The 10 Vs of Big Data
Big data goes beyond volume, variety, and velocity alone. You need to know these 10 characteristics and properties of big data to prepare for both the challenges and advantages of big data initiatives.
- By George Firican
- February 8, 2017
The term big data started to show up sparingly in the early 1990s, and its prevalence and importance increased exponentially as years passed. Nowadays big data is often seen as integral to a company's data strategy.
Big data has specific characteristics and properties that can help you understand both the challenges and advantages of big data initiatives.
You may have heard of the three Vs of big data, but I believe there are seven additional important characteristics you need to know. Conveniently, these properties each start with v as well, so let's discuss the 10 Vs of big data.
Volume is probably the best known characteristic of big data; this is no surprise, considering more than 90 percent of all today's data was created in the past couple of years. The current amount of data can actually be quite staggering. Here are some examples:
-- 300 hours of video are uploaded to YouTube every minute.
-- An estimated 1.1 trillion photos were taken in 2016, and that number is projected to rise by 9 percent in 2017. As the same photo usually has multiple instances stored across different devices, photo or document sharing services as well as social media services, the total number of photos stored is also expected to grow from 3.9 trillion in 2016 to 4.7 trillion in 2017.
-- In 2016 estimated global mobile traffic amounted for 6.2 exabytes per month. That's 6.2 billion gigabytes.
Velocity refers to the speed at which data is being generated, produced, created, or refreshed.
Sure, it sounds impressive that Facebook's data warehouse stores upwards of 300 petabytes of data, but the velocity at which new data is created should be taken into account. Facebook claims 600 terabytes of incoming data per day.
Google alone processes on average more than "40,000 search queries every second," which roughly translates to more than 3.5 billion searches per day.
When it comes to big data, we don't only have to handle structured data but also semistructured and mostly unstructured data as well. As you can deduce from the above examples, most big data seems to be unstructured, but besides audio, image, video files, social media updates, and other text formats there are also log files, click data, machine and sensor data, etc.
Variability in big data's context refers to a few different things. One is the number of inconsistencies in the data. These need to be found by anomaly and outlier detection methods in order for any meaningful analytics to occur.
Big data is also variable because of the multitude of data dimensions resulting from multiple disparate data types and sources. Variability can also refer to the inconsistent speed at which big data is loaded into your database.
This is one of the unfortunate characteristics of big data. As any or all of the above properties increase, the veracity (confidence or trust in the data) drops. This is similar to, but not the same as, validity or volatility (see below). Veracity refers more to the provenance or reliability of the data source, its context, and how meaningful it is to the analysis based on it.
For example, consider a data set of statistics on what people purchase at restaurants and these items' prices over the past five years. You might ask: Who created the source? What methodology did they follow in collecting the data? Were only certain cuisines or certain types of restaurants included? Did the data creators summarize the information? Has the information been edited or modified by anyone else?
Answers to these questions are necessary to determine the veracity of this information. Knowledge of the data's veracity in turn helps us better understand the risks associated with analysis and business decisions based on this particular data set.
Similar to veracity, validity refers to how accurate and correct the data is for its intended use. According to Forbes, an estimated 60 percent of a data scientist's time is spent cleansing their data before being able to do any analysis. The benefit from big data analytics is only as good as its underlying data, so you need to adopt good data governance practices to ensure consistent data quality, common definitions, and metadata.
Big data brings new security concerns. After all, a data breach with big data is a big breach. Does anyone remember the infamous AshleyMadison hack in 2015?
Unfortunately there have been many big data breaches. Another example, as reported by CRN: in May 2016 "a hacker called Peace posted data on the dark web to sell, which allegedly included information on 167 million LinkedIn accounts and ... 360 million emails and passwords for MySpace users."
Information on many others can be found at Information is Beautiful.
How old does your data need to be before it is considered irrelevant, historic, or not useful any longer? How long does data need to be kept for?
Before big data, organizations tended to store data indefinitely -- a few terabytes of data might not create high storage expenses; it could even be kept in the live database without causing performance issues. In a classical data setting, there not might even be data archival policies in place.
Due to the velocity and volume of big data, however, its volatility needs to be carefully considered. You now need to establish rules for data currency and availability as well as ensure rapid retrieval of information when required. Make sure these are clearly tied to your business needs and processes -- with big data the costs and complexity of a storage and retrieval process are magnified.
Another characteristic of big data is how challenging it is to visualize.
Current big data visualization tools face technical challenges due to limitations of in-memory technology and poor scalability, functionality, and response time. You can't rely on traditional graphs when trying to plot a billion data points, so you need different ways of representing data such as data clustering or using tree maps, sunbursts, parallel coordinates, circular network diagrams, or cone trees.
Combine this with the multitude of variables resulting from big data's variety and velocity and the complex relationships between them, and you can see that developing a meaningful visualization is not easy.
Last, but arguably the most important of all, is value. The other characteristics of big data are meaningless if you don't derive business value from the data.
Substantial value can be found in big data, including understanding your customers better, targeting them accordingly, optimizing processes, and improving machine or business performance. You need to understand the potential, along with the more challenging characteristics, before embarking on a big data strategy.
For Further Reading:
George Firican is the director of data governance and business intelligence at the University of British Columbia. He has over ten years of project management and business/technical analysis experience in higher education, software and Web development, and e-commerce industries. George’s passion for data and continuous improvement led him towards successful program implementations in the data governance, information quality, and business intelligence fields.
He can be followed on Twitter (@georgefirican) or reached via e-mail at George.Firican@ubc.ca.