Big data is seeping into every facet of our lives. Smart home gadgets are becoming part of the nerve systems of new and remodeled homes, and many renters are demanding these interconnected gadgets from landlords.
But nowhere has Big Data created a bigger buzz than in business. Companies of all sizes are collecting data at a seemingly insurmountable rate. Big data is larger than ever before.
We’ve collected more data in the past two years than in the entire history of the human race. It’s also continuing to grow at an incredible rate: By 2020, analysts believe we’ll be generating about 1.7 megabytes of information per second for every human being.
This information can be useful for businesses in a wide array of mediums, from cloud computing to data processing speeds and customer relations. But just because businesses can collect all this information doesn’t mean they know what to do with it or have the resources to analyze it.
In fact, many businesses are still struggling to understand what Big Data is all about. Much of this has to do with the vast complexity of data analysis, but it probably has a fair amount to do with some of the myths that permeate the industry as well.
Here are five of the biggest.
1. Big data is large
Big data is effectively just a name, and a somewhat misleading one. When people refer to big data, they’re talking about all the data in the world, but most businesses don’t collect all of it.
They focus more on individual transaction data, which is granular and specific. Big data is made up of a lot of very small chunks data, most of which most businesses never see or collect.
The smaller size of typical data collection is highly beneficial for businesses. It’s much easier for executives to understand and control information when it’s collected in small portions.
For that reason, organizations shouldn’t get intimidated when they’re advised to use big data. It’s not nearly as overwhelming as it might sound.
2. It’s expensive to analyze data
Small businesses in particular may be afraid to collect and analyze data because they think it will have a substantial impact on their bottom line.
This might have been a problem five years ago, but today there are so many free data tools available that anyone, even a one-person operation, can analyze a lot of data.
“Availability of inexpensive but advanced analytics tools, combined with the government releasing treasure troves of data — and the avalanche of ‘user exhaust’ data generated in social networks — enables these start-ups to bring innovative products and services to market with little funding,” says an article from The Enterprisers Project.
“They do not need millions of dollars and years of development work to actually achieve significant value — or become a disruptor in the industry along the way. It does not take a team of Ph.Ds to get there either, everything is much more accessible these days.”
3. All data is good data
Another major distinction is the difference between wholesome, useful data and garbage. The quality of data varies, and companies should recognize the difference before trying to use data that won’t be of any use.
Even though the process of collecting accurate, real-time data is improving, there are still a lot of errors and superfluous detail. Photographs and videos can easily be tagged incorrectly or sarcastic content can be taken seriously.
There can also be information about a customer base that’s missing key information, which renders the rest of the data useless. Being willing to throw away data that isn’t helpful will help your business make better sense of its collections.
4. You need clean data
Though we just spoke about throwing away useless data, it’s worthwhile to make certain it’s definitely useless first. Many companies believe that “dirty data,” data that’s clouded with useless and confusing details, isn’t worth their time.
But in reality, analyzing dirty data can potentially lead to great insights. Even when the data is not clean, a firm can employ analytics processing to illuminate useful strengths from the depths of the information.
At other times, the analytics may come back with nothing. This is how you distinguish good, dirty data from clean, useless information.
5. Big data will take your job
One of the primary arguments against big data is that it’s making way for machines to take over the jobs of human analysts. This is not the case, however.
“The World Economic Forum warned that robots and technological advances will take more than 5 million jobs from humans over the next five years,” says Ben Rossi, contributor for Information Age. “Machine learning has undoubtedly earned its place in the workforce, but machines don’t necessarily have to replace humans — they can in fact enhance the work humans can do.”
Rossi goes on to explain how big data is actually paving the way for better jobs. It handles the grunt work that humans were once required to do and makes it possible for people to fill or grow into better positions, so that big data is a good thing for the job front, not a negative factor.