The future is predictable!?
image source :http://steadman.io/blog/2013/01/06/25-quintillion-bytes-of-data-every-day/
Every single move of us in the digital world can turn into a datum. Everyday, we create 2.5 quintillion bytes of data, which equal to 2,500,000,000,000,000,000,000,000,000,000 bytes! This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. (IBM, 2014)
The value of Big Data is not in the data itself. The value comes from the highly useful and actionable insights powered by custom algorithm development, efficiently processing huge data sets, machine learning and data visualisation. (DiUS, 2014) The process of machines learning from data unleashes the power of this exploding resource. It uncovers what drives people and the actions they take-what makes us tick and how the world works. With the new knowledge gained, prediction is possible. (Siegel, 2013)
Using data to make prediction was not a new idea. Company use their past sale records to predict their benefit in the coming season. The prediction will affect the budget setting for different expense such as media fee and the rise of salary of employees. Government conduct census and use the result to predict the country’s/ city’s growth, hence to decide policy or budget for health care, education, public service and infrastructure etc. Those predictions are all about probability and statistic. The result will be more accurate when the sample size is closer to the universe and more factors are effectively considered. In the other words, the more relevant data fed for machine leaning the more accurate the prediction is. Therefore, the accuracy of prediction are advanced by the growth of technology in big data usage.
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Organizations can have more accurate prediction due the 4Vs of big data technology: volume, velocity, variety and veracity. Volume refers to the vast amounts of data generated every second (Linkedin, 2014). As mentioned, the more relevant data used the more accurate the prediction is. The big data technology provide affordances to store and use these data sets with the help of distributed systems, where parts of the data is stored in different locations and brought together by software. Velocity refers to the speed at which new data is generated and the speed at which data moves around (Linkedin, 2014). Data is meaningless unless we are possible to analysis it and conclude the result before the data is ‘outdated’. Big data technology allows us now to analyse the data while it is being generated, without ever putting it into databases. Variety refers to the different types of data we can now use (Linkedin, 2014). As I said in the last paragraph, the more factors are effectively considered the more accurate the result will be. So, variety of types of data are under consider the prediction will be more reliable. Veracity refers to the messiness or trustworthiness of the data (Linkedin, 2014). 1 in 3 business leader don’t trust the information they use to make decision (IBM, 2014). Even big data can predict the future by statistic calculation, however, many forms of big data, quality and accuracy are less controllable. Big Data service provider like IBM, Linkedin, DiUS work to help keep data clean and processes to keep ‘dirty data’ from accumulating in systems.
image source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
Reference:
DiUS. 2014. “predictive analytics”. Accessed May 10, 2014. http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/
IBM. 2014. “Big data at the speed of business”. Accessed May 10, 2014. http://www-01.ibm.com/software/data/bigdata/
LinkedIn. “Big Data: The 5 Vs Everyone Must Know”. Accessed May 10, 2014. http://www.linkedin.com/today/post/article/20140306073407-64875646-big-data-the-5-vs-everyone-must-know
Siegel, Eric. 2013. “Introduction: The Prediction Effect”. In Predictive Analytics: The power to predict who will click, buy, lie or die, edited by Eric Siegel, 1 – 16. Hoboken: Wiley. Accessed May 10, 2014. https://qutvirtual3.qut.edu.au/qv/olt_material_search_p?p_unit_code=KCB206














