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Author's profile photo Hemant Pal

Top Trends Dominating the Big Data Analytics Industry in 2020

We are living in an age of data deluge. Worldwide, 26 zettabytes of data were produced in 2017 and the figure is estimated to reach 175 zettabytes by 2025. The rapid digitalization across the globe has contributed to an ever-growing datasphere. In order to harness this data to their advantage, enterprises are increasingly switching to big data analytics.

When used in conjunction with technologies such as the cloud, artificial intelligence and Internet of Things (IoT), data analytics services help organizations in all stages of growth acquire a competitive advantage. Big data analytics has gone a long way from being just another industry buzzword to an integral part of every organization’s strategic objectives.

In this blog, we will discuss the top trends expected to dominate the big data analytics space in 2020 and beyond. But before that let’s try to understand what big analytics is all about.

What is Big Data Analytics?

Big data analytics refers to the complex process of examining large, complex datasets to uncover information-trends, patterns and correlations-that help organizations in informed decision-making. Companies using big data analytics are able to analyze huge volumes of structured, semi-structured and unstructured data emanating from various sources-internet browsing, social media, customer emails, survey responses, call records and machine data captured from IoT sensors.

Big data analytics services help businesses in:

  • Identifying new business opportunities
  • Branding and positioning more effectively
  • Delivering enhanced customer service
  • Improving operational efficiency
  • Acquiring a competitive advantage

Trends for Big Data Analytics in 2020

Now that we have a fair idea of what big data analytics is all about, let’s one by one discuss the trends that are dominating this space.

1) Augmented Analytics

In July 2017, Gartner published a report, in which it introduced the concept of augmented analytics. Gartner has described augmented analytics as:

“An approach that automates insights using machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market.”

To understand this further, let’s first try to understand why augmented analytics was needed in the first place.

While big data analytics can reap huge benefits for any business, it isn’t at all easy to pull off. Data analytics comprises a series of steps-collecting data from varied sources, cleaning and filtering the data, conducting the analysis, generating insights, communicating these insights to the right person(s) and deciding the right course of action. Each of these steps involves spending considerable time, money and efforts.

Plus, most of the time you need to hire data scientists and work in close association with them to ensure their analysis makes sense. And that’s cost-prohibitive for many businesses.

So this is where augmented analytics comes into the picture. The purpose of augmented analytics is the automation of insight generated through the use of advanced artificial intelligence and machine learning algorithms. This means you won’t need to depend on data scientists for insights as much because the analytical engine will do most of the work on its own. The technology is yet to reach maturity but expected to grow exponentially in the next couple of years.

2) Natural Language Processing (NLP)

Put simply, natural language processing is a branch of artificial intelligence (AI) that equips machines with the ability to read, understand and derive meaning from human language. Thanks to advancements in data processing, NLP has witnessed a boom and newer applications of the technology are emerging now and then.

Some of its prominent use-cases are:

  • Disease Prediction: NLP helps in the prediction of diseases based on electronic health records and clinical trial reports.
  • Sentiment Analysis: Companies using big data analytics use NLP to identify if the customer reviews on a particular product/service are positive, negative or neutral.
  • Spam Identification: Email service providers like Google and Yahoo identify if a particular e-mail falls in the spam category by analyzing its text.
  • Recruitment: NLP is used by organizations to filter candidates for a particular role based on the skills mentioned in their resume.
  • Financial Trading: Financial traders use NLP to track news, reports, comments, press releases pertaining to any company. They incorporate this information into a trading algorithm to earn profits.

In 2018, the market size of NLP worldwide was estimated at 5 billion USD. This figure has been forecasted to touch 43 billion USD by 2025.

3) Quantum Computing

With the proliferation of the internet, we have access to massive amounts of data, processing which can be a pain. If somehow we could reduce the processing time considerably, we’ll be able to crunch billions of data points within minutes and expedite the decision-making process. Quantum computing is what can make this possible. Quantum computers are not intended to replace classical computers-they can be used to solve complex problems that are beyond the capabilities of a classical computing device.

