Three BIG Things BIG DATA is NOT, Nor Likely to Ever Be
Three BIG Things Big Data Is NOT, Nor Likely to Ever Be
With all the hype surrounding Big Data, determining what capability Big Data is delivering as well as what potential it has can be a bit cloudy. Some experts exhort praise for the amazing ways in which Big Data has already liberated the people to a newer level of understanding … and invested resources accordingly.
The White House has already invested more than $200 million in big data projects.
Goodness knows I can’t last two minutes after hitting unexpected traffic before I’m clamoring for my phone to rescue me with a new route or at least a picture of how long I’ll be trapped in the gridlock. That’s quite pedestrian for the glory that is forecast by some for Big Data (including me.)
Contrarians argue that Big Data is a fad or worse, a plague of unintended consequences from whatever benevolent or malevolent quest. Google was hailed for its ability to predict flu outbreaks weeks before the Center for Disease Control. Google did this by analysis of search requests for information about flu associated terms. This “nowcast” shook up predictive analysis in 2008 with much applause, providing specific flu information weeks before the CDC … until it didn’t. The effectiveness wore off. In 2013 Google missed the peak flu by 140%. Condemned as false prophet, Big Data does not deliver any solutions easily.
Regardless of what camp you fall into, Big Data is not, nor likely to be three things, at least for the foreseeable future.
Of course, if you could foresee the day when you’d always have a flashlight as long as you had a phone on you, you are ahead of 99.999% of us.
#1 Not a Straight Line
Big Data is not a linear process. There’s no straight, curved, looped or contorted line between problem and BD solution. You can’t walk up to a Big Data vending machine, plunk in a couple of coins and expect a sordid chain of bumps and clicks to derive the satisfying “gedunk” sound of what item you chose falling out the bottom drawer.
Big Data is a bit more like Life – it’s complex and chaotic – with all the physics and psychology lexicon trappings. A butterfly flutters its wings on the Amazon website or the South American river and a typhoon erupts in the African ecology or Chinese economy or Australian financial markets. Even holding a potential solution with intention while framing a Big Data algorithm defeats the process and purpose.
#2 Not So Scientific?
Big Data is not the traditional scientific methodology in which many have been trained to believe. The process does not involve a null hypothesis to be proven or disproven. Big Data is about observation – patterns, trends, anomalies, outliers. It’s a journey of discovery, not of certainty. Big Data creates options, not opinions.
At the end of the day, Big Data can tell what happens, which can be used to predict what will happen. Big Data though does not explain WHY, which is the more traditional demand in man’s quest to understand the world (and get one step ahead.)
#3 No Silver Bullet
Big Data is not the silver bullet that explains the WHY of the world, nor is it a solution in and of itself. Like business productivity movements such as Six Sigma or Total Quality Management or the Deming Method or ledger of similar potential, it has famed promises and variable application.
Similarly, the Internet isn’t the answer to all questions. As Google can testify with a million search results for any given query, more data – even Big Data – doesn’t mean a singular response. It’s more likely more questions and a suite of possible solutions.
Doing Big Data for the sake of being in with the Early Adaptors isn’t going to secure the future for anyone. Big Data should be implemented in harmony with the circumstances and ability of today’s framework. Big Data is best incorporated holistically, and in that regard, it is likened to the various business and life productivity trends before it.
Big Data is an embryonic capability. It’s a tool. It’s a philosophy. It’s a technique. It is not the pot of gold at the end of the rainbow.
If you’re still pondering it’s effect or lack thereof, consider these four stats from Forbes:
The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race.
Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.
By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.
Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.
(16 more cool stats from Forbes)
Big Data isn’t going away. Business operations will leverage the capability, and economies will turn in tow. Societies worldwide continue aggregating greater and greater data stores, setting soliton waves into motion that have only begun to influence global and local policy. Learning what Big Data it is and isn’t is integral to understanding how it will alter business, governments, and societies.
The mechanics of Big Data are still very much in development. The digital collection of the volume, velocity and variety of information is far from perfection, with no single or solid methodology to anchor the veracious stream. But there is gold from Big Data – for some it’s predictive analytics and for the purists it is the simple beauty of seeing what emerges. This is still an art and not so much a science.