With the holidays behind us and the new year in full swing, it’s the perfect time to take a look at what trends are going to be important throughout 2017 and how they may impact organizations the world over. Big data had a big year in 2016 – it’s helping businesses keep up with product demand, driving research into human genome mapping in health care and quickly becoming a household term.

The promise of big data is sure to delight organizations that use the insights to properly support their business strategies – and the next year is no doubt going to be another important one for big data. Let’s take a deeper dive into what big data may have in store in 2017:

1. Machine Learning in Biology

Machine learning has been a hot topic recently. By developing new artificial intelligence tools and allowing them to learn and grow on their own, innovators have made strides this year in mainstreaming these tools, according to InfoWorld contributor Serdar Yegulalp. Better machine learning toolkits, as-a-service APIs and access to improved hardware are all reasons that machine learning jumped into the limelight in 2016 and will probably continue to shine throughout the next year.

TechRepublic contributor Matt Asay wrote recently that machine learning is redefining biotechnology as well. The confluence of several different disciplines is creating a new environment for IT trends and biology alike, as the big data gathered by machines and analyzed into software and hardware is put to use in science fields.

“[T]e latest advances in software, big data, machine learning, biotech and chemistry may be combining to quite possibly start a new industrial revolution,” Asay wrote.

Essentially, companies like the Silicon Valley-based startup Lygos are using machine learning principles to create new microbes – essentially, turning them into highly efficient factories by slightly altering genetic code. While this seems like a project straight from the future, that’s what 2017 has in store for machine learning.

2. Machines Learn Security, Too

Data security representation

Machine learning can help organizations stay ahead of a potential security breach.

CIO contributor Santosh Varughese noted that big data, despite being so useful, doesn’t mean much unless you put in the effort to draw out the key insights and apply them to your business strategy. Specifically, one important way that big data is being used to improve organizations’ computing infrastructure is by utilizing the insights to make their environments and networks more secure.

However, there is a catch: When big data is employed after the security breach occurs, it can’t do anything to prevent these unsettling events. Instead, companies need to take the insights gleaned from their collected information and apply them to their security infrastructure before a data breach happens. With analytics, even though cybersecurity is a constantly changing field with many moving parts, you may be able to provide your employees with more training that will help them to better know how to deal with a situation.

For Varughese, the answer is, once again, machine learning.

“Machine learning technology not only makes sense of big data, it can analyze it and extract insight from it far more quickly than a human or even a team of humans ever could,” he wrote. “Because of its predictive capabilities, it can be proactive instead of reactive. In real time, machine learning technology can flag a hacker who is using stolen credentials and stop them from getting into your system.”

3. Lots More Unstructured Data on the Horizon

Machine learning is having an impact on what kinds of data are being collected, as well. According to TechRepublic contributor Mary Shacklett, the Internet of Things is more than likely the future for most organizations, and that “everyone is thinking about it.” This explosion in IoT-related items is contributing to a massive amount of unstructured data being collected by companies looking to cash in on the benefits.

The Internet of Things is more than likely the future for most organizations.

“Organizations’ big data aggregation goals will expand to visions where standard digital data originally entered by humans, and data issued from machines will be aggregated into composite visualizations that will transform the way work is done,” Shacklett wrote. “Big data and analytics vendors and consultants will be called upon to assist companies in defining and achieving these new data aggregation goals.”

In other words, having unstructured data is going to necessitate an investment in the right kind of big data storage and analytics tools. After all, what’s the use of having all the disparate data if you don’t have a way to apply insights to your company?

Get the Insights You Need

As Varughese pointed out, collecting big data and taking advantage of machine learning doesn’t mean much if the data gathered doesn’t form a cohesive picture. By drilling down into the data and creating key insights, your business can use it to improve security, better fulfill customers’ demands and in general increase your bottom line.

When you partner with Aunalytics, you’re putting your data in expert hands. Our end-to-end data management and analytics solution provides all the tools your organization needs to remove the pain points along the analytics journey, allowing for greater speed to insights. In 2017, make the resolution to get on top of your big data – get in touch with the Aunalytics experts today.