Dear Prof Sushil,

I hope that this message finds you well. We have a couple of papers on this issue:

• Fosso Wamba, S, Akter, S., Edwards, A., Chopin, G., and Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, Vol. 165, pp. 234–246.

• Akter, S. and Fosso Wamba, S. (2016). Big Data Analytics in E-Commerce: A Systematic Review and Agenda for Future Research. Electronic Markets – The International Journal on Networked Business, Published Online.

Best regards

Firstly, It is 100% a buzz word that gets bandied around with lots of ease. I also believe it to be a relative term. For example, data that is unmanagable to one person might be easy to manage for another, to the first person, that data is Big Data.

I think it is both a large volume and how one interprets such a large volume of data. A large volume refers to data that is typically unmanageable with the usual approach of (1) load all the data into memory, (2) perform some analysis, (3) plot results.

What is and isn't unmanageable changes over time with new methods of analysing and extracting information from "big data".

So, to your quesiton.

I think it is a miperception due to the peoples relative skill with manipulating data.

I think it is misused regularly because it sounds impressive.

I don't believe there is a threshold. Perhaps it is your PC RAM ability to open the data.

In Health Sciences, I've heard people talk about "big data" when the sample size is larger than 1000 or even when the sample size is not so big but the number of variables is. Kinda makes me think of this: http://i0.kym-cdn.com/photos/images/newsfeed/000/982/077/839.png

I like how Jamie put it. I tend to over-simplify and say "if you can open the data on Excel, it's not big data".

The Big Data is not only about Size and/or Techniques, don,t forget the Type, Source, and other characteristics defined in the Vs.

In General Big Data is any data that can't be processed as a normal database in any DBMS system, what ever the reason is.

For Example: take any normal MYSQL database, this data needs to be processed in some manner that is not easy when it is in MYSQL Server, so it converted to text file then processed in map reduce cluster that produced a better, easier results, what ever the reason here, this data can be considered as Big Data.

For me Even a Relational data in any DBMS could be considered as Big Data.

It Is a general Term rather than a specific definition.

Since the data can be in multiple formats, i.e. structured, semi-structured, and unstructured, the term Big data is at times loosely used in both research and practice.

Dear Prof Sushil,

I hope that this message finds you well. We have a couple of papers on this issue:

• Fosso Wamba, S, Akter, S., Edwards, A., Chopin, G., and Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, Vol. 165, pp. 234–246.

• Akter, S. and Fosso Wamba, S. (2016). Big Data Analytics in E-Commerce: A Systematic Review and Agenda for Future Research. Electronic Markets – The International Journal on Networked Business, Published Online.

Best regards

If we talk about big data value chain, it involves lot of activities, viz', data generation, data collection, data transmission, data processing, data storage, data analysis, and so forth.

Each of the above activities involves considerable research in itself. Looking into the original question by Prof. Charles, who commented that people have misused the term 'big data' is felt because of the reason that people have researched by focusing more on any one of the above-mentioned activities of 'big data' as they have limitations to comprehensively address all in a single research paper.

And I don't think they are in any way wrong in doing so.

Indeed, to much hype here. My definition would be - when a data asset requires more than one average 1 PC to store, more than 1 structure to describe and more than one person to make analysis.

I think the last characteristic is the main point here.

I have not yet known about the term "big data". The term "large data" is existing in the literature of statistics. I think that the term "big data" has been used here to mean term "large data".

“Big data” has 5Vs as characteristics: Volume, Variety, Velocity, Veracity and Value. A solid grounding in mathematics statistics and operations research are highly recommended.

The five characteristics of big data mentioned by Cecilia Nembou can be regarded as one meaning or one definition or one characterization of big data.

Thanks to Cecilia Nembou for pointing out the five characteristics of big data that helps i finding its meaning.

Good point. I think there are three related concepts:

1. Big Data - vast data sets characterized by 3-4-5 V's

2. Big Data Analytics - a collection of tools and methods for analyzing vast data sets

3. Big Data Movement - a managerial trend or fashion involving the use of vast data sets for improving organizational performance.

Good summary on big data has been given by Vlad Krotov.

Appreciation to him.

Really interesting question and good answers as well. From my point of view, it can be (for some publications) difficult to see whether they are studying big data or data, where also similar wordings are related to business analytics, supply chain analytics or advanced analytics etc.

I've conducted some research on conceptualizing big data and to better understand its terminology as well as its applications in SCM:

• Brinch, M. (2018), “Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework”, International Journal of Operations & Production Management, Vol. 38 No. 7, pp. 1589–1614.
• Brinch, M., Stentoft, J., Jensen, J.K. and Rajkumar, C. (2018), “Practitioners understanding of big data and its applications in supply chain management”, The International Journal of Logistics Management, Vol. 29 No. 2, pp. 555–574.

If we look at the business processes within a company, they have always been using data as an input to support decision making. The difference, when it comes to big data, is that these business processes are now embedded in a big data environment (e.g. 3V's, multiple data sources, expanding IT infrastructure), where new data flows can be used as an input, which then changes business process structures and the way decisions are being made.

I hope you find this useful and would gladly discuss this further.

There is necessity of defining the two terms "big data" and "large data" very clearly. Distinguishing features/characteristics of them are also required to be searched for.

There is a distinction between different terms. In an article we recently published we surveyed studies in the field and distinguished between big data, big data analytics, and big data analytics capabilities. You can find the reference of the article below, and also the full version of it on ResearchGate. Hope this help.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2017). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 1-32.

Large scale data classified into 5 V characteristocs is referred as Big Data. But, what makes Big in Big Data is still not precisely defined.

Big Data is a marketing term in my view.

I appreciate the work reported by Samuel.

However, I agree that the term despite clarity on definition has been loosely used by many researchers. It will take time for it to stabilise and its multiple perceptions will jell together as a discipline in due course. But this should not deter us in carrying it forward as it carries immense potential.

Although the term "Big Data" is well defined in terms of Vs and Cs, I often find research papers that use it without a clear indication of what exactly they refer to: the size of the data being analysed? Or the data analytics technique or technology being used to analyse it?

Commonly known definitions are quite precise in the context what Big Data is.

“Big Data as a term used to describe data sets so large, so complex or that require such rapid processing (sometimes called the Volume/Variety/Velocity problem), that they become difficult or impossible to work with using standard database management or analytical tools.”

(NewVantage Partners. Big data executive survey: Themes & trends. [http://newvantage.com/

wp-content/uploads/2012/12/NVP-Big-Data-Survey-themes-trends.pdf]. December, 2012).

Simultaneously it is necessary to distinguish between two notions i.e. Big Data and Big Data analytics.

“Big data analytics is the often complex process of examining large and varied data sets -- or big data -- to uncover information including hidden patterns, unknown correlations, market trends and customer preferences that can help organizations make informed business decisions” (Rouse M.: Definition big data analytics. [https://searchbusinessanalytics.techtarget.com/ definition/big-data-analytics]. September 2018).

And if they refer to the size of the data, is there any threshold? When exactly can one tell that they are dealing with Big Data in a research paper? What should the size of the dataset be that would entitle us to call the data Big Data?

Tom Davenport is quite precise in this context. In his book “Big data@work” in the table 1.1 (p. 4) he writes about Big Data sets “100 terabytes to petabytes.” (Davenport T.: Big Data @ Work, Harvard Business School Press, Boston 2014).