Applications of Big Data in Business

In class we learned how businesses big and small utilise Big Data in a business environment.

Amazon, one of the biggest retailers in the world rely on Big Data to promote products. This is done by comparing searches, clicks on similar products as well as using geological information to target region-specific requirements. For example, someone who bought a notebook will most likely require stationary. As for the region, It will promote warm winter clothing in areas with cold climates while it would be less likely to promote shorts or tank tops. This is done through algorithms that compare other users buying habits. There are many factors at play, but the basic ones would be age, sex, occupation, and lifestyle. Reviews are also a big part as well. Poorly rated products are less likely to be promoted. Big Data is used to maximise profits and encourage consumers to buy more.

Facebook also uses Big Data. Likes, comments, and cookies provide valuable data that can be extracted. This alone can be used to build a picture of the type of person is behind the keyboard, even if little external information is given. This can be quite intimidating especially if it is used maliciously, however Facebook mostly uses this data to target advertisements and make recommendations. Furthermore, they use it to analyse trends in user behaviour. 

Google utilises Big Data in Google Maps. The algorithms identify ideal routes to take with the shortest journey time. This is imperfect however as in some cases it leads to roads that have been closed off temporarily.

Smaller businesses can also utilise Big Data, not just the global giants. In fact, they should use it to gain the upper hand against competitors. Whether it is to find out trends in customer spending habits or find ways to better promote their business, it can be an effective tool in gaining the advantage in a business environment where competition is tight. 

References:

https://ilearn.fife.ac.uk/course/view.php?id=9751#section-7

Applications of Big Data in Society

In class we learned about how Big Data is used in Society today.

People in society naturally generate a lot of data, whether it is through social media, e-commerce or the websites they access. This information can be useful to government institutions, allowing them to track citizens behaviour and influence them in different ways.

One application would be the GDELT project (Global Database of Events, Language and Tone), which maps a number of themes on society, particularly media, that spans across the world. This tracks human sentiments and creates a network of events happening on a global scale.

Education would be another factor. Big Data can look at students test results and highlight those who need extra support. This might seem insignificant, however multiple students dropping out of education will contribute to a larger societal issue. By providing extra support highlighted by these algorithms, these numbers will decrease. Furthermore, the same can be applied to the courses themselves, those with low pass rates may indicate the need for further funding and development.

Crime is another application in which Big Data has been implemented. Records of previous crimes and their locations is vital information. Comparing this data on a graph can show key areas where crime rates are higher and more police are required. This is currently a work in progress however, as some criticisms have come forward arguing that predictive policing is seen as racist and targets certain communities. Fraud detection would be another application, as algorithms can be trained to look for certain anomalies online. 

References:

https://ilearn.fife.ac.uk/course/view.php?id=9751#section-9

Applications of Big Data in Science

In class we learned about how Big Data can be used in science. 

Weather forecasting has become more accurate and efficient due to the rise of Big Data. The time between forecasting has been reduced from over a week to just under 3 days. This is because Big Data uses predictive algorithms to learn weather patterns, giving insight into weather forecasts much sooner than alternative methods. It will only get more accurate over time as the algorithms gather more data, particularly on extreme weather patterns which are not as predictable. 

Healthcare has also benefitted from the rise of Big Data. It allows people to receive diagnoses much faster, but also creating new links between symptoms and illness. For example, a new study showed a link between the retina and diabetes. When an algorithm is shown multiple pictures of eyes under a powerful camera, it can distinguish who is most likely to be vulnerable to illnesses like diabetes. How cool is that? It also played a crucial role during the COVID-19 pandemic where it was able to track outbreaks and predict the number of cases in certain regions.

Another application would be in the military, particularly in the development of new technologies and analysis of patterns. DARPA utilises Big Data in managing their defense on cyber attacks, encryption, and in their engineering. It plays a crucial part particularly in the defence of countries, but has many other uses. Data from sattelites can provide vital information on the battlefield if analysed correctly, furthermore Big Data can also analyse social media platforms to judge morale.

References:

DARPA and Data: a Portfolio Overview. (n.d.). Docslib. https://docslib.org/doc/626715/darpa-and-data-a-portfolio-overview
https://ilearn.fife.ac.uk/course/view.php?id=9751#section-8

Characteristics of Big Data

In class we learned about the key components of Big Data and what variables apply to it. Each of these measures analyse the effectiveness of data sets, and are essential in having accurate, reliable data.

