What Are the Three V’s of Big Data?

What are the big V's of Big Data?
What are the big V's of Big Data?
what are the three vs big data that often define its characteristics

The Big Data buzzword has swept across the business world in recent times to provide businesses with an improved understanding of how their organisations can improve and grow through the use of Big Data. But what are the three V’s (valued V’s) that define its characteristics? In some ways, the three V’s define some of the challenges that businesses face in using Big Data. They describe differences between the traditional ways in which data was handled, the analytical challenges that need to be overcome, and the different types of data used. By defining these key areas, they allow business owners and managers to think about the right approach for their organisation.

Let’s start with the traditional ways in which data is handled. Often, data sets are not constructed as a whole. Rather, smaller units are collected and combined to create an overall statistical value. Businesses often treat this as the primary Big Data tools, but they overlook the fact that data quality must be considered, aggregated, and communicated in a meaningful way.

Another way in which traditional Big Data collection differs from “Big Data” is the quality of the information it provides. Traditional data collection methods are prone to errors, and the range of quality can vary greatly. For example, the quality of weather reports can fluctuate drastically from day to day and relying on just one type of weather report can be unreliable at best.

Furthermore, unlike traditional types of data collection, traditional Big Data tools are often visual in nature, providing information to users in terms of charts, maps, or graphs. As such, visualisation techniques are frequently employed to represent the data, and can change how individuals interpret the data in order to maximise its potential. However, it is important to remember that Big Data visualisation techniques are only one of many ways in which businesses can interpret or manage their Big Data.

Some may question whether or not being able to utilise the full potential of big data is a good thing. After all, those who argue that Big Data can in some way replace traditional forms of data collection argue that by only storing and accessing limited pieces of information, businesses could potentially miss out on the opportunity to create relevant, actionable data sets. Others may point out that Big Data is actually very similar to traditional forms of data collection in that it requires collecting, storing, analyzing and then presenting the information. Lastly, some may point out that because of the increasing interactivity of devices such as iPhones and iPad, consumers are now more likely to share their experiences with others than ever before.

Whilst the potential for big data has its benefits, many IT staff feel that the potential also has its pitfalls. One of the key problems encountered is trying to train IT professionals to use big data software. Although there are many ready-to-use dashboards available, most IT teams feel that it is still difficult for them to effectively use these tools, given their unfamiliarisation with the format and the peculiarities of the three-dimensional data sets produced. Furthermore, whilst IT departments may be initially overwhelmed by the volume of data produced, they are usually only required to deal with limited data sets within certain time-periods – in other words, they are usually operating within very specific time constraints.

In response to the challenges presented by big data and how it affects the way organisations operate, a number of technology vendors have released bespoke software solutions. Typically aimed at IT staff who are either already comfortable using specific open source software packages (such as Hadoop) or can find their way around the system using a library of related scripts. For example, MapR provides tools that can help users to analyse large volumes of unstructured data using sophisticated visualisations. To simplify things, users would probably only need to ask MapR for advice on how to best interpret the data and map it into useful metrics. In addition to this, some vendors offer ‘customer centric’ software solutions which are designed to make using big data much easier and intuitive for end users. In short, customers get to do what they do best – utilise and harness big data to drive improvements in their business.

In conclusion, what are the three I’s of big data? As suggested in the title of this article, big data is not only transforming businesses. It is also likely to have a significant impact on organisations over the next few years. However, as technologies become more commonplace, the v’s will begin to diminish. As such, a strong IT mindset combined with a willingness to embrace new technologies and embrace experimentation is the winning combination for ensuring businesses remain ahead of the curve.

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