Referred to by many as the new currency, big data is a crucial aspect of any business in today’s digital world.
So much so, that a survey conducted by NewVantage Partners found that 62.5% of Fortune 1000 companies currently have at least one big data initiative underway. While only 5.4% of those surveyed have no upcoming big data plans.
Keeping track of the ebb and flow of trends in big data is like trying to keep track of teenage slang. Every time you think you have a handle on it, it changes.
But that’s not to say there aren’t some key trends worth keeping an eye on. Check out this guide to learn the top big data trends of 2019.
Big Data: What’s the Big Deal?
Those on the outskirts of the tech industry might not understand why companies need so much data in the first place. Simply put, data is information. Information fosters knowledge. And we all know knowledge is power.
Information that provides insight into a specific market can help companies offer better products and services to their customers. Not only that, data is the information needed to fuel innovation.
The most disruptive technologies are the ones with a deep, thorough understanding of what’s missing in the world. Without big data, no one would ever be able to identify these holes in the market. Thus, innovation would come to a standstill.
So, how has access to information changed since the Dewey Decimal System? Here are the main trends in data that are happening right now.
Open Source Applications
Open-source data platforms began to sweep the industry roughly five years ago, and they continue to be the way forward for big data analytics.
Apache Hadoop is at the forefront of this major data trend. Hadoop is a library of open-source software that helps businesses process and analyze huge amounts of data to create actionable insights.
Open-source data programs are available for viewing and modification by the public user. They are usually a public collaboration, which allows for massive amounts of data to be mined for analysis.
Essentially, open-source data analytics software is a data collection system. Once data is collected, it’s monitored on a more granular scale in the form of data clusters.
Then, machine learning and deep learning principles are applied to mine and analyze the data for the specific purposes of the collector. Each end-user (data collector) will have a different purpose for gathering the data.
The user sets parameters within the machine learning framework to get information they can then analyze and turn into an action plan.
Using sophisticated open-source data software libraries like Hadoop and NoSQL, businesses can speed up data processing and get to their end goals faster than ever before.
Instead of the traditional method of storing data on hard drives or solid-state drives (SSDs), in-memory technology allows for storage of large amounts of data in a computer’s RAM instead.
Many big tech players like IBM and Pivotal are now offering in-memory database storage options as an alternative to traditional storage methods.
A Forrester survey predicted that in-memory technology will grow almost 30% per year now that it’s been established as a viable option for storing large amounts of data securely.
Working in tandem with open-source platforms like Hadoop and in-memory data grids, companies can now analyze data as it happens using real-time, streaming analytics.
The faster information is received, processed and analyzed, the faster it can be acted upon. It’s been a big challenge for even the most senior data analytics experts to be able to pull this off. But the technology is getting there.
Open-source SQL databases and cutting-edge streaming analytics platforms like Hadoop an are drastically speeding up data processing time.
And with advancements in machine learning, automated decision-making is saving tons of time. Plus, data analytics teams are becoming more and more sophisticated with all this new, helpful technology at their fingertips.
Referred to in the industry as “Special K”, Kubernetes is a technology developed by Google to manage and store data using multi-cloud storage systems.
Instead of running software on operating systems that in turn run on servers, Kubernetes is essentially a hybrid cloud system that allows the software to run without having to rely on a specific OS.
Hadoop and other leading analytics companies are now ensuring that their software is written to be compatible to run on Kubernetes instead of the traditional operating systems.
Along with amazing advancements in big data comes big risks. Privacy and security are of the utmost importance to both consumers and corporations. With the European Union’s passing of GDPR, the US is expected to soon follow suit.
The Harris Poll recently conducted a survey that found that almost 60 million Americans were victims of identity theft last year. A 300% increase from 2017. This is largely to do with the lack of data governance in the United States.
Right now, companies operating in the US have to navigate the rules of over 80 different data mandates that vastly differ from state to state.
Considering the sensitivity and power big data holds, it will be a huge surprise if a federal data protection mandate isn’t established in the US very soon.
A Shift in Skills
Even with all the advancements in machine learning and AI, humans still play an incredibly important part of the world of big data. Machine learning is great, but it needs to have human-specified parameters to work within.
Recruiting people with the right skills who can adapt to ever-changing trends is crucial for success. Data scientists are in high demand, and salaries are expected to increase as the job and its related technology becomes more complex.
So if you’re worried about robots rising up and taking your job, learn data science to make yourself indispensable.
Other Big Data Trends
So far, we’ve barely scratched the surface of the trends happening in big data. Others worth mentioning are developments in IoT solutions, edge computing, and predictive analytics.
IoT (the Internet of things) is, simply put, the transfer of internet connectivity to every day objects like your watch, your microwave, your car. It’s no surprise that “smart” objects like the Apple watch are a huge player in big data.
What matters most to companies nowadays is not only that they are able to collect data, but that they are able to process it, analyze it and act upon it as fast or faster than the competition.
A new innovation in data analytics has probably been announced in the time it took you to read this article. Keep an eye on this space to stay updated on current and upcoming big data trends.