What are the New Trends in Big Data Analytics?

The Big Data wave is not slowing down, it appears only to be accelerating. While Big Data emerged on the fringe in research, defense, and tech sectors, it’s now firmly established in the mainstream, driving decision making in technology, business, and public sectors.

Evolving with accelerated use of the Internet of Things (IoT), and our increased ability to capture everything digitally, from video-logging of peoples lives, satellite and geo-mapping, to automatically capturing environmental changes, and everything in between, Big Data itself is no longer a trend, but an established part of data analytics and business as a whole. Big Data has opened up a world of data analytics previously unheard of and continues to do so as a revenue-generating technology, applications develop as exponentially as the data itself, and we need to keep abreast of development.

Let’s take a look at some of the current key trends for Big Data analytics.

Increased Applications in Public and Private Sectors

Big Data initially has taken root in technology where the industry uses data as a form of fuel, as with all technology, it’s been firmly established in research and defense, spilling over to big business, where there are now estimated 40% of companies using Big Data. The current trending expansion areas are in public services such as health, education, and utilities. These industries are slower on the uptake, sadly since typically having smaller budgets.

With Big Data technology becoming cheaper, and clear benefits, for example, cost savings, now established, public usage is expanding. We will see user benefits such as improved medical analysis and diagnostic tools, optimized energy provisions by suppliers, effective and predictive energy resource management, specialized education techniques that cater for knowledge deficiencies, and increased higher education options in the data sector itself.

Consumer engagement in Big Data will continue to grow, with items such as Chatbots (automated user assistance) now becoming a part of daily life, and Big Data’s place in search, as well as apps like Google Assistant and Siri, continues to drive consumer choices and availability of information.

Automatic Data Capture

With technology for storage becoming cheaper, and the advent of capacities to store unstructured data, public and private sectors are beginning to capture more and more data.

Automated selective tracking and dumping are also possible to only store what a company needs.

Audio and Image Capture

The potential for audio capture in Big Data is only just beginning, and there may be a widespread conversion of audio files into text for data in the way text reader technology has converted most of the world’s books from hard copy to digital form.

Image recognition improvements and advanced pattern recognition mean we can draw conclusions from images more easily. Image resources are a huge source for new big data mining projects. This can also have applications in analyzing large dark data stores such as video surveillance footage, video media, or satellite images.

Machine learning capabilities will be used in both these presently unstructured Big Data stores to help improve error removal from the transcriptions and conversion processes.

Non-Relational Databases

Non-relational data stores are predicted to be the fastest growing technology in Big Data processing and analytics. Non-relational databases allow storage of non-structured data and increase the ability to make use of dark data.

Increased capacity for storing and analyzing non-structured data opens up another avenue of data-mining opportunities for Big Data.

Parallel and Distributed Systems Architecture

The development of parallel and distributed systems architecture continues to grow. Hadoop’s open source framework has made this method more accessible to many companies, at a lower cost entry point. Massively Parallel Processing (MPP) is also becoming cheaper.

Data Repositories and Cloud Storage

Data storage repositories, data warehouses, and cloud storage are growing with the demands of Big Data.

Hadoop itself, one of the leading software solutions in Big Data is essentially a cloud provider, that is, data can be accessed from a Hadoop cluster via the net, just like your cloud storage providers servers are providing cloud storage.

While Hadoop is not actually marketed in this means, since it can be stand-alone or cloud, there is a chance some cloud technology you are using works on Hadoop, for example, Amazon’s EMR and Netflix both use Hadoop as a cloud storage.

More established cloud frameworks include Linux Ubuntu, OpenStack, and Eucalyptus.

Security Assurance and Security Applications

Big Data is both assisting security, and raising security questions. In terms of assisting security, companies are using Big Data to profile, track security breaches, and analyze how to make systems better protected against fraud.

As a security threat, with larger data sets, and more stored information about users, a data breach can have severe impacts. At present, security means against cyber threats aren’t nearly enough to protect users. There is a call for more government protection, of which the European GDPR (general data protection regulations) is a start to the trend.

Virtualization Queries

Modern databases are starting to reduce processing time and errors by querying data where it lives, often data across multiple data systems. The technology behind this includes In-Memory Analytics and Stream Analytics.

In-Memory Analytics

While traditional data analytics takes place with data stored on a hard drive, In-Memory Analytics takes place in the computers random access memory (RAM). This makes it a much quicker and more efficient method of data analysis.

Stream Analytics

Stream analytics take place with live data, and again analysis of the data takes place by drawing or analyzing variables from a live stream, not by removing the data to a database, only required variables are stored. Apache Spark and Cassandra are popular streaming database applications.

Improved Data Prediction

Larger data sets provide more accuracy in predictions. Accuracy in predictions is particularly beneficial in industries like medical and research. Predictions drive business success in investment strategy or product choices. They also benefit consumers as businesses learn to serve needs better.

Machine Learning Capabilities

Larger data sets drive machine learning capabilities. With more opportunities to draw parallels and develop conclusions, Big Data is poised to be the catalyst that leads to the successful growth of machine learning applications.

More Specialized Data Skill-sets Needed

One of the trends in Big Data use and the exponential development of data is that we cannot keep up with demand in some respects. Most notably is the shortage of skilled data professionals. Jobs are growing at a rate of 25 to 50% in some areas, whereas, the availability of data professionals, especially in the high-end sector is falling behind. In specialized areas such as artificial intelligence, there is an estimated 50% shortage in demand versus supply of skilled applicants.

Automatic Databases

With an existing gap between big-data needs and skills available, data specialists are developing automized data systems. A system may be rather expensive to implement, but once implemented, data collections, storage, back-up, reporting, and analytics functions can all be performed relatively easily by end users rather than data specialists. Database administrative functions will largely fall away, and only high-end tech skills or supplier support will be needed. These databases have their key application in commercial technology where data functions are more repetitive.

Data Integrity Concerns

With large and unstructured data sources entering the domain of data analytics, some fear is raised as to how to ensure the integrity of the data, however this is being addressed by software developments, especially in machine learning for processing of language-based values such as social media data and audio or image files.

Software Growth and Open Source

Because Big Data has proven so many revenue driven applications, it’s growth has prompted a surge in technology applications that match. The easiest applications to develop are open source, since it is a technology that allows anyone to view the source code, and to modify or add on to it.

One wonders if open source is as good if it’s freely available. The proof is in the widespread use of open source code in all types of commercial operations as preferred software. Because it’s open and freely available, the source code has been able to grow and improve with each new iteration. The benefits of a sharing economy help everyone gain better products, a concept that is spreading to other industries.

Open source is definitely the future of Big Data, be it in software applications, or in sharing of all non privacy-related information uncovered.


In a quick summary of the trends talked about above, here are the following key takeaways from this article:

Big Data applications will continue to expand, while currently dominant in big business and technology domains, it will reach further into health, education, and consumer applications;

Technology in Big Data will continue to grow, in particular open source software, attributed networks, and No-SQL technology, stream and in-line analytics, pattern recognition and machine learning;

Data applications will become easier and more automated, as like all technology, it moves from high tech to reachable for everyone;

At the same time, the need for expert-level data specialists will continually expand and training a workforce to keep up with skills will be needed.

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