Big Data Analytics
In 1983, author Isaac Asimov published a remarkable science fiction anthology called Foundation. Its central theme was the use of mathematical models to envision the progress of civilization and to establish appropriate benchmarks for its successful continuance over thousands of years. In essence, the book focused on big data as the driving force behind the biological and political furtherance of humankind, a science that proved remarkably efficient until (spoiler alert!) it was undone by a completely unforeseen and random element: a mutation.
As companies and organizations worldwide scramble to incorporate big data analytics into their commerce strategies, they are reminded constantly of the importance of staying up-to-date with the latest that the numbers can provide: real-time analysis of consumer demand, trends, peaks and means, and using this to shape their game plan. Management is often criticized for being slow to embrace change and for holding back the deployment of new technologies to address a swiftly-changing, consumer-led economy. Yet it is equally important for management to exercise the privilege of the position: to express due concern that the random element must always be factored into an equation for there to be true balance.
Take SQL Injection as an example. This is the act of injecting malicious SQL code into a database by squeezing it into an otherwise innocent form field on, let’s say, a website’s shopping cart page. Normal shoppers recognize the space required for the 16-character credit card number, and fill it in accordingly. But hackers and thieves – the mutation in an otherwise ideal commerce scenario – see this panel as an entry point into which they can place their own destructive code.
Great Power In Numbers
Random elements exist anywhere that data exists. Password security is often compromised by sloppy human behavior – writing passwords down, or using ones that are easily guessed. Data breaches are often caused by careless employees, rather than sophisticated international hacks. These are the mutations that chip away at the sanctity of an otherwise perfect digital system.
With big data, numbers have great power, but outliers and random elements can skew results badly. Something as simple as a Twitter meme gone wrong or a rogue tweet from an employee may be sufficient to destroy a hard-won brand identity.
Recently, a well-known provider of off-site airport parking services was caught short-handed when a freak winter storm deposited two feet of snow over all of the cars parked in its lot. The storm happened over a Saturday night and ended just as planeloads of tourists returned from their sunny vacations, looking for their vehicles at 6:00 a.m. on a Sunday morning.
With only one staff member on duty, the company was forced to go into damage control, dealing with hundreds of frustrated families, along with media coverage. Storms are, of course, a normal part of winter, but their randomness makes the allocation of staff and resources on a quiet weekend something less than a perfect science, regardless of the analytics available.
All of these examples, the snowstorm, the twitter memes and the SQL hacks, conceal a critical lesson that must occupy a space at the strategic table: big data is powerful, and can assist companies in engaging with their customers more efficiently, profitably, and individually. But to be seduced by the data alone – without allowance for the random mutation – leaves a company exposed to the realities of life, which are not always calculable.
For more on this topic, please visit businessvalueexchange.com, sponsored by HP Enterprise Services.
By Steve Prentice