Emerging Trends in Big Data for 2019 and Beyond!

Feb 20,2019 by Parul Singh
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Big Data is revolutionizing the economy, thanks to the constantly evolving information technology space. Colossal data sets are processed using specialized software platforms to gain valuable insights that are transforming businesses like never before. The Big Data industry is witnessing several noteworthy trends which will continue to grow in 2019 and beyond:

1) Evolution in the Internet of Things (IoT) space:

The Internet of Things (IoT) is a system of interrelated devices, mechanical and digital machines or objects which are provided with unique identifiers and can transfer data over a network without requiring human-to-human or human-to-machine interaction.

Connected devices and sensors in IoT space generate an enormous amount of data every day. It is estimated that by 2020 there will be over 10 billion connected IoT devices.

In order to milk the maximum benefit from the data flowing in from these devices, Big Data solutions would be required. Big Data systems can store, process and analyze heaps of data using specialized tools like Hadoop or Spark to unveil valuable trends, patterns and correlations.

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IoT devices have already invaded many aspects of our lives. Smart homes, smart watches, smart cities and Industrial IoT have become the latest buzzwords in the technology sector.

a) Smart Homes:

The smart home concept is slowly catching up and companies like Nest, Ecobee and Ring are coming up with a range of innovative solutions for modern era home-owners.

In a smart home, the appliances and devices are interconnected and can be controlled remotely through one central point, which may be a smart phone, tablet or laptop. These devices can optimize and control several crucial functions like security access, temperature, lighting and home theatre. Locks, thermostats, cameras, lights and even appliances such as televisions and refrigerators can be controlled through a home automation system.

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Companies can deploy appropriate Big Data solutions to harness this data to track consumer behavior, improve customer experience and eventually enhance customer loyalty.

b) Wearables:

The rapidly expanding market for wearables such as Apple Watch and Fitbit has provided an opportunity to healthcare professionals to collect patients’ data in real-time and prescribe the course of action.

These wearable devices can track vital health metrics of a patient such as steps taken, heartbeat, quality of sleep and/or blood pressure. These can used as inputs in Big Data systems to monitor the patient’s health closely and address risk factors on time.               

An important example in this regard is Epiwatch, an app developed by Johns Hopkins University to collect data of patients before, during and after an epileptic seizure. The app works on both Apple Watch and iPhone, and uses memory games and other activities to collect vital information on the health of epileptic patients. Researchers at John Hopkins have been using the information thus collected to predict and report seizures.

c) Industrial IoT:

With IoT becoming more ubiquitous, industries such as oil and gas, utilities, manufacturing and transportation have embraced it and are coming up with innovative applications. Big Data can empower industries to harness maximum value from the data gleaned from IoT sensors and devices. Manufacturing companies, for instance, use the data collected from sensors installed in plants to predict and schedule preventive maintenance and thus improve equipment lifespan.

By using Big Data in collaboration with IoT, businesses can have a better understanding of their data; they can make more informed decisions and stay ahead of the competition.

2) New, exciting roles in the IT industry:

Now that Big Data has become more central to the functioning of organizations, newer roles are coming up and companies are trying to rope in competent Big Data professionals in order to make the most of the data available to them. Some of these roles are:

a) Data Scientist:

A data scientist uses descriptive and predictive analytics to deconstruct large sets of data and communicates the results to different functions of an organization such as marketing, operations or IT. The role needs a high degree of proficiency in languages like Python, SAS or R, a thorough understanding of advanced statistical and machine learning techniques and an excellent working knowledge of platforms like Hadoop and Apache Spark.

b) Data Engineer:

A data engineer is one who develops, tests and maintains infrastructure (i.e. architectures and systems) which drives analysis and processing of data. They develop processes for data modelling and mining, integrate new solutions into production systems and ensure that the architecture supports business requirements. A data engineer needs to work in close collaboration with the data scientist as well with the IT team.

In coming times, newer and more exciting roles, such as Chief Data Officer will be in demand across all verticals. This will provide an opportunity to professionals to learn new technologies and flourish in the Big Data space.

3) Emergence of Dark Data and its Migration to Cloud:

Given the immeasurable volumes of data that organizations collect, it should come as no surprise that a large chunk of their data is not processed and analyzed.Research giant Gartner has labelled this data as ‘dark data’.

According to a study by the International Data Corporation, an estimated 90% of the unstructured data goes unanalyzed. Organizations seldom process several categories of data such as customer information, archived e-mails, call logs, hand-written notes, old documents and website visitor behavior, and there are solid reasons behind their failing to do so.

In many organizations, different departments have their own data collection and storage processes which are usually not known to (and, therefore, not utilized by) other departments.

A lot of data goes unused in this way. Then, there may be technological constraints (e.g. difference in file formats) in integrating data from different sources in order to paint a more holistic picture. Most of the companies have pre-decided goals before data collection and may not use the data not directly related to their end-goal.

For example, a company trying to collect employee feedback through an analytical tool considers only existing employees and disregards any information from previous employees.

This unutilized ‘dark data’, if analyzed, can provide eye-opening insights, unravel hidden patterns, and in many cases, decide the future course of action. Inability to manage sensitive data, such as customer information can throw a company into legal and financial turmoil and even cause a loss of reputation. Majority of the organizations are, therefore, migrating this data to the cloud until its best usage can be determined.

4) The Dominance of Open Source:

Till now, the Big Data space has been dominated by open source tools and technologies, and the trend will continue in 2019 and beyond. Open-source software framework Hadoop has become almost synonymous with Big Data. Hadoop is known for its large-scale distributed processing of very large datasets.

Another renowned name in open-source space is Storm, an engine for real-time processing of Big Data that behemoths like Yahoo, Twitter and Spotify have leveraged to their advantage. Open-source platforms, be it MongoDB, an open-source non-relational data storage solution or Lumify, a Big Data analysis and visualization platform or Jaspersoft, an open-source BI tool, have become central to the functioning of organizations across the industry.

Given the exponential rate at which data is being generated, more open-source tools would be made available soon. This will be particularly beneficial for small and medium-sized organizations that look for pocket-friendly solutions for data storage and processing.

5) Cybersecurity Opportunities and Threats:

The growing pool of Big Data has opened up new avenues of attack by cybercriminals who have developed more sophisticated methods of data breaching of late.

Technology-savvy criminals exploit machine learning algorithms to detect vulnerabilities in the security system and bypass security software. Traditional cybersecurity tools which were once considered effective are now becoming obsolete. These tools have been more reactive than pro-active in approach, rendering them unsuitable for current times. Besides, they lack the bandwidth for very large datasets.

Companies need robust cybersecurity measures in place as they have to safeguard personal and sensitive information (e.g. their customer database) and deal with data ownership and/or copyright infringement issues, if any.

Cybersecurity experts have kept pace with the changing times and are coming up with advanced threat detection methods such as behavior analytics and machine learning. Machine learning models are trained on voluminous datasets multiple times in an attempt to automate threat detection using supervised and unsupervised learning techniques.

Supervised learning techniques make use of labelled datasets and train the model to distinguish malicious files from benign ones.

Unsupervised learning techniques, on the other hand, use unlabeled datasets and teach the model to pinpoint anomalies in the data. These techniques, coupled with human discretion, will go a long way in building a robust cybersecurity system.

The Bottom Line:

Given the pace at which the Big Data industry is transforming, organizations need to ensure that they stay abreast of the emerging trends in this space and put in the needed resources to extract maximum value from their data! Cyfuture takes this proactivity to the highest levels!

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