Analytics Magazine Analytics Magazine, November/December 2014 - Page 10

Exe cu tive EDGE telephonic conversations, e-mail communications, videos and the like. This creates a maze of data that cannot be easily handled with traditional models, resulting in a waste of time and human effort. So what can tame big data and put it to good use? Machine Learning Applying machine learning algorithms on big data is the art of putting all fragmented and often disconnected data sources together to generate actionable insights for the enterprise. To gain that 360-degree view of the customer, organizations need to be able to leverage internal and external sources of information to assess customer sentiment. As more and more organizations are stepping out of the traditional boundaries of the enterprise to understand the impact of the environment on their business, the number of data sources keeps multiplying. Social media channels, websites, automatic censors at the workplace and robotics, for instance, are producing a plethora of structured, unstructured and semi-structured data. Machine learning weaves together the two budding trends of 2014 – realtime data collection and automation of business processes. Bringing in the computational power, machine learning runs on the machine scale. The number 10 | a n a ly t i c s - m a g a z i n e . o r g of variables and factors that are taken into consideration by this methodology is unlimited. Machine learning brings in the capability to cover data from varied channels, such as social media, websites, automatic censors at the workplace and robotics. The job of data scientists here becomes to oversee what type of variables enter the models, adjust model parameters to get better fits and finally interpret the content of models for decision-makers. How and When to Introduce Machine Learning Machine learning is ideally suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. The more data fed to an ML system, the more it can learn, resulting in higher quality insights. Keep in mind that big data can only unfold incremental insights. The Pareto 80-20 rule applies here as well, as 80 percent of the details one would need for business come from the internal and transactional data. Using big data is only viable for organizations that have matured in the data utilization curve. Once the business intelligence bit and predictive analytics have been achieved, only then does it makes sense to move toward big data. w w w. i n f o r m s . o r g