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