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Finding Content: Big Data vs. Smart Data – There is a Difference
There is no debate: big data is here to stay, especially in research. With the ability to collect high amounts of data with lightning speed, it is no surprise Big Data is the new buzzword within many industries, especially in science and technology.
Don’t believe me? Check out this quote plucked out of CEO and Chairman of IBM Ginni Rometty’s commencement address to the graduating class of Rensselaer Polytechnic Institute, where 3/4s of the students graduating are planning to enter the field of science and technology:
“As the graduating Class of 2014, you stand at an important inflection point. You’re facing a new age being made possible by a new natural resource – data. I would assert that what steam was to the 18th century, electricity to the 19th, and hydrocarbons to the 20th, data will be to the 21st.”
So, How Important is Big Data to Researchers?
As big data becomes ubiquitous in our day-to-day, it will grow increasingly more important for researchers looking at behavior and trends across every field. But the true value in big data actually lies in what you’re able to do with – not just your ability to collect it – and with billions upon billions of data points, that can be a daunting task. You have to be smart about how you identify, target and structure your database queries so that you save yourself time and focus on the research question you have in mind. Thus, you must begin thinking about the difference between big data, and smart data.
Big data alludes to the accumulation and collection of large amounts of data, allowing researchers to have a larger spread of a population. That is great for researchers. Researchers are able to collect vast amounts of data from large demographics in a shorter period of time, allowing their conclusions more accurate. But sorting through and Harnessing the Power of Big Data is no small task and many researchers find that big data asks more questions than it answers.
That is where smart data takes over. Smart data is knowing where/how your data fits in with the people behind the numbers and having the creativity to tell a story from it. It also tells you what you should be focusing on and how to run you analysis. There is a tendency to think “the more the merrier…” but in the case of data, that is wrong. Be a reductionist. Throw out data sets or nodes that don’t speak directly to your research. This will save you lots of time and keep you from getting over-inundated with data.
As Joe Rospars, CEO and co-founder of Blue State Digital, the agency behind President Obama’s campaigns notably said, “harnessing big data is not just about analyzing antiseptic information, it’s about using whatever information is at your disposal to understand the people behind it all.”
So…What Does This Have To Do With Researchers Again?
Since big data has been such a buzzword for the scientific community, data-driven research has been more important than ever, with the collection of data growing at 50% a year, as estimated by IDC, a technology research firm. Many people poking around your research will be expecting a large sample size along with the conclusions you arrived from interpreting that data. Researchers need to have a structure in their minds and a point of emphasis before even looking at the data collected. Thus, not only do researchers need to be skilled in analytics, but they need to be familiar with methodologies and tools for breaking down those analytics, so they can tell a story and paint a complete picture. Simply put: to be a successful researcher in the 21st century, you need to have a clearly defined approach and understanding of not just big data sets, but how to manipulate, adjust and extrapolate from them. Click here for an article on Turning Big Data into Smart Data.
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