A company like Visa gathers data on every swipe of every Visa card in the world on a continuous basis. This is a massive, constant stream of transactions.

A company like Walmart gathers data on every purchase at every till in real time. Imagine the volume of purchases registered by each till, and the opportunity this data may provide to customize merchandising or the like.

Social media through multiple channels like Facebook, Twitter and others has become a strategic way for companies to reach customers and to get feedback. However, if you are a big firm with a massive digital presence, how do you systematically tap into the constant flow of unstructured, textual social media comments and feedback by customers? Stopping to analyze it carefully will immediately leave you days, if not weeks, behind the real trends.

Large logistics companies marshal immense movements of products every day. For instance, Davenport & Dyché (2013: 4) report the following: “UPS is no stranger to big data, having begun to capture and track a variety of package movements and transactions as early as the 1980s. The company now tracks data on 16.3 million packages per day for 8.8 million customers, with an average of 39.5 million tracking requests from customers per day. The company stores more than 16 petabytes of data.” They go on to discuss an even bigger logistics system problem involving big data for UPS.

The growth in measurement. Businesses and other generators of data have simply found more and more ways to analyze data. From computers and networks that naturally gather data to sensors in machines and in many physical spaces, the growing volume of data offers the opportunity for more and more business research. These increased means of gathering data have led to an explosion in the amount and breadth of data collected. The speed of data collection has also increased, especially with networking and mobile and wireless connectivity.

The growth in storage. The growth in measurement has been matched with exponentially greater storage. Storing data used to be prohibitively expensive. The storage costs have dramatically declined, allowing for more and more data to be stored.

The growth in computing power and speed. The ability to move, process and analyze data has also increased dramatically. This has, in turn, allowed for the growth in data.

The growth in unstructured data. As suggested by the social media example above, unstructured data has become a far more regular feature of business information. Unstructured data is that which has no immediate, latent meaning. Often, it is textual in nature, like the flows of social media commentary that need interpretation before they can be given even the most simple “good/neutral/bad” coding. Unstructured data includes more than text, however. It includes images (for instance, photographs), sounds, and the like.

Volume. As discussed in the previous section, increasing means of measuring and other factors have led to massive growth in the volume of data. The volumes are truly staggering. For instance, IDC (2014) states, “Like the physical universe, the digital universe is large – by 2020 containing nearly as many digital bits as there are stars in the universe. It is doubling in size every two years, and by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes.”

Velocity. It’s not just about the amounts of data. Data is also being generated at faster and faster rates. Computer processing and networking, as well as increasingly fast instrumentation (such as sensors in cars or appliances), are again allowing the speed of data streaming to increase exponentially. Traditional business statistical methods required us to look at historical data at our leisure, however in business we are increasingly called on to analyze real-time data as it is being produced. Banks, for instance, wish to detect fraud as it is occurring, not afterwards, so predictive models that sift through huge volumes of transactions per second must quickly identify possible cases of fraud and report this to the fraud department for immediate follow-up.

Variety. The diversity of data has increased substantially. Whereas businesses used to have relatively small, stable and controllable datasets previously, many factors have broadened the variety. Consider the increase in unstructured data mentioned in the introduction. Examples include social media text, images such as photographs, audio data such as recorded telephonic conversations in a call center, and the like. Business data used to be largely structured, where the data had native meaning (numerical or qualitative data forced into categories). Increased ability to gather and analyze unstructured data has led to its explosion and to a commensurate increase in the huge variety of data. There has, therefore, also been an increase in the variety of sources from which data is gathered.

Value. Value speaks to the usefulness of given parts of the data being gathered. One challenge associated with increased measurement and storage capacity is the temptation to gather everything, whether useful or not. There are two views on this. The first is “why not gather everything? Storage is cheap these days and we might find trends or findings that we did not know were there.” Another view is that excessive data gathering makes for problems in merely sorting through the information. This point of view might argue for gathering data based on specific business needs.

Veracity. Along with volume, velocity and variety comes issues with data accuracy, integrity, abnormalities, and the like. This can be a real challenge, especially when you do not have the luxury of time to sit and analyze a dataset.