We’re at the cusp of intelligent digital services. In the next 3-5 years there will be an explosion in the adoption of model generation for predictive algorithms, i.e. machine learning. They will be as ubiquitous as smart phones, but much more valuable (not only in a financial sense). By now you’ve heard of big data, machine learning, predictive analytics, artificial intelligence, data science, etc., but I’m going to break it down into two terms: big data and machine learning.
The cart before the horse:
We learn by experience. The more we experience, the more we adapt, and the more we’re able to modify our behavior to achieve our goals within the confines of our environment. Experience is data, and the more you have, the more it becomes big data.
I’d prefer to not die in an airplane crash. I’d hope that the airplane and air traffic model has been fine tuned to reduce the probability of an unfortunate event. In the development of this aviation model we have our wishes (inputs), and we have our desired results (outputs). What lives in between is a black box (model) that contains all the variables that affect our result – the importance of each is weighted and analyzed reduce the probability of an accident. The generation of this model is the core of what is known as machine learning.
Machine learning can exist without big data, but the more data we have (experiences) the more we can train an effective model. In essence, we’re taking what we’ve experienced and we’re breaking it down into a systematic process to reproduce results. This way, within the context of our problem, we can feed our model any variation of inputs and predict a result. Big data, data analytics, and business intelligence as they exist today revolve more around people organizing existing data in a way that provides value. Machine learning uses this data to automate the informative process, telling you more about what you’d like to know, what you can expect, and it provides deeper insights into how your data (experiences) interacts with each other.
“We used to trust domain specialists to make decisions from data dashboards and reports. Now we trust the data to make decisions for us.” – not-so-distant future businessman
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