Big data can help shape society by finding out the opinion of the population to see which policies are preferred by the majority. This can influence government decisions and change the way the world works.
Medicine and healthcare could possibly be improved with the use of big data because looking at past medicine results and how well things worked can help predict what will work best for future patients. This may increase the populations age as people will likely live longer.
The rate of crime may be lowered because of big data analytics we would have a better understanding of what makes crime more likely to happen therefore allowing us to avoid crime. This will result in a safer society to live in with potentially lower death rate.
Even though big data may be overall good for society, these improvements could negatively affect some peoples lives as individuals. For example if crime rates were lower then a lot of police jobs may be cut, leaving lots of people without a job and this could impact the economy.
People may not like the fact that they are being shown tailored advertisements due to big data as they could feel like they are being spied on by companies and manipulated for their money.
Lots of people want to stay private online and with big data advances this is now very difficult to be private. There may be a lack of privacy in society because of big data.
Tuesday, June 11, 2019
Personal Implications on the Use of Big Data
Advantages/ Disadvantages
The main advantage of big data for you personally is that you will see more information that is related to you and your interests based on previous browsing history and searches. You may enjoy seeing content which is more relevant for you rather than unrelated content which was not based on your data.
A disadvantage of big data being used for you is that your personal data is stored and may be shared to third parties which you did not know had your information and you may not consent to having the data shared to these people. The information can be used without your consent and companies can use knowledge about you to manipulate you eg. by showing you tailored ads for a specific agenda they have.
The main advantage of big data for you personally is that you will see more information that is related to you and your interests based on previous browsing history and searches. You may enjoy seeing content which is more relevant for you rather than unrelated content which was not based on your data.
A disadvantage of big data being used for you is that your personal data is stored and may be shared to third parties which you did not know had your information and you may not consent to having the data shared to these people. The information can be used without your consent and companies can use knowledge about you to manipulate you eg. by showing you tailored ads for a specific agenda they have.
Wednesday, June 5, 2019
Strategies for Limiting Negative Effects of Big Data
Negative effect - Your personal information being shared
How this can be limited - You can limit the amount of personal information you post online to protect your data by making it unavailable to anyone. You can be aware of what information is stored about you this could be done by reading the terms and conditions and privacy statement of companies and websites. Companies should be transparent with the users in what they store and how they use the personal information.
Implementing rules of use of big data - there could be stricter laws made in how big data should be handled by organisations and companies. It should be made very clear what is being stored about people and how it is being used. People should be able to opt in or opt out of personal data storage by companies. The most important way to limit the negative effects of big data is for companies to be transparent about how they use data.
How this can be limited - You can limit the amount of personal information you post online to protect your data by making it unavailable to anyone. You can be aware of what information is stored about you this could be done by reading the terms and conditions and privacy statement of companies and websites. Companies should be transparent with the users in what they store and how they use the personal information.
Implementing rules of use of big data - there could be stricter laws made in how big data should be handled by organisations and companies. It should be made very clear what is being stored about people and how it is being used. People should be able to opt in or opt out of personal data storage by companies. The most important way to limit the negative effects of big data is for companies to be transparent about how they use data.
Limitations of Predictive Analytics
why don't we use big data all the time?
Incorrect data:
Most of big data is unstructured and unstructured data is difficult to make sense of. It is very likely the system will not be complex enough to understand it fully. It may be interpreted wrongly resulting in nonsensical output or it may contain incorrectly spelled words or grammatically incorrect sentences which we cannot use easily.
Incorrect/missing out data:
Another problem is that one person out of the data set may have recorded information incorrectly and so it is not a true data set therefore making analysis of the incorrect data meaningless for real life. Data produced from people can be biased, this leads to inaccurate predictions. People may have been paid to give out certain information which can be incorrect e.g. sponsored content is often ingenuine and this can reduce the effectiveness of the predictions as we can't tell if the opinions were true or not.
Correct data:
Another thing that affects big data analytics is that an unknown third variable may come into why the data is the way it is. If we have a graph with two variables we are assuming that only those two variables will be affecting the result when in real life there are often multiple variables affecting an outcome. Predictive analytics cannot magically decide what the third variable is without knowing. There is almost always unexpected things we would not be able to predict. A major limitation of predictive analytics is that data cannot predict human feelings - human feelings often lead to the outcome. Human emotions can also lead to data being taken out of context which affects the reliability of the data and therefore can lead to inaccurate predictions.
