Artificial intelligence (AI) is all around us. Unlike the Terminator robots , AI exists in subtle ways, embedded in our daily activities thanks to the rise of big data and machine learning.
When Facebook tries to connect users with new friends or businesses, Netflix suggests a new TV series to watch, or Amazon recommends a book, these are all examples of AI presented to people via machine learning – a statistical method that finds patterns and makes predictions based on vast volumes of data.
After years of hype, most organisations now have a solid understanding of the potential of big data. In fact, the majority of them are actively pursuing means to capitalise on all the information they capture.
A recent Gartner survey found that more than 75% of companies are currently investing or planning to invest in big data initiatives over the next two years. This heightened interest has led analysts to speculate that big data project investments will reach $242 billion in short order.
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When it comes to big data, the real opportunity for enterprises is in advanced data analytics, specifically machine learning. With this methodology, big data can be mined to automatically uncover business insights as well as generate predictive models.
The ultimate scenario is one where machine learning can accurately guide forward-looking business decisions and reveal patterns never before seen. It is this promise of delivering accurate, actionable, predictive information that will drive machine learning to play a greater role in big data analytics, and make 2016 the start of the age of enlightenment for high-performance machine learning.
Mining big data
Machine learning is a fundamental tool in creating a world that can sense and react to dynamic, distributed phenomena. The number of variables and factors that can be taken into consideration by this methodology is unlimited.
It weaves together real-time data collection with the automation of business processes, and is ideally suited to deal with complex, disparate data sources and the high number of variables involved in data sets that are large, diverse and fast changing. In other words, machine learning is primed to handle big data.
Wherein traditional analytics tools are limited by data volume and the need for human interaction to specify program execution, machine learning offers the scale, speed and accuracy needed to truly uncover the full value of big data.
High-performance machine learning can analyse all of a big data set rather than extrapolate against a sample of it. This scalability not only allows predictive solutions to be more accurate, it also illustrates the importance of software speed to interpret billions of data points in real-time and to analyse live streaming data.
Also, unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into the system, the more it can learn and apply the results to higher quality insights.
From data scientists to business users
The impact of machine learning on big data is significant in two primary ways. The first is in allowing data scientists to be more productive by being able to process and analyse the volume, velocity and variety of both structured and unstructured data.
In doing so, data scientists can fine tune the parameters of their predictive models to more accurately interpret content and deliver actionable insights to decision makers.
The second is in uncovering hidden patterns that even the best data scientists may have overlooked. With this kind of power put into the mining of big data, enterprises now have a real opportunity to tap into the promise of big data, and quickly see ROI on their big data project investments.
While data scientists have enjoyed the benefits of machine learning for some time, the future for big data and machine learning lies in getting this technology into the hands of business users.
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As more providers innovate to bring this technology to the masses, it will broaden the accessibility of big data beyond the enterprise, and dramatically accelerate time-to-insight for organisations of all sizes.
There is tremendous opportunity in the application of machine learning to discover valuable insights that can lead to better and faster business decisions.
Today, the practical applications of machine learning on big data are abundant. Financial institutions can more quickly detect fraud. Utilities can systematically predict failures and perform prescriptive maintenance. Retailers can mitigate customer turnover, and anticipate consumer purchases with higher accuracy.
As machine learning continues to make significant strides in the coming years, enterprises will be compelled to take a closer look at how this technology can impact their bottom lines.
Sourced from Gary Oliver, CEO, Blazent