Big Data – 3V and 3W

By Bob Travica

Big in volume, variety, and velocity (speed) – that is what Big Data is about. The defining three “V” aspects are depicted in the figure below. You search the Internet looking for some brand product, purchase it in a Web store, “like it” of the Facebook, talk to people who talk about it in some other social medium, mention it in texting via your cell phone… All these data (and more) coming from different sources (high variety), may be collected continuously (high velocity) and submitted to customer profiling analysis. The data can also be compared to historical records for the same customer, which would grow it in volume. Another volume booster is by looking at other products associated with the same customer, or by looking across customers sharing the interest in the same brand product. Although it hasn’t been around for long, Big Data has generated big interest. Technologists are interested in the technical part which challenges traditional database systems. Data in various formats and states of organization challenge rules behind well-known relational databases. Say, integrating text messages with data in traditional tables requires new technologies, such as Hadoop and NoSQL. Players in the use and management arena are equally interested in Big Data.

Applicability of Big Data creates a list that keeps growing. Here is where what I call the first “W” of Big Data surfaces—worth: the monitoring of oil-well sensors, human genome research, managing energy grids and transportation networks, studying cancer cell behavior, observing patient vital signs and bacteria sensors in animals, analyzing product-related sentiments on social media, tracking cell phone communications and locations for business purposes…

The worth list is so long that I need to insert a new passage for easier reading. Big Data enable personal analytics in many areas, including fitness, tracking of psychological moods, managing finance, and handling romance. Monitoring technology performance spreads to cars, athletic equipment, machinery, home appliances, power grid and consumer devices. International threats and lawbreakers are more effectively tracked.

In economy, one gets better understanding of business fundaments, such as the customer, competitor, partner, employee, product, performance, and market. Some writers differentiate between transactional data (for example, messages exchanged) and the data about transactional data – metadata (the identification of communication systems used, actors’ locations, times, message size, etc.). Some authors assume that Big Data is more about these contextual descriptors than the content itself. I’d say, it’s about both.

The worth of Big Data may overshadow potential threats. Big Data is not necessarily all good. Beware of another “W” that stands for “worry.” While some of the profiled customers from the example above may be happy when getting an unsolicited marketing message based on their profiling, others may not. Big Data analysis can also break anonymity of an evaluator of films watched in separate digital environments that intersect with the Internet. Consequences may be worrying if the evaluator is from a conservative, small town, living an alternative life style associated with the film’s topic. Consequences may be quite worrying if the evaluator is tracked down by authorities who advocate against the filmed behaviors endorsed by the evaluator.

Profiling lawbreakers appears a useful social use of Big Data. But if a lawbreaker profile is automatically attributed to a wrong person, a big worry arises for all parties involved. The problem gets even worse if the police act upon criminal profiling with the hope of preventing crime before it happens. No matter how good a prediction based on history and profiling of law offenders is, there is always a probability that a crime may not occur. Arresting-just-in-case pre-empts the due process, and turns crime prevention into a mockery of legality. A long-term worry arises from the character of digital footprints everybody does or will have to leave behind. These footprints grow over time into permanent profiles that may haunt a person to the end of life. A picky employer and oppressive political regime come to mind as unexpected users. But all possible users cannot even be conceived today. Migration in the space will no longer provide an escape. As in the other worrisome cases, freedom is the ultimate victim.

As Big Data evolves in technology and problem solving applications, uncertainty will paint relationships between firms and between countries, since digital and economic divides will widen. Consequences of the divides are hard to predict, as they will become part of opportunistic moves and emerging strategies.

The remarkable contrast of the worry and worth brings us to the third “W” of Big Data. It stands for “wisdom.” The development, use and management of Big Data require big wisdom on the part of all the players in order to avoid the big worries that parallel the big worth. Instead of unconstrained optimism due to technological possibilities or expected particularistic benefits, I recommend wise weighing of options. With Big Data, each step in its life process is big.