Is Big Data Overhyped?

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?

Functions, Uses, And Importance

Some Newer Predictions In Big Data Trends

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 Forbes.com, 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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