Big Data Demand Signals
How Machine Learning Identifies Demand Signals Across Multiple Disparate Data Demand Sources
By Ravneet Bhandari Chief Executive Officer, LodgIQ | October 23, 2016
Big data, more than a buzzword, has by now become a conundrum that we, consumers and providers of information, try to crack and make sense of it. Essentially, we know that data is becoming larger with wider access to complex algorithms and connections. The onion metaphor - the peeling back of many layers - can be used to reflect the multifaceted aspects of machine learning technology. These swaths of data or rather layered strings of data sets turn these complex entities into a more accurate view of customer demand for the hotelier.
What is Machine Learning?
As a sub-field of computer science and artificial intelligence dealing with building systems that autonomously learn from data, machine learning allows companies to sift through remarkable amounts of information and make empowering recommendations.
Netflix utilizes it to make viewing suggestions, and Facebook uses it to populate your feed with trending video and content selections. The technology introduces mathematical modelling into the process of identifying patterns and making decisions. Then, it adapts and "learns" from all the data signals collected over time and optimizes its decision-making based on all previously inputted data.
The learning ability is what makes this technology powerful by becoming a faster and more efficient version of itself. In all, there are four types of machine learning styles, supervised, unsupervised, semi-supervised and reinforcement. The first three are all slightly different permutations of the same idea, the only major difference relates to whether there are specific desired outputs programmed in. Reinforcement learning maximizes performance though determining an ideal behavior, such as when a computer gets better at playing an opponent in an online chess match.
What's in it For the Hotelier?
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