Technical Article => Big Data => Big Data

Today technology has come to play a big role in how things are managed and run. In almost all spheres and extents of life. Imagine trying to find the best Italian restaurant, somewhere in the US and within a mile radius of your location. If you have a smart phone and internet connectivity, all it would take a few taps on the device screen. The real purpose of big data is situational awareness, powered by a wide database of information and statistics. It has the potential to impact the profitability, efficiency and effectiveness of any enterprise that relies on documented information. It’s like having the whole world as a team and using all the information to challenge and solve the most complex problems.





How can Big Data make the unusual into usual?



When we say that big data has the potential to turn the impossible into something easy, what do we mean? Here have always been systems and programs that have been able to understand and react to circumstances and even take care of normal exceptions. However, they fail when trying to understand events that occur very rare – probably once every year. Putting the exact location of such an event into context, the timeline widens further. It becomes impossible to say when the event will occur next for the same location. It is like understanding something you have never seen.



Big Data can, however, be useful here and utilized by programs and machines to address all kinds of events from all types of locations while putting the whole practice of processing information into an organized flow. So even if the ‘event’ occurs once in a decade for a location, Big Data has the data for all kinds of similar events from across the globe and will be able to effectively use the data to serve a purpose. It’s about taking the ‘bigger view’ to resolve very local and ‘smaller’ problems. This always involves a grueling filtering process that reduces the analysis to meet the requirements of a smaller data set. For example, NASA satellites and other space equipment generate petabytes of data daily but to judge much smaller but important events.



Big Data can be better understood under the following heads:



Goal: The goal is vague and can be about several events. There isn’t a way to exactly specify what the Big Data structure will contain as it holds various types of information that can be expected to be organized, analyzed and connected to data resources.



Location: The reach of Big Data typically spreads across the electronic space and can be located anywhere in the globe.



Content and data structure: Big Data can understand both structured and unstructured data including images, sound recordings, statistics, videos, feedbacks, customer reviews/ratings, traffic across multiple disciplines.



Preparation: Since data comes from several different sources and prepared by millions of people. However, people who use the data are rarely the ones who have prepared it.



Longevity: Big Data databases are stored perpetually and will be continuously absorbed between resources. Big Data projects thus extend both in the past and the future making the data both retrospective and prospective.



Data Measurement: Several types of data are delivered in several formats and the measurements will take up different protocols. This consequently makes verification of big data quality one of the most challenging jobs for data managers.



Reproducibility: While replication is seldom feasible, data managers can always expect low-quality Big Data to be flagged.



The stakes: Irrespective of the event, Big Data projects are almost obscenely expensive and consequently failed projects will easily lead to institutional collapse, bankruptcy or even disintegration of resources. However, each failed effort also adds to the intellectual remnants!



Introspections: If in any case, a big data resource has been ill-designed, the content can become inscrutable. The information can be achieved through techniques called introspections.



Analysis: Although a few exceptions, like the ones conducted through supercomputers, big data usually takes incremental steps. The data is always reviewed, extracted, transformed, normalized, interpreted, visualized and reanalyzed through different methods.





Big Data’s ability to identify the unusual and uncommon thus proves critical in events such as cyber security, weather forecasting, sales analysis and similar things, with each becoming more simplified and comprehensible to understand. Big Data starts by helping realize what is normal (normal isn’t a constant thing again) and then filter out the unusual. On the other hand, alternative solutions that look to consider a narrower data set are less capable of identifying and understanding threats.



The idea applies to all sectors, including medicine, manufacturing, and science. You might want to test some drug indicator that affects a few in a billion or understand what type of cloud formation and pressure situation will likely cause a storm over Mexico.



Big Data thus becomes quite the unusual from what we achieved from technology before. It is about being automatically be able to identify and react to both adverse and profiting situations and achieve a more calculated and accurate forecast – be it in archeological surveys or the stock market or in anything else. With situational awareness and analytical ability present, we humans would be able to stand guard against a lot of adverse situations and harmful things ranging from cyber attacks to wars, famines, storms, political failure, and everything else that badly affects a community, institution or individual.



Big Data thus truly defines the term globalization, wherein participants from every corner of the globe help to come to an agreement and come to a solution for a pressing situation. The good thing is that it is not just about data generated by human activity but everything around us, even the winds and the clouds. Big Data automatically makes the future more convenient for everyone by allowing more informed decision making by data scientists, analytics professionals, predictive modelers and artificial intelligence. It is a feat of mankind that will define the fate of this planet and help us prepare better for the future.

Author Bio

Vipin Jain is the Co-Founder & CEO of Konstant Infosolutions, a leading mobile application development company India. Having finesse in marketing and business administration, he is involved in Research & Development and Project Management in the company. He has a passion in penning down his technical and business intellect.