First there was dot-com. Then web 2.0. Then cloud computing. Now it seems “big data” is catching all the headlines.
Big data is the term used to describe the enormous datasets that have grown beyond the ability for most software to capture, manage and process the information. But volume is not the only way to define big data. The three Vs generally used to describe big data also include the multiple types – and sources – of data (variety) as well as the speed (velocity) at which data is produced.
If you need more perspective, think about this for a second: According to IBM, 90 percent of the data in the world today has been created over the past two years. That amounts to 2.5 quintillion bytes of data being created every day.
Big data may seem to be a bit out of reach for SMBs, non-profits and government agencies that don’t have the funds to buy into this trend. After all, big usually means expensive right?
But big data isn’t really about using more resources; it’s about effectively using the resources at hand. Take this analogy from Christopher Frank of Forbes who likened big data to the movie Moneyball: “If you have read Moneyball, or seen the movie, you witnessed the power of big data – it is the story about the ability to compete and win with few resources and limited dollars. This sums up the hopes and challenge of business today.”
Specifically, it shows how organizations with limited financial resources can stay competitive and grow. But first, you have to understand where you can find this data and what you can do with it.
Ideally, big data can help resource-strapped organizations:
Small businesses can’t compete with the enormous advertising budgets that large corporations have at their disposal. To remain in the game, they need to spend less to reach qualified buyers. This is where it becomes essential to analyze and measure data to target the person most likely to convert.
There is so much data freely accessible through tools like Google Insights that organizations can pinpoint exactly what people are looking for, when they are looking for it and where they are located. For example, the CDC used big data provided by Google to analyze the number of searches related to the flu. With this data, they were able to focus efforts where there was a greater need for flu vaccines. The same can be done for other products.
Big data can be like drinking from a fire hose if you don’t know how to turn all the facts and figures into something useable. But once an organization learns how to master the analytical tools that turn its metrics into readable reports, charts and graphs, it can make decisions that are more proactive and targeted. And only then will it have an intimate relationship with the “big problems” affecting the business and an understanding of how to improve its situation.
A majority of the information in big data comes from social chatter on sites like Facebook and Twitter. By keeping a close eye on what is being said in the various social channels, organizations can get a bead on how the public perceives them and what they need to do to improve their reputations.
Take the paper “Twitter mood predicts the stock market” as an example. Johan Bollen tracked how the collective mood from large-scale Twitter feeds correlated with the Dow Jones Industrial Average. The algorithm used by Bollen and his group predicted market changes with 87.6 percent accuracy.
Imagine what you could do for you organization if you could track how people felt about you.
Data has always presented a problem when it comes to security; it’s a primary target for cyber attacks because the bad guys know that it is one of the most valuable resources a company has.
And with the growth of mobile devices used to access, analyze and input all of this data, the threat is even greater. Throw in the need for endpoint security and some big picture protection issues come into play.
However, with proper planning companies can secure data stores, on-site resources and mobile devices while harnessing big data as a tool to help them reach their goals.
By Jeff Orloff