Top 7 Big Data Analytics Trends For 2019

by October 29, 2018

The amount of data generated today from all industry domains, also known as big data is huge, encompassing data gathering, data analysis, and data implementation process. Over the years, big data analytics trends are changing, from a departmental approach to business-driven data approach, embracing agile technologies and an increased focus on advanced analytics. Business enterprises need to implement the right data-driven big data analytics trends to stay ahead in the competition.

Previously, big data was primarily deployed by big businesses, who could afford the technology and channels used to collect and analyze the information. Today the scope of big data is changed leading to business enterprises large and small rely on big data for intelligent business insights. This has led to big data evolving at an unbelievably fast pace. The best example of the growth is big data in the cloud which has led to even small businesses taking advantage of the latest technology trends.

The never-ending stream of information is valuable to the business, but it can also be a challenge to draw actionable insights from a large data pool of data which may be unstructured. Even with these roadblocks, there’s no denying the fact that big data offers business tremendous opportunities for growth. Here are the “Top 7 Big Data Analytics Trends” that will be the talk of the technology world in 2019 and beyond.


1. Fast Growing IoT Networks

Internet of Things (IoT) will be the trend, which will generate more than $300 billion annually by 2020. According to the latest industry trends and research reports, the global IoT market will grow at a CAGR of 28.5%. Business houses will rely on more data points to collect information for more detailed business insights.


2. Predictive Analytics

Predictive Analytics offers customized insights that lead organizations to generate new customer responses or purchases and promote cross-sell opportunities. Predictive Analytics helps technology to integrate into diverse domains like finance, healthcare, automotive, aerospace, retailing, hospitality, pharmaceuticals, and manufacturing industries.


3. Dark Data

Dark data in technology is the digital information that is currently not in use for business analysis. This data is acquired through various computer network operations which are not used in a manner to derive insights or for decision-making. As analytics and data become daily aspects of organizations, there is an increased need to understand that any data left unexplored is an opportunity lost and may lead to a potential security risk.


4. CDOs in Demand

The profile of the Chief Data Officer (CDO) has evolved and human resource personals are scouting for professionals who can fill this trendy job role. Though in demand, CDO is still a relatively new concept to many companies. Organizations have realized that they need to hire a CDO, so if you are a data leader managing enterprise-wide data cleaning, analysis, visualization and studying intelligent insights, CDO may be the work profile for you.


5. Quantum Computing

Tech giants like IBM, Microsoft, Google and Intel, race against each other to work rigorously in a bid to build the first quantum computer. Quantum Computing enables seamless data encryption, weather prediction, solving complex medical problems, real conversations and better financial modeling to make organizations develop quantum computing components, algorithms, applications and software tools on qubit cloud services.


6. Open Source

2019 will witness more free data and software tools to become available on the cloud. Small organizations and start-ups alike will benefit the most of this data trend in 2019. Open source analytical languages like R, a GNU project associated with statistical computing and graphics has seen a huge adoption credit to the open source wave.


7. Edge Computing

Edge Computing has been into the technological space streaming network performance for quite a while now. All credit to edge computing that data analytics is partly reliant on the network bandwidth to save data locally close to the data source. Edge Computing makes data to be handled and stored away from the silo setup closer to end users with processing taking place either in the device itself or in the fog layer or in the edge data center.