It is an indisputable fact that Big Data has revolutionized the way we do business. From helping credit card companies with fraud detection to introducing enormous profitable changes in business practices, Big Data has found application in a number of beneficial ways. Nevertheless, the field has its own set of issues and glitches that hamper smooth implementation. Industry experts claim that the defects inherent to this technology can be overcome only in the long run, when some changes are introduced in its functioning. This has also spawned a number of critics and naysayers, many of whom claim the technology is overhyped - and as not worthy of investment of time and effort.

The recent Gartner’s Hype Cycle report for emerging technologies in 2015, published in August, 2015, has only added fuel to the fire by removing Big Data off its hype cycle. Gartner Inc., a research and advisory company, had previously predicted the fall of Big Data in 2014. 

However, regardless of its demerits, Big Data continues to thrive, with the technology being immensely popular with most clients on account of its major functionalities, and the subsequent job scope it offers. Read on to find out why Big Data is claiming the lion’s share of the market in IT as well as business, what changes experts recommend in its functioning, and whether the hype surrounding it is justified.

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Betsy Burton, a Gartner Analyst, has stated that Big Data will not be considered to be an emerging technology and claims that the deletion of big data from Gartner’s Hype cycle in 2015 is a prognosis rooted in research. However, it is evidently a technology in demand as a multitude of other technologies like the Internet of Things (IoT), content analytics, and cloud computing are influenced by big data analytics.

The Hype Factors

 The following are some reasons why Big Data is criticized as being overhyped:

1. Inaccuracies

Given the scale and complexity of Big Data solutions, certain inaccuracies and subsequent defects that affect quality adversely are to be expected. As a result, this is not a technology that can be relied upon entirely, when making critical decisions based on data analysis.

The failure in the much-hyped Google Flu Trends (GFT) to accurately predict flu levels since August 2011 is one such case of inaccuracy in Big Data Analytics.

2. Overplaying the value of harvesting greater volumes of data

Another hyped factor is that harvesting more data will create more value. This is not true. Data with a certain amount of history is always more beneficial than large, freshly-acquired data sets. Instead of mining more data, which can ultimately prove futile in its application to produce desired results, even less data with more history will prove beneficial.

In this regard, Michael Jordan, a professor in the University of California, Berkeley, a respected authority on machine learning, points out that the combination of results that a humungous volume of data can produce continues to grow exponentially and therefore cannot be accurate. Further, as a possible solution to this flaw in the technology, he adds that ‘error bands’ could be introduced in all the predictions that are based on Big Data analytics. This will, in turn, improve accuracy in prediction.

3. Lack Of Standard Model For Repeatability: while Data Scientists can create insights based on results derived from this technology, one needs an entire operating model to apply and put to use the collected data and the analytics in a repeatable manner. One possible solution could be to embed Artificial Intelligence with Big Data Analytics, in its application. Applying experience and knowledge into insights is another way in which results from Big Data Analytics can be implemented effectively.

In the long-term, it could be said that Big Data, despite being a technology that needs improvement, cannot be swept off existence. As Jordan himself points out - “the field will continue to progress and move forward, because it’s real, and it’s needed.” As improvements are constantly researched and introduced, Big Data will continue to remain the most sought-after technology in a majority of industries for a long time to come.

As we shall now see, the utility of Big Data more than makes up for these shortcomings, rendering any criticism of the technology as being overhyped baseless.

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How Is Big Data Revolutionizing The Current Age?

Big Data has now become a common buzzword even among professionals from other industries, owing to the way it has changed how businesses operate. Big Data and Analytics technology includes the frameworks, tools, and techniques involved in collecting, processing, and analyzing petabytes and exabytes of data that may be structured, unstructured, or semi-structured.

And, at the most basic level, if the fact that 90% of all the existing data in the world was generated only in the last two years is true, then the value and importance of Big Data becomes self-explanatory. A technology that can handle the huge volumes of data transmitted, which can administer these data sets and use it beneficially, is then the need of the hour.

