Improving Big Data Analytics with Machine Learning
The amount of data generated by businesses every single day is colossal – and it’s growing at an exponential rate. Many organisations are now reporting that they have a mass of unstructured data and no idea how to analyze it or use it in any way – from emails to social media posts, customer letters to voicemail messages, there’s now so much information to digest and analyze, businesses are finding it almost impossible to keep up. What’s the point in collecting and storing so much data if you can’t find a way to interpret it effectively?
But advances in analytics tools and software have endowed businesses with an exciting way to leverage big data: machine learning. Machine learning is described as ‘a type of artificial intelligence which provides computers with the ability to learn without being explicitly programmed’. It also focuses on the ‘development of computer programs that can teach themselves to grow and change when exposed to new data’.
Here’s a real-world example of big data and machine learning at work. When Google sends you an alert that you should set off on a journey in order for you to be on time for a meeting, that’s the result of big data and machine learning. When an ecommerce website sends you a list of product recommendations, that’s all down to machine learning and the data collected from other customers in your demographic. When Spotify creates a playlist for you, it’s learning from masses of data that has been collected.
Developments in the world of machine learning have given businesses a way not only to improve how they analyze their big data – it also gives them a way to predict what will happen next. Predictive analytics combines with machine learning gives businesses new ways to break down their unstructured data, with an enormous number of benefits.
Machine learning allows organisations to dig deeper into the mindset of their customers. Where just a few years ago, this might involve assessing their browsing habits or delving into their purchase history and cross-referencing it with their demographics, now businesses can look at social comments, customer support messages, contracts and feedback in order to provide a more comprehensive insight into the customer’s mindset. Machine learning and predictive analytics then combine to predict how they’ll respond, so that businesses can provide a reactive experience to each and every one of their customers.
To learn more about improving your big data analytics with machine learning, register free for Big Data LDN at Olympia London on 3-4 November 2016. The event will host leading, global data and analytics experts, ready to arm you with the tools to deliver your most effective data-driven strategy.