While conventional computers use bits, quantum computers will use quantum bits, called qubits. These computers will have processors that could work millions of times faster than the ones used today but consume far less energy. If somehow these devices are made commercially viable, large-scale enterprises will reap colossal dividends.

That said, it’s difficult to predict how quantum computing will change the world around us. With quantum computing, we will venture into an entirely new realm of physics (quantum physics) and there will be use cases we haven’t conceived of yet. In other words, we will usher into an era of endless possibilities.

Big data analytics companies around the globe, be it Google or IBM, have realized its potential and are investing heavily in the technology. In fact, IBM has been doubling its systems’ quantum volume every year since 2017. With the increase in quantum volume, the ability of its computers to solve complex, real-world problems will potentially improve.

4) Open-Source Solutions

There’s no denying that data collection and processing is an expensive proposition: you need to hire data scientists, purchase tools and techniques to get the analysis done and pay for virtual/physical storage to store raw and processed data. This expenditure can be reduced considerably by choosing open-source solutions.

Open-source not only trims down the operational expenses to a significant extent but also offers more control over the entire thing. No wonder, more and more establishments are opening up to open-source. Some popular open-source tools for big data analytics include:

Hadoop: Hadoop is among the most popular tools for big data analytics, thanks to its powerful data processing capabilities. Its success can be partly attributed to its distributed cloud storage model, HDFS that’s compatible with large-scale bandwidth and transfers data between nodes rapidly allowing for data redundancy.

That Hadoop is a Java-based framework adds to its popularity. Organizations using SAP solutions for their business processes are harnessing the power of Hadoop for mining colossal datasets.

Apache Cassandra: Apache Cassandra is a distributed NoSQL database that chiefly processes structured data and offers high availability with no single point of failure. Its scalability, availability and ability to handle multiple users across data centers have captured the attention of the likes of Apple, Netflix and eBay who are capitalizing on its capabilities to build relational databases.

5) Edge Computing

The exponential growth of IoT and the consequent proliferation of IoT devices and sensors have spurred the growth of edge computing. A vast number of IoT-based applications that are critical to any company’s operations require real-time processing of data.

IoT devices generate a colossal amount of data during their operation.  In a traditional setup, these devices need an internet connection for sending this information to or receiving information from a central cloud repository.

While a single IoT device can easily transmit data across a network, the problem arises when the number of such devices multiplies. Such an arrangement not only brings latency in the network but also costs significantly in terms of bandwidth.

In edge computing, the data emanating from IoT devices gets processed close to where it’s being produced rather than in a central cloud repository located thousands of miles away.

By switching to edge computing, organizations can avoid network latency issues and expedite the decision-making process. Processing a certain percentage of data locally also trims down bandwidth costs.

Kuba Stolarski, a research director at the IDC asserts in his report,

“With enhanced interconnectivity enabling improved edge access to more core applications, and with new IoT and industry-specific business use cases, edge infrastructure is poised to be one of the main growth engines in the server and storage market for the next decade and beyond.”

6) Dark Data

Dark data is the data a company collects, stores and processes during daily operations but doesn’t use for purposes that could be beneficial. If research reports are to be believed, around 7.5 sextillion gigabytes of data is generated globally every single day out of which 6.75 septillion megabytes remain unanalyzed or unprocessed and falls under the ambit of dark data.

It includes data from call logs, emails, old documents, employee database, voice transcripts and so on.

Organizations have their reasons for not processing this data. Either they lack the resources or bandwidth for processing additional data or believe dark data won’t enhance their decision-making process in any manner.

The mushrooming of electronic devices coupled with the growth in 5G technology will only trigger an explosion in the data space. Unless we devise ways to leverage this data to our advantage, we might miss out on a lot of eye-opening insights.

AI-ML-powered tools and techniques can play a critical role here. The trick, however, lies in using the data judiciously. Wrong use of data can result in erroneous decision-making and even invite penalties.

So, these are some trends dominating the big data space in 2020. If you are seeking top-of-the-line data analytics services, connect with our data professionals today. We’ll be glad to address your queries.

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