What is Big Data?
The first mention of Big data was in 1997 when NASA scientists, Michael Cox and David Ellsworth stated that the visualisation of some scientific problems (specifically fluid and gas dynamics) poses significant challenges to computer systems (Guess, 2012). This primarily means an increased need for storage of vast amounts of input data both in RAM and on hard disks, which is simply characterised as the problem of big data.
The Guardian (2016), highlighted that debate over Big Data grown quite a bit in the last few years. Everybody is talking about the benefits and drawbacks of the big data revolution. Even the businesses are interested in collecting valuable data, employees are facing the challenges of using them effectively. According to Techrepublic (2020), a study showed that only 25% of respondents said they feel free to use collected data. Another 37% think their decisions are influenced by analysing data, and 74% feel overwhelmed when working with data at all.
How Big data is defined?
Oracle defines Big data as ‘data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs. Every day we are doing something which is related to Big Data solutions. For example, whenever a search engine automatically completes term that users are typing or an online bookstore suggests a title that they might like. New technologies made it easy to collect data from smartphones and desktops. That data can be collected from different places, by visiting different sites, joining groups, commenting on social networks and websites, downloading and using applications, through different questionnaires, registering and buying products on sites (Segal, 2019).
IBM states that this collected data enables analysts, researchers and business users to make better and faster decisions. Previously, this data was unknown and unused. Businesses now have the opportunity to take advantage of advanced analytics techniques. These techniques are machine learning, predictive analytics, data mining and a wide range of statistics. All of this helps us to take a bigger market share or get more customers.
3Vs – volume, velocity, variety
The big data is a very comprehensive terminology and this post will explain the main three concepts which are volume, velocity and variety.
What is 3Vs?
As Forbes (2012) states:
Volume indicates the essential characteristic of the big data trend, which is a massive amount of data (Gewirtz, 2018). To have a clearer idea of how massive is volume – we can think of 2 billion users on Facebook, 1 billion on YouTube and 700 million users on Instagram who contribute to billions of images, posts, and videos (Whishworks, 2017). Many companies are in possession of a huge amount of data but are struggling to find ways to process it.
Velocity indicates the speed at which new data arrives. Thanks to social networks and web portals, half-an-hour news is already slightly outdated, and content needs to be continuously updated. Besides the speed at which data arrives, is the speed at which decisions are made. Some of this data needs to be processed right away and can’t tolerate the time it takes to store it. Industry calls this the “streaming data”. Some of the technologies used to handle this data are IBM’s InfoSphere Streams, Yahoo’s product S4 and Twitter’s product Storm (Dumbill, 2012).
By increasing the velocity and volume, variety experiencing significant growth and that means a huge amount of heterogeneous data (Jain, 2016). The power of data is in its variety but processing data from the raw state to the state where data is ready to be consumed by the application often causes some loss. Processing tries to structure all the data but according to Muse (2017), only 20 % of data is structured. Everything else has remained semi-structured or unstructured at all.
The real information is worth a fortune, but getting through the data forest has become increasingly difficult as never before. They are larger today than they were yesterday, and tomorrow they will be bigger than they are today. Learn more about Big data value in marketing on the next blog post.