Implications of Big Data for Society

Big data helps organisations and also policy makers to develop answers to society’s issues and also to help aid social change e.g monitoring climate changes such as natural disasters to social welfare programs and humanity aiding projects etc. An implication of it would be the cost of implementing big data solutions which makes it challenging for some businesses. Also because it can be biased it means that it can be discriminatory towards certain people of society which is primarily true in the healthcare and financial systems, biased data sets leads to biased results.

https://www.harvardonline.harvard.edu/blog/pros-cons-big-data#:~:text=From%20monitoring%20climate%20trends%20and,and%20drive%20positive%20social%20change.

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Limitations of Predictive Analysis

One of the limitations is data quality, the predictive models rely entirely on large and accurate datasets to make realistic predictions. So if the data is inaccurate, biased or incomplete then the model will be flawed. The next is overfitting which happens when a model is trained on a particular dataset and becomes too complicated makes it difficult to generalise new data. Because of this it can make inaccurate predictions and poor performance. Next is changing conditions, predictive analytics models are made to predict future outcomes based on previous data. However, the future is very uncertain and because of this the conditions can change quickly so it can be very difficult to predict accurately. And lastly ethical concerns can make it difficult to predict analysis because of ethical concerns such as bias or privacy. Predictive models can have an already existing bias and discrimination. Also predictive analytics can have privacy concerns...

Data Mining Methods

Data mining is the process of using statistical analysis and machine learning to reveal hidden patterns or odd things in large datasets because of this you can help make important decisions and predict what’s going to happen. it’s where you take data such as structured data, an image, video, text etc and train it, deploy and serve it then you can get actionable insights and application events. The techniques are classification which is used to organise data into different classes or categories so it trains a model on labelled data and uses it to predict the class. Regression is used to predict numbers or continuous values based on relationships so it finds the function or model that best fits the data to make accurate predictions. Clustering is used to group similar data uncover patterns or structures in the data without any classes or labels. There is also association rule, anomaly detention, time series analysis, neural networks, decision trees, ensemble methods and ...

Future Applications of Big Data

There’s several ways in which the Big data in future applications some of which are: The increasing volume of big data analytics which in the future will focus on the freshness of the data with real time analysis which helps to make better decisions which have more information and additionally increase competition. It also is essential for gaining real life vision however can have some implications because of higher risk of having inaccurate or incomplete data. E.g ‘Snowflake’ announced ‘Snowpipe’ they republished their Kafka connector which made it so that when data lands in Snowflake it is immediately questioned which results in a 10x lower latency. Being able to see the data that’s happening right this second so things like trading bitcoin etc. Because of this is that it has shaken up industries such as finance and social media. For example Walmart has created one of the largest hybrid clouds to help manage their supply chains and analyse sales n that moment and not a eee...

Annabelle McCulloch

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