Incorrect data:
Most of big data is unstructured and unstructured data is difficult to make sense of. It is very likely the system will not be complex enough to understand it fully. It may be interpreted wrongly resulting in nonsensical output or it may contain incorrectly spelled words or grammatically incorrect sentences which we cannot use easily.
Incorrect/missing out data:
Another problem is that one person out of the data set may have recorded information incorrectly and so it is not a true data set therefore making analysis of the incorrect data meaningless for real life. Data produced from people can be biased, this leads to inaccurate predictions. People may have been paid to give out certain information which can be incorrect e.g. sponsored content is often ingenuine and this can reduce the effectiveness of the predictions as we can't tell if the opinions were true or not.
Correct data:
Types of Visualisation of Big Data
A map is a good way to see visually where something is. It makes it very easy to be able to see how one place relates to another place and the distance between places.
A graph could be a line bar, scatter graph etc. It is good to use graphs to see trends and relationships. It is good to visualise numerical data.
Charts can be used an example of a chart is a pie chart. This is good to be able to visualise and quickly see things like percentages to see how something is divided.
A Table is a nice way to show structured information each row and column has a meaning and everything placed in the table has been placed in the correct categories. It looks more organised than scattered data.
A graph could be a line bar, scatter graph etc. It is good to use graphs to see trends and relationships. It is good to visualise numerical data.
Charts can be used an example of a chart is a pie chart. This is good to be able to visualise and quickly see things like percentages to see how something is divided.
A Table is a nice way to show structured information each row and column has a meaning and everything placed in the table has been placed in the correct categories. It looks more organised than scattered data.
Future Applications of Big Data - Machine Learning
What is machine learning - machine learning is when a computer can learn the same way humans do and this creates Artificial Intelligence (AI). They learn from past experience by saving data and looking at past data to help decide what to do in its current situation. If a computer has enough data it can look at how humans do and respond to things and then do it in the same way.
how is big data used with machine learning - intelligent computers would be able to make more accurate predictions from looking at big data and identify patterns
how is big data used with machine learning - intelligent computers would be able to make more accurate predictions from looking at big data and identify patterns
Applications of Big Data Use for Society
Changing public opinions for elections -
Big data can be used by the government to collect data about the opinions of the public. This knowledge can be used to then try to change the opinion of public by sending targeted messages to specific groups of people to manipulate what they think about certain topics. For example in 2016 US elections people who supported Clinton were shown targeted ads through means such as social media posts to try to sway their opinion to support trump instead. This type of knowledge about the opinions of public makes the government very powerful.
getting public opinions for policies
The government can collect opinions the public has about government policy. This allows the government to see what the public want, if they like the current policy and what they want to see changed. This can greatly influence government policy as they are put under pressure to change policies. For example the murder of a 6 year old by a 17 year old caused public outrage and then caused a change in policy by a judge.
Big data can be used by the government to collect data about the opinions of the public. This knowledge can be used to then try to change the opinion of public by sending targeted messages to specific groups of people to manipulate what they think about certain topics. For example in 2016 US elections people who supported Clinton were shown targeted ads through means such as social media posts to try to sway their opinion to support trump instead. This type of knowledge about the opinions of public makes the government very powerful.
getting public opinions for policies
The government can collect opinions the public has about government policy. This allows the government to see what the public want, if they like the current policy and what they want to see changed. This can greatly influence government policy as they are put under pressure to change policies. For example the murder of a 6 year old by a 17 year old caused public outrage and then caused a change in policy by a judge.
Future Applications of Big Data - Crime Prevention
Big data can be useful in stopping crimes as it can be used to predict who is more likely to commit crime and in which situation and circumstances a crime is likely to occur. This can therefore prevent crime as police could be more informed about if a crime is likely to occur and they can take precautions to prevent it from happening.
It is not completely reliable to use big data to stop crime as there are disadvantages - we can't solely use big data because we would just be making guesses as to which crimes are going to happen and we cannot be sure. Big data is also unlikely to remove unpredictability of crimes as many criminals come as a surprise. It may also put innocent people under suspicion wrongly if they appear to meet the criteria of an average criminal even though they are law abiding.
