Vs in big data Big data is usually defined by 3V: velocity, volume and variety of data I'm looking for more "V", here's a list that will probably be more complete!
| V | Defenition |
|---|---|
| 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 prediction |
| 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 |
| Velocity | Not only is the volume of data ever increasing, but the rate of data generation (from the Internet of Things, social media, etc.) is increasing as well |
| VENTURESOMENESS | Venturesome Big Data has quality or state of being Venturesome, which always brings some new adventurousness |
| Venue | Data science work takes place in different locations and under different arrangements: Locally, on customer workstations, and in the cloud |
| Veracity | Reproducibility is essential for accurate analysis |
| VERBOSITY | Loquacity, Garrulity, Volubility Big data is a huge data, which comes from various sources they may be structured or unstructured data, and good or bad data. Bad data refer to the information, which is wrong, incomplete or out of date. The effects of storing these types of information and data may be risky sometimes. Therefore, it is recommended to check that the stored data is secured, complete, relevant, and trustworthy |
| Verdict | As an increasing number of people are affected by models' decisions, Veracity and Validity become ever more important |
| VERIFICATION | "Authenticity, Desired Outcome Verification |
| valuable | data, i.e. storing clinical data for a new drug on cheap and unreliable storage may save money today but can put data on risk tomorrow |
| VERBO | |
| VERSATILITY | Adaptable, Alterable, Polytrophic Big data is evolving to satisfy the desired requirements of various organizations, researchers, and Government as well. It facilitate the urban planning, visualization, environment modelling, quality classification, analysis, computational analysis, securing environment and manufacturing process required by organizations with cost-effective models and sophisticated exploration of the result |
| Versed | Data scientists often need to know a little about a great many things: mathematics, statistics, programming, databases, etc |
| Version Control | You're using it, right? |
| VERSIONING | Version Control System Data science and software development both are involve in writing codes. Data science tends to be more iterative and cyclical, where one cycle often starts with some initial understanding of the data. This cycle moves to collect, explore, clean, transform the data, and finally to build, validate, and deploy machine-learning models |
| VERVE | Spirit, Excitement Verve is refers to the spirit, energy, liveness, excitement of Big Data, always anywhere for all |
| Vet | Data science allows us to vet our assumptions, augmenting intuition with evidence |
| Vexed | Some of the excitement around data science is based on its potential to shed light on large, complicated problems |
| Viability | It is difficult to build robust models, and it's harder still to build systems that will be viable in production |
| Vibrant | A thriving data science community is vital, and it provides insights, ideas, and support in all of our endeavors |
| Victual | Big data — the food that fuels data science |
| VIOLATION | (In privacy): Crimes, Terrorist Activities Big data applications have helped analyze and solve many datascience problems for businesses and governments as well. Governments have used Big Data applications to identify criminals, detect terrorist activities, and enhance citizen services. For example, in a smart city, vehicle movement can be tracked through sensors to determine volumes and patterns of traffic [45]. This information can then be linked with vehicle owner information to determine the relationships between age groups & their travel times and locations. This analysis can then be used for improved city planning |
| Viral | How does data spread among other users and applications? |
| VIRTUALIZATION | Effective, Implied, Implicit Data Virtualization refers to an approach of data management, which allows an application to retrieve and manipulate data with no technical details about the data i.e. how data is formatted at source, or where it is physically located [35]. This approach should also be able to provide a single customer view of the overall data [36]. Unlike the traditional extract, transform, load process, the data remains in place, and real-time access is given to the source system for the data. This approach reduces the risk of data errors in the workload moving data around that may never be used, and it does not attempt to impose a single data model on the data. The technology also supports the writing of transaction data updates back to the source systems [37] |
| Virtuosity | If data scientists need to know a little about many things, we should also grow to know a lot about one thing |
| Viscosity | Related to Velocity; how difficult is the data to work with? |
| Visibility | Data science provides visibility into complex big data problems |
| Visualization | Often the only way customers interact with models |
| VITALITY | Stamina, Bouncing, Vigorousness, Growing The quality or state of Big Data being vital; the principle of life; vital force; animation; as, the vitality of eggs or vegetable seeds; the vitality of an enterprise |
| Vivify | Data science has the potential to animate all manner of decision making and business processes, from marketing to fraud detection |
| Vocabulary | Data science provides a vocabulary for addressing a variety of problems. Different modeling approaches tackle different problem domains, and different validation techniques harden these approaches in different applications |
| Vogue | "Machine Learning" becomes "Artificial Intelligence", which becomes...? |
| Voice | Data science provides the ability to speak with knowledge (though not all knowledge, of course) on a diverse range of topics |
| Volatility | Especially in production systems, one has to prepare for data volatility. Data that should "never" be missing suddenly disappears, numbers suddenly contain characters! |
| Volume | More people use data-collecting devices as more devices become internet-enabled. The volume of data is increasing at a staggering rate |
| VOLUNTARINESS | S: Permissive, Discretional Big data is a big ocean of big amount of data. This big ocean should be free for volunteer usage by different organizations without any restrictions and without any interference. Voluntaries of Big Data refers to its assistance retailers by giving them knowledge of customer preferences, urban planning by visualization of environment. Big Data voluntarily help numerous enterprises. It modelling and traffic patterns, manufacturers by predicting product issues to optimize their productivity and to improve the equipment and customers performance. Those companies who producing energy improving efficiency by reducing the losses, healthcare’s professionals to prevent diseases and improving patient health [32]. Research organizations improving efficiency to obtain quality of research and revolutionize life science, medical science, physical science and scientific research [33], [34], financial service organizations improving efficiency to identify and prevent fraud, government agencies to expand services in their respective fields. All these collaborators need the voluntary behavior of the Big Data |
| Voodoo | Data science and big data aren't voodoo, but how can we convince potential customers of data science's value to deliver results with real-world impact? |
| Voyage | May we always keep learning as we tackle the problems that data science provides |
| Vulpine | Nate Silver would like you to be a fox, please |