The 42 V’s are a broad explanatory list of ideas associated with big data and data science, published by Tom Shafer at Elder Research in 2017. They are not a technical standard, an exhaustive taxonomy, or 42 equally formal measures: the list mixes practical concerns such as data quality and deployment with communication ideas and deliberate wordplay.
What the 42 V’s are—and what they are not
Shafer presents the V’s as a way to build and communicate a mental model of a complicated subject. As he puts it, “Understanding and effectively communicating a concept often requires first building a simple mental model.” He also cautions that “This kind of model trades correctness (shaving off “unnecessary” detail) for an increased ability to grasp the larger picture.” That trade-off is central: the list is useful as a prompt for discussion, not as a checklist every data project must satisfy.
The article describes earlier lists of three, four, seven, and ten V’s, and incorporates terms from multiple prior articles. Shafer writes that Gartner perhaps helped start the alliterative framing in 2001; that is a qualified attribution, not a settled origin claim. The number 42 is simply the count of entries in Shafer’s list, not a measured property of big data.
Data characteristics and quality
These terms describe what data is like, how it changes, and whether it can support dependable analysis.
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- Vagueness: Data can be ambiguous or unclear, regardless of how much of it is available.
- Validity: Analytical rigor matters if predictions are to be valid.
- Variability: Sources and inputs differ; production models may encounter changing data.
- Variety: Data work spans formats such as flat files, relational databases, and graph networks, which can differ in completeness.
- Veracity: Reproducibility is important to accurate analysis.
- Volatility: Production systems need to cope with changing data and unexpectedly malformed inputs.
- Volume: The amount of data can grow as data-collecting devices become more common.
- Velocity: Data-generation rates can increase alongside the amount of data.
- Viscosity: Related to velocity, this asks how difficult data is to work with.
- Vastness: Shafer connects data growth with the Internet of Things.
- Viral: Consider how data spreads across users and applications.
Analysis, modeling, and decision-making
This group focuses on the work of finding patterns, making predictions, and using results responsibly.
- Vaticination: Predictive analytics forecasts outcomes; accuracy depends on analytical rigor and problem complexity.
- Veil: Data analysis can be used to examine latent variables.
- Vantage: Big data can offer a view of complex systems.
- Varifocal: Bringing perspectives together can reveal both a broad picture and finer details.
- Vane: Data science can help point decision-making in a useful direction.
- Vet: Use evidence to examine assumptions and intuition.
- Vanilla: A simple model, built rigorously, can still provide value.
- Verdict: When models affect more people, validity and veracity become more consequential.
- Visibility: Data science can make complex data problems easier to see.
- Visualization: Visual displays are one common way customers interact with models.
- Vocabulary: Modeling and validation concepts give teams language for addressing different problems.
- Voice: Data science can support informed discussion across topics without implying complete knowledge.
- Vivify: Data science can animate decision-making and business processes.
- Value: Data science can provide value as data and techniques develop.
People, systems, and the work around data
Data science is not only analysis. Shafer’s list also points to the skills, infrastructure, and organizational conditions that help work move from an idea to use.
- Versed: Data scientists draw on mathematics, statistics, programming, databases, and other fields.
- Virtuosity: Effective practitioners combine breadth across subjects with depth in at least one.
- Version Control: Tracking changes is a practical software-development concern for data work.
- Vault: Security matters because large data sets are often sensitive.
- Venue: Work may happen locally, on customer workstations, or in the cloud.
- Viability: Building robust models is difficult; making systems viable in production is harder still.
- Veer: Agile work should be able to change direction as customer needs evolve.
- Varnish: User interaction and polish matter, not just the underlying model.
- Vibrant: A thriving data-science community supports learning and exchange.
- Victual: The list metaphorically casts big data as fuel for data science.
- Vexed: Difficult, complicated problems are part of what motivates data-science work.
The playful and rhetorical V’s
Some entries are intentionally lighthearted or provocative rather than technical categories. They round out the list’s mnemonic style and should not be mistaken for formal data properties.
- Valor: A playful call to take on difficult problems.
- Varmint: A humorous reminder that bugs can grow along with data systems.
- Vogue: A playful observation about fashionable shifts in terminology, such as “Machine Learning” and “Artificial Intelligence.”
- Voodoo: A rhetorical challenge to explain data science’s practical value and impact.
- Voyage: A lighthearted reminder to keep learning.
- Vulpine: A playful reference to Nate Silver’s characterization of a “fox.”
How to use the list
Use the V’s as conversation prompts rather than a scorecard. For a project, the practical terms can help a team ask whether its data is understandable and suitable, whether changing inputs could break a model, how sensitive information is protected, and what it will take to put the system into production. The broader terms can open discussion about who needs to understand the results, how they will interact with them, and whether the team has the skills to maintain the work. The playful entries are memorable flourishes, not extra requirements.
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