It is not completely reliable to use big data to stop crime as there are disadvantages - we can't solely use big data because we would just be making guesses as to which crimes are going to happen and we cannot be sure. Big data is also unlikely to remove unpredictability of crimes as many criminals come as a surprise. It may also put innocent people under suspicion wrongly if they appear to meet the criteria of an average criminal even though they are law abiding.
Applications of Big Data Use for Business
People opinions (on current/upcoming product)-
We can quickly gather opinions of products from a large group of people. This aids businesses in decision making for future as it can tweak the product to suit the opinions gathered from the public. This can lead to more sales as the product can be suited to the opinions of people, making them more likely to buy the product. Finding out people's opinions can help businesses understand what is popular in general.
Targeted Advertising - targeted advertisements are shown to people which are more likely to be relevant to the person. Allows person to slightly change what the data says so the person seeing it will like it. It is designed to try to make sure money for ads are being spent wisely. Shows you the products your most likely to be interested in so there's more chance you will engage with the ad and ultimately spend money. Business can purchase data so they know what you are more interested in so they can show specialised ads for you
We can quickly gather opinions of products from a large group of people. This aids businesses in decision making for future as it can tweak the product to suit the opinions gathered from the public. This can lead to more sales as the product can be suited to the opinions of people, making them more likely to buy the product. Finding out people's opinions can help businesses understand what is popular in general.
Targeted Advertising - targeted advertisements are shown to people which are more likely to be relevant to the person. Allows person to slightly change what the data says so the person seeing it will like it. It is designed to try to make sure money for ads are being spent wisely. Shows you the products your most likely to be interested in so there's more chance you will engage with the ad and ultimately spend money. Business can purchase data so they know what you are more interested in so they can show specialised ads for you
Applications of Big Data Use for Science
Live/Historic Data For Research - live data would be data produced within the past week and historic data can go way back hundreds of years. A lot of data is available for scientists to analyse. This can help science move forward as we find out the most efficient way to do things and we can make discoveries. An example would be pharmaceutical companies can use big data to alter their medicine. They could find out if a medicine worked from looking at facebook posts from users who talk about it.
Finding and collating research papers (and their findings) - some studies are quite small and some are not public. If you want to find out as much information as you can from studies, the best way to find out is word of mouth. Looking at all the small studies spoken about through social media can give you a bigger idea of the wider population.
Finding and collating research papers (and their findings) - some studies are quite small and some are not public. If you want to find out as much information as you can from studies, the best way to find out is word of mouth. Looking at all the small studies spoken about through social media can give you a bigger idea of the wider population.
Data Mining Methods
Data is extracted in order to find some sort of pattern in the data set. There are different levels of analysis we ca
Association rule mining: this is when the program tries to see if there is a relation between two or more variables from data. It helps find the probability of relations between variables in large sets of data by making associations from the relations found.
Correlation analysis: this is used to see how strong the relationship between two variables is. It can either be a positive or negative correlation. Positive correlation is when x goes up y goes up but negative correlation is when x goes up and y goes down (on a graph).
regression analysis: this allows you to study the relationship between two or more variables. In this type of analysis one or more variables is dependent on one variable. Points are plotted on a graph and a regressive line is drawn (or line of best fit) and this can help predict the future as we see which direction the line is heading.
Association rule mining: this is when the program tries to see if there is a relation between two or more variables from data. It helps find the probability of relations between variables in large sets of data by making associations from the relations found.
Correlation analysis: this is used to see how strong the relationship between two variables is. It can either be a positive or negative correlation. Positive correlation is when x goes up y goes up but negative correlation is when x goes up and y goes down (on a graph).
regression analysis: this allows you to study the relationship between two or more variables. In this type of analysis one or more variables is dependent on one variable. Points are plotted on a graph and a regressive line is drawn (or line of best fit) and this can help predict the future as we see which direction the line is heading.
Monday, June 3, 2019
Characteristics of Big Data Analysis 2
The big data analysis system should be powerful enough to be able to work with the data iteratively. This means the processes can keep looping until the desired result is created. The system must also be able to handle a lot of attributes because big data comes in large sizes with hundreds or even thousands of attribute types in a data source. Whereas before it may have been many records with the same attributes. Big data is often too complicated to make sense of right away in its raw form so the analysis system must be programmatic meaning it can process the data through programs. It is advised that the system should be able to connect to other computer systems via the cloud. This is because if several computers can cooperate to process the same big data set then it will reduce the processing time.
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