Executive Edge: Machine learning unleashes big data potential
The art of putting fragmented, often disconnected data sources together to generate actionable insights for the enterprise.
By Durjoy Patranabish and Sukhda Dhal
Big data has no doubt created a big business buzz, and organizations and thought leaders are constantly talking about big data, yet many critics note that the widespread application of big data has not matched all the hype.
Yes, big data helps unveil millions of facts about consumer behavior and trends. Leveraging emerging big data sources and types to gain a much more complete understanding of customer behavior – what makes them tick, why they buy, how they prefer to shop, why they switch, what they’ll buy next, what factors lead them to recommend a company to others – is strategic for virtually every company. But have data science organizations built the capabilities to truly harness big data?
It’s clear that traditional predictive analytical models will be unable to work on big data, as these modeling tools need human intelligence to work across the data sets. They definitely make the analysis robust and quick, but only for the structured data sets. Big data, however, is mostly generated via unstructured formats such as images, comments on portals, 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?
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 – real-time data collection and automation of business processes. Bringing in the computational power, machine learning runs on the machine scale. The number 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.
Organizations need to prepare themselves with adequate knowledge resources and skilled data scientists who are adept at not only building statistical models, but also at using cutting-edge programming for applying machine learning.
Business experts need to first ask themselves these questions: What use case will they apply big data on? What insights do we need from the big data?
Premature application of big data techniques, either without requisite expertise or without the knowledge of the business case to be solved, will result in the waste of human and capital resources.
Durjoy Patranabish is senior vice president of Big Data Analytics at Blueocean Market Intelligence. He has more than 17 years of experience in IT services, KPO and analytics services, and BPO and back-office services for global brands and regional leaders. Sukhda Dhal is a consultant, Big Data Analytics, at Blueocean Market Intelligence. She has more than four years of experience in business consulting, analytics and technology services at blue chip firms and startups.
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