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5 ways Big Data Will Shape The Enterprise In 2016

5 ways Big Data Will Shape The Enterprise In 2016

One of the most common misconceptions about technology is that people will always be on the losing end of new advancements. It’s an easy assumption to make considering the number of past human occupations usurped by machine automation. But the replacement phenomenon isn’t linear. There are boundless examples of new technologies that have failed to approximate the value delivered by the humans they replaced.

It’s common to apply automation with too broad a brush, asking machines to do things that only humans do well. This includes tasks like answering telephones and reading facial expressions. Meanwhile, in other disciplines, we require humans to complete data-driven tasks that machines do quite well, such as deciding how best to arrange store inventory.

Thanks to big data analytics, we can correct both missteps. Rather than replacing humans in a one-for-one exchange, big data analytics can be added to human-led processes, creating a collaborative hybrid of human and machine. By adding more data at the right moment in a process, big data analytics narrows the situations in which humans must guess the right decisions to make.

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By making it possible to apply automation far more judiciously, big data allows machines and people to collaborate on decisions about processes and policy, leveraging the strengths of each.

For global business, this is a huge advantage because technology can be added and replaced as needed to deliver on whatever intelligence automation the organization needs with near-perfect reliability and continuity.

While large-scale big data analytics installations are already in place around the globe, they have been mostly out of public sight, leveraged for confidential, high-value purposes. In 2016 this will change, bringing the analysis of big data front and center in a wide range of business applications.

Here are five critical business benefits we predict big data analytics will begin to deliver in 2016. 

1. Optimization of labor We all have hunches and “gut feelings” about what to do, what’s right, and what’s wrong. But intuition without hard data to back it up seldom leads to an ideal choice. Optimization happens when data drives a decision and is supplemented with human intuition. 

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For example, drivers of commercial vehicles relied primarily on intuition and prior experience to guide their decisions about which route to take. When given telematics and route optimization data, people can vastly improve their driving efficiency and use their intuition to problem solve when necessary. These types of process hybrids allow both machines and humans to be their best “selves” and bring optimal value to business processes and customer experiences. 

By supplementing human-led processes with more data-driven decision support, big data analytics enables a closed feedback loop that can optimize human processes.

People have strong preferences about channel. Research shows, for example, that millennials will avoid processes that aren’t offered via mobile or social media, their “native” channels.

Channel diversification is great for user choice, but it creates a challenge for businesses, which haven’t had the technology support to make every channel equal in experience. For example, call center agents use decision support tools to help them resolve issues according to policy, but if the system data and policies aren’t identical to those in other channels, the customer experience splinters. This leads to user frustration and confusion because different channels may yield different results.

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Big data analytics can help organizations become channel agnostic. When you’re able to analyze big data quickly and accurately, every channel can draw on the same data sources and policies, giving assurance that all channels work equally well. Moreover, big data analytics can support frictionless cross-channel processes, meaning employees and customers can always choose the channel that is most convenient at any given time.;


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