Data is generated everywhere, all the time around us. Every second, there are 40,000 searches on google which totals to 3.5 million searches every day and 1.2 trillion searches every year. Now that is, an amount worth thinking about. But what is done with such humungous amounts of data? Herein comes the importance of Big Data, a term that is very commonly used today all over the world.
What is Big Data?
Big Data refers to a set of data, very large and complex. Due to their extreme complexity, traditional data processing applications are inadequate to deal with them. Hence, the data are processed by using various analytical tools like predictive analytics, user behavior analytics, etc. The data is analyzed to find correlations and meaningful insights to help businesses take the right decision.
The analyzed result from big data can be used by anyone – scientists, business executives, medical practitioners, advertisers or even the Government.
Applications of Big Data
Big Data and its analysis are widely used by information management specialists like IBM, Microsoft, Oracle Corporation, HP, etc. The industry was worth more than $100 billion in the year 2010 and has been growing at a rate of 10% every year since then. Several of the big tech honchos of the world have developed their customized data analysis tools which help companies solve various problems at hand, empowering them to take informed business decisions.
Analysis of Big Data
Analysis of Big Data is actually the most important aspect to consider with regards to Big Data. If the data are not analyzed correctly, the very idea of gaining something meaningful out of the data will be lost in the labyrinth of massive amounts of data.
Three types of analysis are considered to be of top notch importance in this regards. We have explained each of them in details along with a use case of the same:
Predictive analytics, as the name suggests, is a branch of analytics, which strives to make predictions about the future. It extracts information from big data to find out patterns thus enabling users to make predictions about future outcomes and trends. However, the most important thing to note here is that it does not forecast what will happen in future. Instead, it forecasts the probability of an event happening in the future based on what-if analysis and risk assessment. Predictive Analytics helps businesses change the focus from a historical perspective of a forward-looking view.
Predictive Analytics are used widely in various functional areas. It has been effectively used for patient safety and care in ICU in hospitals. In ICU, the most critical patients have a low immunity and are prone to sudden infections. Predicting this sudden infection beforehand to take remedial measures has been a centre of research for a long time. However, the best use of predictive analytics has been made at the University of California Davis and Massachusetts General Hospital. At the University of California-Davis, the routinely collected HER data is analysed to get early warnings of sepsis, a disease which has a 40% mortality rate. The Massachusetts General Hospital, on the other hand, uses an analytics system called QPID where all the critical patient data are recorded while admission and all through treatment. The system then throws up various indicators and results in a dashboard, enabling the physicians to take critical decisions with regard to treatment.
This is the second way of analysing big data which are very popular. Descriptive analytics, as the name suggests, describes a set of data to help analysers interpret the same. This analysis describes the past to understand historical behavior that might influence future outcomes. Most statistical tools that are used commonly fall in this category. It is a very useful tool to show things like stock inventory, customer behaviour, sales predictions, etc.
Descriptive analytics are used widely by social media sites, especially Facebook. Every second, there are millions of posts, mentions, fans, page view, check-ins, etc are generated. It is not only difficult but is pointless to list all of them. However, the data are analyzed to find out the behaviour of each user which in turn is used for making a targeted advertisement to these users.
The third and the final analytics method is Prescriptive Analytics. Prescriptive Analytics find the answer to the question why will something happen in the future. Therefore, prescriptive analytics help take actions and mitigate actions in the present to prevent an even from happening in future. It also takes new data and re-predicts and re-prescribe actions to take remedial measures. Prescriptive Analytics can process hybrid data to predict what lies in the future.
Prescriptive Analytics is widely used in oil and gas exploration. The predictive analytics software predicts production and prescribe various kinds of configurations which can help in drilling and production at an optimal level. It can help in equipment maintenance by suggesting methods to optimize configuration and prevent unplanned downtime. Predictive analytics can also study market trends and therefore predict the price fluctuation of oil and gas.
While these three types of analytics are widely used in different sectors of life, they are not free from challenges. It is often argued that predictive analytics is unable to handle extremely complex data, making the results inaccurate in many cases. Descriptive analytics, on the other hand, depends on the human interpretation of data. It also fails to provide techniques to facilitate the understanding of the analysed results. Hence, descriptive analytics have a scope of human error inbuilt in its core. Finally, prescriptive analytics are often criticised for the time it takes to predict an event in present. In many cases, an event needs to be predicted at real time to take remedial measures. The crucial time lost while conducting prescriptive analytics sometimes fails to provide results at the right time, thus failing to prevent an event from happening.
While these analytics tools facilitate decision making, they will never replace human judgment. At the end, the success of an analysis depends on how well a decision is taken based on the analysis results. Hence, human intervention and judgment will continue to remain a crucial factor in big data analytics.