Big Data is the basis of today’s advanced technologies. It covers the whole gamut of science: astronomy to space exploration. Today’s technologies help us collect and analyze massive amounts of data in a fraction of the time it would take us to do the same thing using traditional data analysis methods. Big Data has even made its way into the lives and businesses of everyday individuals through the use of applications like Twitter and Facebook. These applications make it possible for people to interact with each other on a much more intimate scale.
This new generation of data is incredibly voluminous, which is why new software and hardware are required to process all of this data. We no longer live in a world where IT departments deal with storing, managing and maintaining computers; we now deal with data. With so many applications and devices are running on the Internet, we literally have billions of possible sources for data.
This wealth of information provides an unprecedented opportunity for organizations to improve their internal processes, product designs, marketing strategies and operational procedures. Big Data is used not only by the consumer marketer but also by every major corporation in the business sector. It has revolutionized how business can be conducted today. However, this ‘big data’ is not without its own constraints. The information that is available today is massive, messy and inefficient when used in conventional data collection and analysis.
Today, data has become both a threat and a blessing. On one hand, it allows us to quickly measure and control elements of organizational performance. It is also a means of obtaining and providing general information about anything and everything from product lifecycles to customer relationships. Data has changed the way we communicate, conduct business, build networks, manage risk, and make decisions in nearly every industry and sector.
As opposed to previous generations, however, this data is now too pervasive and diffuse to be effectively used for traditional metrics or in isolation. Instead of being stored, processed and analyzed alone, the bulk of this data is scattered, making it inefficient and difficult to analyze. Moreover, the majority of the data currently available only represents a portion of what is actually available to users. Despite this problem, it has led to the increased focus on big data solutions in the business context. Organizations today are leveraging data in multiple ways in order to improve operations, productivity and product design.
Big Data is not a concept that was exclusive to the corporate realm until recently. In fact, the development of the Internet itself generated massive amounts of data. Today, thanks to advances in computing, large petabytes of data are being stored on servers all over the world. Thanks to advances in satellite technology and other forms of geospatial communications, the volume of this global data is growing exponentially, allowing researchers to mine it for new insights into virtually every aspect of life.
However, even after the volume of data generated from satellites and other space based technologies has grown, it is still limited by the speed of light. While this limitation means we can’t simply send billions of emails or post items to Facebook within the same amount of time, it does mean we don’t have access to all of the big data tools that we have at our disposal today. We’re limited by bandwidth and by storage space. So how do we make the most of what we have?
The answer is to exploit the power of big data analytics. Today’s IT organizations are quickly discovering that the best way to leverage the collective power of this raw material is to use it as part of a bigger strategy. Rather than simply sending a few emails, a group of IT professionals can use big data analytics to determine the health of a manufacturing plant. They can determine the number of faulty pixels on a printer or the speed with which it runs. This kind of analytics can help companies cut expenses today and regain productivity tomorrow.