Big Data

Big Data: Does it bring hope, or is it a hype?

By March 8, 2019 No Comments

The next big thing in data science is machine learning.


In this blog, you will discover the latest applications of data science and the aspects of algorithms within the context of business. These are driving value creation. You will discover a substantiated answer to the question: “Does Big Data bring hope? Or is it just a hype?”

The creation of new business models is propelled through the implementation of Big Data.

To start, the latest applications of data science are indicative of positive effects in the long run. Almost 6 years ago, AT&T realized that the number of errors they were making in their invoicing, could be costing them tens of millions of dollars. When they researched the issue, they discovered the difference in data notation within different data sets (invoices). More importantly, AT&T noticed that the billing number and office number changes throughout the process. An analysis yielded that more than 60% of records that went through the process, had an error. This was followed by insights as to where the issue was taking place in terms of geography and specific input field. Lastly, the specific interfaces in which the issues were taking place was uncovered.

Saving millions of dollars more

Heading 6 years into the future, and potentially saving millions of dollars more, data sets are becoming increasingly centralized or accessible. With this comes to opportunity for machine learning to play a role in the discovery of errors in systems. Insights such as those from AT&T can be proven to being incredibly useful in the generation of algorithms that solve these issues more efficiently, and on a much larger scale. Additionally, the innovations within the industry of the internet of things, are propelling the creation of new data that can be used to identify opportunities within a production system. In short, the latest applications of data science are those being driven by the more efficient possibilities to discover new opportunities. These opportunities derive from the accessibility and creation of data.

“A business must commence or advance their data science efforts.”

In the case of AT&T, the business problem formulations transitioned from being invoice-focused to process-focused. The team realized that there was an issue or a certain truth but they had yet to realize how this truth came to be. As such, AT&T intended to unravel the “new truth” by evolving the issue from being invoice-specific to process-oriented. This shift in thinking can be applied to any business. Businesses should act as “informationalizers” that create and provide relevant information to customers. This can be anything derived from the data associated with shipping containers to Heineken’s cans turning blue at a certain temperature.

An AI and Big Data Case

Let’s grab a new case that describes the combination of data science and algorithms. First, the advances in data science have made it possible to analyze large sets of data more efficiently. In airplane design, it is now possible to process data points surrounding the aircraft and combine it with expected weather conditions at certain heights. Second, and arguably the most important, what advantage does the business owner seek? In the case that it concerns safety, the business can design an algorithm that takes the data into account in order to analyze the probability of failure in varying weather conditions. With this, comes insights into when and why a part of a plane malfunctions. Thus, the business can promote the safety of their planes.

The next big thing..

The next big thing in data science is machine learning and the efficiency with which algorithms can process vast amounts of data. From this rises the opportunity to create new business models which, in turn, propel Big Data to continue to have an extraordinarily positive impact on value creation. This will be achieved through leveraging the latest applications of data science and designing algorithms that deliver unique insights. The struggle will lie in the role that hype plays in Big Data. But in order to overcome those struggles, it will be paramount to focus on transparency regarding the collection of data and the creation of algorithms. With this in mind, a business must commence or advance their data science efforts for there is hope in the opportunities surrounding Big Data.

Works Cited

Dedehayir, O., & Steinert, M. (2016). The hype cycle model: A review and future directions. Technological Forecasting and Social Change, July(108), 28-41.

Dieline. (2014). Heineken Cool Can. Retrieved January 17, 2019, from https://beta.thedieline.com/blog/2014/8/4/heineken-cool-can

Ethics Defined. (2019). The Epistemology of Ethics. Retrieved January 15, 2019, from http://www.ethicsdefined.org/what-is-ethics/the-epistemology-of-ethics/

Fenn, J., & Raskino, M. (2009). Understanding Gartner’s Hype Cycles.

Jiebing, W., Guo, B., & Shi, Y. (2013). Customer knowledge management and IT-enabled business model innovation: A conceptual framework and a case study from China. European Management Journal, 31(4), 359-372 .

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1-12.

Lente, H., Spitters, C., & Peine, A. (2013). Comparing Technological Hype Cycles: Towards a Theory. Technological Forecasting and Social Change.

Redman, T. C. (2014, August 7). Even the Tiniest Error Can Cost a Company Millions. Retrieved January 13, 2019, from https://hbr.org/2014/08/even-the-tiniest-error-can-cost-a-company-millions

Taggart, S. (1999). The 20-Ton Packet. Retrieved January 17, 2019, from https://www.wired.com/1999/10/ports/

[1] (Redman, 2014)

[2] (Kitchin, 2014)

[3] (Ethics Defined, 2019)

[4] (Jiebing, Guo, & Shi, 2013)

[5] (Taggart, 1999)

[6] (Dieline, 2014)

[7] (Dedehayir & Steinert, 2016)

[8] (Lente, Spitters, & Peine, 2013)

[9] (Fenn & Raskino, 2009)

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