Most people have become aware of the fact that everything they do online and on their phones leaves a cyber breadcrumb trail, where your internet searches and activities are logged. That is because all of these interactions that people engage in with technology every single day generate data points, thus creating a cyber breadcrumb trail that can be analyzed as a unit of data. As technology continues to play ever more important roles in our daily lives, it only makes sense that the amount of data that is being created will continue to increase. The sheer quantity of data that exists out there is getting bigger, and as follow, big data metrics and analytics are becoming more and more important.
Big data means the study, analysis, and application of data sets that are so large, complex and just plain big, that the traditional data-processing application software cannot handle it, thus the name “big data”. As previously mentioned, in this modern day and age, data is created every single day and exists pretty much everywhere. However, unstructured data does little to prove any point or highlight a pattern- the data has to be organized and compiled into a form that makes sense. Big data gathers data from sources such as log files, devices, sensors, web, social media, and turns that information into data sets that can be captured, analyzed and managed. Big data is so important because it can be used to highlight different patterns in regards to human behaviors and interactions with technology. Big data is different from traditional form of data analytics because the data that falls into the big data category has one or more of the following characteristics: high volume of the data, high speed(velocity) at which that data is created and high variety of the type of data that is being created.
Big data allows researchers, businesses and data analysts to use data that was previously not being utilized and gives them the ability to use it in a new way. Big data can be used in conjunction with previously collected data sets to help businesses make strategic decisions. Implementing big data can help researchers and businesses determine root causes of pain points, identify areas of opportunity or improvement, and help influence predictive analytics. Since big data is able to use data that has not been traditionally accounted for, big data can also offer insight to hidden or unseen information, patterns and correlations.
While big data can be an extremely useful business tool, it is important to always remember the importance and value of human insight. It is not time to relinquish all our power to our robot overlords just yet. Like all new technological innovations, there are still some technical issues, or bugs, present in regards to the use of big data. While sometimes there are technical failures that are just out of our control, it is important to recognize the role that we as humans play in generating quality data. For example, if the data is input in an inconsistent or confusing manner, it will inevitably result in a faulty output. It is important to verify the input and the output to make sure that both make sense. By double checking the output, one can also determine if the data makes sense given the context and if the model is the right model to convey the data.
The analysis and use of big data sets can be analyzed to reveal insightful patterns and trends that can help to inform strategic business moves, as well as help businesses make predictions towards future trends. Since big data encapsulates a large amount and variety of data, it can generate insights that might have gone previously unnoticed or otherwise remain hidden. While big data can be an extremely valuable business tool, it is important to remember that big data is not a magic cure-all; we as researchers, analysts and businesses must be critical to making sure that big data is utilized in the most efficient and accurate way possible.
READ MORE: Why Competitive Intelligence (CI) Matters, What is Datafication?, Smart Technology In Our Daily Lives, 5 Commonly Used Metrics In User Research
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