James Dick Big Data Blog

Data Mining is the practice of deriving information from large amounts of existing data. This can be done in a variety of ways. One of these days is by creation Associations between sets of data. This is a common practice in retail as drawing connections between customer purchases allows businesses to utilise these behaviours in order to encourage more purchases by placing associated items near one another, or offering deals. Classification is another Data Mining practice wherein common factors can be identified in order to group entities together and apply predictions on one such entry across the broader category. This was touched upon briefly in an earlier blog, in reference to how recruitment can be aided by identifying common factors among successful applicants. For more information you can read here: https://datafloq.com/read/5-major-data-mining-techniques-being-used-big-data/3352

There are a lot of problems where Big Data Analysis can provide a solution. One such area is in logistics. Mechanical failures of delivery vehicles represents a problem as it costs money to repair the vehicles, and this wastes the time of both the company and the customer who receive their items late. Big Data Analysis allows logistics companies to take preventative measures by monitoring all of their vehicles and identifying when their components are likely to fail, and by doing so they are able to ensure that their components are replaced at the optimum time for maintaining time and cost efficiency. For other examples of problems Big Data can solve, read here: https://www.forbes.com/sites/gregsatell/2013/12/03/yes-big-data-can-solve-real-world-problems/#416d707a8896

As mentioned earlier when discussing Big Data's implications for individuals, there is a high potential risk to personal privacy associated with the increase in personal data being stored about us everywhere we go. This cumulative data profile can hold a dangerous amount of personal information, and as such it is important to demonstrate responsibility with the data that is handled. In order to protect the individual from the malicious exploitation of their data, it is common practice to attempt to anonymise stored personal data in order to ensure that this can be used for things like pattern recognition and inferential analytics without exposing the individual to undue risk of their data being compromised. However, this is often insufficient as correlation can still happen by comparing multiple data sources and identification of individuals can still be done. You can read more here: https://www.ft.com/content/105e30a4-2549-11e3-b349-00144feab7de

As mass adoption of Big Data continues, the potential societal implications are massive, and may extend far beyond the understanding of the average individual. Big Data has the potential to change the way our world functions, not in the least in our economy. Across the world, in areas like health care, administration and IT, Big Data has the potential to increase value and spending in the hundreds of billions, as well as creating a significant amount of jobs in analysis. You can read more here: https://marksbigdatablog.blogspot.com/2019/06/implications-of-big-data-for-society.html

As individuals, we of course benefit from the Societal and Scientific Big Data applications that were discussed earlier. On a solely individual basis it can be left up to interpretation whether the tailored content that is presented to us in search engines and advertisements is a good thing or a bad thing. Some people certainly are not thrilled that their data is being used in order to frame the content that is displayed to them, or to sell them things. The fact that our data can be used in ways that we may not be comfortable with, without our express consent is one major disadvantage of the onset of Big Data. It has significant implications for privacy and personal security, as our personal information is being collected, and if this information is compromised it could be a big problem. 

One limitation of Predictive Analytics is the current inability to account for human decision making, even with a substantial data set to work with. In the last Presidential Election in the United States, a variety of analytics sources placed a 70% probability that the outcome of the election would be in favour of Hillary Clinton. While concluding that the 70% chance was incorrect is somewhat of a misunderstanding of probability, the consistency with which similar predictions were made indicates that there are factors that predictive analytics can not yet comprehend, and as such it should not be relied upon solely in matters such as these. You can read more about this here: https://www.dataversity.net/limitations-predictive-analytics-lessons-data-scientists/#