Annabelle McCulloch

 By analysing the data it can help to make useful decisions such as: Tracking customer spending habits - see what they’ve previously bought to help to help to advertise things toward that customer that they are more likely to buy. E.g Amazon, banks etc.  Smart traffic system - GPS to show you what way has speed cameras, traffic etc. additionally for pilots to see what the weather conditions are gonna be like e.g speed, moisture, temperature etc. lastly self driving cars.  Virtual personal assistant tool such as Siri (Apple), Alexa (Amazon) etc. which tracks the location so they can tell them the local time, season, weather, news or any other question asked by analysing all data it provides an accurate and fast answer.  https://www.geeksforgeeks.org/applications-of-big-data/

Big data visualisation refers to the techniques and tools used to present the large and complex data into such a way that it’s easy to read and understand. Some examples include: Heat maps which is used to show the amount of data points or activities across different regions and categories.   Network diagrams helps to visualise relationships and interactions in data for example social connections/data flows etc.  Geospatial maps show the mass amount of geographical data with traditional data sets to provide spread apart analysis.  Stream graphs show trends and patterns across loads of different categories and regions.   Same for the parallel coordinates which also show patterns but also correlations across numerous different variable.  Chord diagrams help to identify clusters, patterns, trends etc to help with making intense decisions and also helps with analysis etc.  https://www.geeksforgeeks.org/what-is-big-data-visualization/#what-is-big-data-visualizat...

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 ...

 One of the greatest challenges is storage with insane amounts of data generated everyday. Also because Unstructured data can’t be stored in traditional databases.  Processing big data is also a problem which refers to reading, analysing etc or useful information from raw information because of this the changing from all this data to finding all the useful parts is very challenging.  Security is another problem because non-encrypted info is more likely to be stolen or damaged which makes it such a large concern for organisations.   https://www.simplilearn.com/challenges-of-big-data-article#:~:text=Storage,be%20stored%20in%20traditional%20databases.

Making sure that biased data is not used such as making sure it’s accurate and also up to date can help to limit the bias that is seen through but data especially for the healthcare and financial systems.  Large businesses could think about using more modern techniques and tools to help deal with the large mass of data sets, this gets rid of duplication of data. Also they could additionally use machine learning technology, improve its development and marketing strategies etc.  Changes in the Regulatory environment means that they should keep up to date with data privacy needs  so they’re not storing information they don’t need. This will save them money and time because they won’t have to pay fines for holding unnecessary data. Also because they are ahead of other businesses then they can use this leverage for competitive advantage.  https://kms-solutions.asia/blogs/top-challenges-related-to-big-data-and-how-to-overcome-them

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.

Collecting and analysing massive velocity of data increases the risk of unauthorised access, data leaks or cyber attacks because of this it creates privacy and security risks for individuals. For individuals this means more risk and fear of technology. However Data privacy and technology explores the risks and helps to offer solutions and strategies to manage data responsibility.  https://www.harvardonline.harvard.edu/blog/pros-cons-big-data#:~:text=Collecting%20and%20analyzing%20large%20volumes,strategies%20for%20managing%20data%20responsibly.

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...

 The big data process requires clustered systems which provide required computing power. Some steps that businesses use is that it defines business objectives so include stockholders from the start, data scientists employed etc, identify data sources so figure out what format you want your data to reside with and begin the process of sorting the data and analysing it, identify and prioritise meaning to keep, hold and use information as it is needed and agreed upon and nothing more and lastly to formulate a big data roadmap so seeing any missing pieces and gaps surrounding your data.  https://www.techtarget.com/searchdatamanagement/definition/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...

 One way Big Data is used in society is through the government, the US government use ID photos, fingerprints, phone activity etc which they store using big data. The US immigration and Customs Enforcement has used facial recognition technology to go through drivers licence photo databases to spot any illegal immigrants with the hope of finding them and having them deported which is put through large amounts of data to track down on the people they’re searching for.  Big data is used for things such as video games  one way is to improve them so that more people can enjoy them ‘Activision Blizzard’ uses big data one example is the business uses machine learning to detect power boosting to track key indicators for increasing quality of game time.  The rise of social media and networks meant that the way that people socialise has drastically changed because of this relationships have also changed and developed into the digital age some examples of this are ‘Tinder’ uses...

 Healthcare - Data science used the data to identify and predict disease to personalise any healthcare recommendations. In 2008 Google created ‘Google Flu Trends’ which helped to map flu outbreaks by tracking location data on confirmed cases of the flu. But unfortunately it wasn’t successful. But it still showed the importance of data science in healthcare. Other examples are that Google developed a tool called LYNA that was used to identify breast cancer tumours because they can be difficult for people to see with just their eyes, this successfully identified nodes 99% of the time however it needs further testing before being able to be used by doctors in hospitals. Tracking periods by using apps such as ‘Clue’ to predict users menstrual cycles and also reproductive health by using things such as last start date, moods, stool type, hair condition and other different metrics.  Sports - ‘Strong Analytics’ uses pre recorded footage to create detailed plan to help coaches to impr...