Utilizing Data Analytics has become an inseparable part of everyday business for countless companies, enabling new opportunities to attract customers, retain business, and build partnerships in everything from retail to high tech, engineering, and manufacturing. As Big Data begins to mature as a technology for the masses, firms are beginning to integrate the power of analytics with the ingenuity of machine learning, complex algorithms, and other unique, automated services. This marriage of technologies will bring new opportunities to businesses, as well as new challenges in data management.
The power of algorithms in data analytics
Machine learning is integrating into our lives in all sorts of subtle ways. For example, Facebook knows who to tag in your photos each time they’re uploaded, all without human interaction. This is machine learning at its finest, as a facial recognition algorithm learns from the user about who’s who on Facebook, and helps simplify the process for uploading photos of friends and family.
This same predictive technology can be harnessed to bring unique, automated insights for those taking on Big Data Analytics. When dealing with terabytes of information, it’s impossible for any data scientist to rely on their own manual functions to get the most out of that data. This is where machine learning and other predictive capabilities can enhance our existing Big Data capabilities.
There are countless firms researching to build a scalable, machine learning solution for Big Data. Google is a major pioneer in this field with its Prediction API. Google’s software is designed to find patterns in large data sets, helping with everything from improved searching to predicting the World Cup winner. In essence, predictive analytics is like telling the future with your data. It’s in no way a guarantee of what will happen, but can be immensely helpful to determine opportunities and threats for your business.
The data management challenges of machine learning
A barrier in machine learning, even for analytics needs, is the nominal cost of the hardware, equipment, data storage, and coding necessary for even the most simplistic of machine-learning solutions. Take, for example, the IBM Watson computing system. This computer was famously known for playing the game show Jeopardy against human contestants as a pinnacle of machine learning capabilities. Powered off millions of dollars of hardware, Watson can search for patterns and opportunities in just about any Big Data question, from healthcare to fantasy football.
This cost is currently a barrier for firms who want dedicated machine learning capabilities for their analytics needs. However, the cost to produce Watson-like hardware is dropping gradually. There are also free, cloud-based services that give small businesses access to Watson-like machine learning technology and analytics systems. These are some of the ways that managing the data and equipment behind machine learning is becoming more viable, and something accessible to more than just major enterprises.
All of this is coming to a head in the months ahead. As computing capabilities become cheaper, machine learning and other predictive analytics will become a staple of the data analysis. Just as Big Data is used to find trends that would otherwise go unnoticed, machine learning will speed up that process and enable clairvoyance to these trends faster than ever before. As small and medium enterprises gain access to these technologies, there’s no telling how much it will improve just about every part of daily business.