Functions, Uses, And Importance

Big Data, by storing and administering large volumes of data, has proved its potential in various industries, in many beneficial ways. Stored information or data in different systems, silos, and people, once retrieved, serves as knowledge that helps in understanding the nature and scope of businesses. In turn, it has brought about radical, revolutionary changes in the ways in which business is done.

For instance, Big Data analytics has helped in the development of a one-to-one conversational relationship with customers, as it stores and analyzes data relating to customer behaviors and choices, on the web. By doing so, both the customer and the businesses meet their desired outcome as support was provided to the customer right from the beginning of purchasing a specific product, rather than intruding into the customer’s choice halfway, when the customer’s mind is already set. Thereby, this not only improved customer experience, but also proved beneficial for businesses who were able to sell better and grow.

Big Data’s functions and importance varies and is relevant in a wide variety of industries. From helping predict weather conditions, aiding in recording data that determines aircraft performance information, gathering social media data from Twitter, Facebook, Instagram and Google+ for a wide variety of applications, to predicting election results, health related issues and doctor performance, Big Data Analytics has carved a place for itself in umpteen businesses. Big Data, through storing, processing and analyzing data, also helps in re-developing the products/services that one sells, and helps in making productive changes in the manufacturing process itself. There are many more useful ways in which Big Data plays a major role.

On the whole, Big Data, despite its pitfalls, is a technology that has found implementation in varied industries and one that will witness solid, predictable growth in the years to come. It has not only changed the way the tech world functions, but laid its stamp on the fundamental functioning of businesses.

Undoubtedly, the hype factors that surround this huge technology are minuscule setbacks that are bound to be overcome in the future, through constant research and testing of ideas that is already carried on by tech experts.

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Limitations of processing Big Data occur when internal data streams are able to produce only a sectional picture of the accumulated data, largely owing to the growth of digital business. Doug Laney, another distinguished analyst from Gartner, Inc., in his research article published on, comes up with three predictive trends that identify “information’s ability to transform businesses in the next few years”. Considering this is important as it comes from Gartner’s mouth.

His three predictions are that:

i.In another five years, information collected from digital medium will be deployed to “reinvent, digitalize or eliminate” most of business processes and hence the resulting products, than over a decade earlier.
That is to say, with the increase in the usage of the IoT (like connected devices, smart machines and sensors), the ability to generate new information and thereby the ability to participate in an industry’s value stream will also increase. This will result in the automation of the traditional analog and manual processes. Hence, things become agents for themselves, people and businesses.

An instance would be when a car alerts emergency services or an insurance company schedule a service. The enhanced connectivity, communications and intelligence of things will change them to agents of services, which are currently requested or delivered by humans.
ii. In another two years from now, access to broad aspects of big data, for more than 30% of enterprise access will be by serving context to business decisions and through data broker services.

This is to say: Insights into real-time situation, both inside and outside the organization are essential requirements for the growing digital business demands. Changes in the collected data will impact the data inventory, and the enterprise data that are available in an organization’s vault are quite often insufficient to produce context awareness and to acquire subsequent competitive edge in digital business, in terms of – marketing, transportation, financial, healthcare and other business decisions.

In the near future, information services − powerful data brokers or business-centric cloud services – will deliver data to be used as context in business decisions. This could be done either by human intervention or automated.
iii.In another three years, it is expected that product tracking information will be provided by more than 20% of customer-facing analytic deployments and thereby averaging the Internet of Things.  
In the broad terms, this translates to the importance of taking efforts to shift focus from the inward information management in organizations to the participation in the pool of information assets, globally. In order to compete in an emerging digital economy, it is essential for organizations to be able to “curate, manage and leverage big data, IoT content, social media, local and federal government data, data from partners, suppliers and customers, and other exogenous data sources that are materializing.” The key contribution this will make to a business model is to develop a sense of transparency between the customer and a partner relationships.
With these predictions, it is self-explanatory that Big Data’s implication in the current industry trends is vast and it is bound to stay and find its beneficial implementations through the many Advanced Analytics that often act as branches of the common Big Data trunk.

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