February 5, 2016

Big Data Trends in 2016: From Mobile to the IoT

Big Data Trends in 2016: From Mobile to the IoT

After hearing all about “big data,” and how it would transform the way we both live and do business, it finally gained widespread adoption over the last two years. With small and mid-sized companies finally utilizing the value date brought from social media, surveys, customer feedback, forms, and the cloud, there are a number of trends we can expect to see throughout 2016.


The Mobile Security Challenge

Mobile devices are quickly becoming the only device users need. According to a recent Wired article, if paired with a monitor and keyboard, within a couple of years “there’s no real reason why, for the vast majority of us, a smartphone couldn’t handle all our daily computing needs.”[1] With this in mind, mobile devices will begin receiving better security when accessing data repositories throughout 2016. A recent survey found that two-thirds of organizations have a mobility program that includes giving out a smartphone, and as a result, 74% of those surveyed said they experienced mobile security issues.[2] If accessing data repositories, these devices will need to be secure.


As more and more workers bring their own mobile devices into the workplace, the lack of security on those devices poses an increasing threat for business continuity leaders to consider. From Here is an infographic about the security risks associated with mobile devices. Resource:

More Data Scientists Will Be Needed

Big data is a booming global industry, and as more information is collected, it becomes harder to wrangle it. Those who manage the sprawl of data and normalize it, ensuring that companies can do the most with everything collected are called Data Scientists. However, as sprawl increases and more companies need help to analyze what they’ve collected, data scientists are harder to come by.

Within the U.S., there is an estimated shortage of 140,000 to 190,000 people with analytical expertise, and 1.5 million managers and analysts who can’t adequately understand or make decisions based on big data analysis.[3] With this in mind, many are creating partnerships around the world, looking for data scientists who not only understand a business’s needs but will provide them with a relevant analysis of their data. In 2016, as data becomes too large for even the biggest of companies, look for more firms moving their in-house data analysis to technology partners specializing in big data analysis.

The Internet of Things Piles On

It’s possible that “the Internet of Things” has replaced big data as everyone’s favorite “next big thing” in the technology world. Some are predicting that the internet of things will take off in 2016, launching new business opportunities and start-ups. With all devices connected to the internet, they are all creating, sending, and sometimes storing data. To this effect, the internet of things and big data can be described as two sides of the same coin. Gaining value from IoT data will require companies to set up a proper analytics platform and infrastructure so they can analyze all the IoT data that comes in. [4]

In 2016, this infrastructure will need to be created. Keeping in mind the shortage of data scientists and the workload of internal data teams, it is important for companies to consider how they plan on setting up this infrastructure, as well as how they plan to determine the value of the data being collected.

This year will prove to be the year where everyone gets what they wish for, but as the saying goes, be careful about that. Data is expanding at an incredible rate, and not everyone has the security or the capacity to keep up. If this is the case for your company, you will need to consider gaining a partner in the fight to stay on top of data sprawl. Data becoming too big for some, but not others, could prove to be the difference between those who ultimately succeed and those who do not.

Read also:

The Basics of Big Data Storage

Big Data and the Challenge of Unstructured Data

Pros and Cons of Big Data

IoT Security: Running a Secondary Network