The 42 V’s of Big Data and Data Science

Understanding and effectively communicating a concept often requires first building a simple mental model. Consider, for example, how we teach the physical laws to students: it helps to walk with algebra before you can run with calculus. This kind of model trades correctness (shaving off “unnecessary” detail) for an increased ability to grasp the larger picture.

In 2001, Gartner (perhaps) accidentally abetted an avalanche of aliteration with an article that forecast trends in the industry, gathering them under the headings Data Volume, Data Velocity, and Data Variety. Of course inflation continues its inexorable march, and about a decade later we had the 4 V’s of Big Data , then 7 V’s , and then 10 V’s .

But it’s 2017 and we now operate in an ever more sophisticated world of analytics. To keep up with the times, we present our updated 2017 list: The 42 V’s of Big Data and Data Science.
 
Vagueness: The meaning of found data is often very unclear, regardless of how much data is available.
Validity: Rigor in analysis (e.g., Target Shuffling ) is essential for valid predictions.
Valor: In the face of big data, we must gamely tackle the big problems.
Value: Data science continues to provide ever-increasing value for users as more data becomes available and new techniques are developed.
Vane: Data science can aid decision making by pointing in the correct direction.
Vanilla: Even the simplest models, constructed with rigor, can provide value.
Vantage: Big data allows us a privileged view of complex systems.
Variability: Data science often models variable data sources. Models deployed into production can encounter especially wild data.
Variety: In data science, we work with many data formats (flat files, relational databases, graph networks) and varying levels of data completeness.
Varifocal: Big data and data science together allow us to see both the forest and the trees.
Varmint: As big data gets bigger, so can software bugs!
Varnish: How end-users interact with our work matters, and polish counts.
Vastness: With the advent of the Internet of Things (IoT), the “bigness” of big data is accelerating.
Vaticination: Predictive analytics provides the ability to forecast. (Of course, these forecasts can be more or less accurate depending on rigor and the complexity of the problem. The future is pesky and never conforms to our March Madness brackets.)
Vault: With many data science applications based on large and often sensitive data sets, data security is increasingly important.
Veer: With the rise of agile data science , we should be able to navigate the customer’s needs and change directions quickly when called upon.
Veil: Data science provides the capability to peer behind the curtain and examine the effects of latent variables in the data.