HUMANIZING BIG DATA FOR EVERYDAY LIVES
In the simplest sense, we have collected data all the time – whether it was a poll among friends or for a path-breaking survey to understand customers! But the most important use of data is only being realised recently once the data scientists got to work. Today we handle huge amounts of disparate data and we have also started to make sense of it by using it to help every aspect of business and everyday life. We are sorting through mountains of data, scrubbing it clean, dealing with gaps, finding connections and sifting through incomplete descriptions – as we try to make the data work. Another aspect that data science has brought in is that we’re seeing that new elements of behaviour are affecting data, such as politics, opinions, and agents interacting with and influencing each other. Synergies have set in to create complex structures for scientific data analysis. With so many influencing factors how are data scientists really handling this new twist that is making data more human?
Technology has driven every major economic disruption in the last 150 years and today that same transformational impact will be brought about by data and data scientists. In the digital age, a whole new aspect known as crowd science is everywhere, and it is shaping user experiences in online platforms and aiding in the reorganization of governments via political revolutions. Amazon, Twitter and Facebook are prime examples of social and political trend: influencing channels. From the Arab Spring to new product launches, these platforms are data distributors that are causing historical changes in the way information is spread and processed.
Great data scientists are generally surrounded by a smorgasbord of information but they never just rely on information alone. They also add their own experiences because great data scientists also collect original data from live situations such as retail floors, sales desks, shop floors, etc. They combine the information with the data available to get the best conclusions. Their deep curiosity drives them to find practical and real world situations, instead of being just stuck behind a desk. So, for all of you who thought being a data scientists meant spreadsheets and analytics, you should know that it takes more than just those tools to make your data work for you. This human influence that data scientists bring to the table determines how data affects economic, political and social scenarios. The goal of data analysis is to solve real world problems hence it should be viewed in perspective and not alone.
AI is playing a big part in taking data science to the next level. AI brings with it two broad advantages namely, perception and cognition. These combined helps in reducing error in processes, for example a study by the Stanford computer scientist James Landay and colleagues found that speech recognition is now about three times as fast, on average, as typing on a cell phone. The error rate, once 8.5%, has dropped to 4.9%. What’s striking is that this substantial improvement has come not over the past 10 years but just since the summer of 2016. With more such improvements, AI problem solving ability combined with big data is resulting in machines that have beaten the best human poker players. Google’s DeepMind for server management and Deep Instinct for cyber security are intelligent agents today who use the power of data. Machine Learning (ML) has become a big part of Wall Street and the machines are learning through data science techniques and giving their users better profits.
Data science is a broad spectrum of activities that are embedded in reality and once you understand that you will always be excited to work with data. Technology helps you only to a certain extent, the real use of data comes when you treat it as an influencing tool considering the effect it might have on human situations.