Big Data can be measured by using 'The 7 V's of Big Data' which include:

• Volume
• Velocity
• Variety
• Veracity
• Value
• Variability
• Visualisations

Volume refers to the size of the data sample, the scale on which it represents. Data projections have shown a dramatic increase in the volume, growing exponentially every year, which brings us onto the next point:

Velocity is the rate at which new data is being generated. Institutions must constantly upgrade and ensure they have the capacity to store data being put into their systems. Bigger measures for data storage are being used in today's world to accommodate the increasing velocity, with exabytes and zettabytes becoming more common on the large scale.

Variety is the different types of data collected. It can be structured, semi-structured, and unstructured. These refer to how easily data is to analyse, with structured being the easiest and most effective for analysis while unstructured is incredibly difficult to analyse. This can also refer to the sources in which data is acquired. Some examples include but are not limited to science, business, and government statistics.

Veracity is the term that represents the accuracy of data. In other words, its a test of how reliable the data is. This is crucial when analysing data because it isn't accurate, the end result will not be useful whatsoever. Big Data should always use data sets that are as accurate and relevant as possible. After all. if the data cannot be trusted, then why should we use it? No data set is100% accurate, however recent measures have ensured that it is as close to 100% as it has ever been.

Value refers to how useful data is, and how organisations can use the data after its value is extracted. If the data can be used, it automatically has value, however data can be used in different applications and therefore some data may be more valuable than others. Data can be used in many different ways, but a business, for example, could find value in customer data showing what products should be targeted, how to improve products, and providing valuable feedback on certain products, to name a few.

Variability is similar to veracity but slightly different. It looks at the consistency of data and the real meaning behind it. Some data may have a different meaning than what is originally intended. If inconsistencies are not found it can greatly impact the accuracy of results. 

Visualisations refers to the way data can be displayed and represented. This is commonly done through charts and graphs and makes information more readable in contrast to looking at data in a table. The format in which data is displayed makes data easy to comprehend.

We made a poster in class to represent all of these terms in class. How cool is this? (Admittedly it could be a little better)

References:

https://ilearn.fife.ac.uk/course/view.php?id=9751#section-5

Types of Visualisation

Last week in class we learned about how data can be visualised in ways to make data more readable. Large clumps of data are far too complex to understand initially, this is why we use charts and graphs to utilize and understand these data sets. Descriptive and inferential statistics are used to show relationships of different variables.

Big Data makes data handling and visualisation simple and it is the most accurate it has ever been. Since Big Data has surfaced, new ways of visualising data has emerged to handle the larger sampler sizes. Traditionally, data would be interpreted using small scale graphs using pen and paper, however the advancement of computers allows us to interpret data in ways never seen before, revolutionising the process entirely! How cool is that?

Here are a few examples:

Basic Line Chart

Bar graph with negative stack

3D Column Chart

Map Demographics

Overall, these data sets would not have been made possible before due to the limited sample size and inefficient data collection methods. These visualisations help display data in a manner that is readable and consistent in a relevant format, allowing new predictions and theories to be created. The larger sample sizes also means information is more accurate and reliable. I am excited to see what new methods of visualisation will arise in the near future.

References:

https://www.highcharts.com

https://ilearn.fife.ac.uk/course/view.php?id=9751#section-15

The Future Applications of Big Data

In class we learned about how Big Data can be utilized in future settings. Seeing the current trajectory and importance of Big Data, it shows no signs of slowing down. It's prevalence today is undeniable, but what part will it play in the future? How and why will Big Data remain relevant in a fast moving world? 

One area that Big Data plays a big part in future applications would be the political scene. Users' clicks and likes can indicate their voting pattern, predicting the most likely candidate based on similar users interests. Big Data undoubtedly played a key part in the recent American election where it was neck and neck. Big Data can highlight areas with strong opinions, and even attempt to sway voters using targeted adverts and subliminal messaging. It can even sway people to vote for corrupt politicians! How cool is that?

Big Data exceeds in analytics and data analysis. This can be used to predict future trends which is especially useful for businesses and marketing. Modern day businesses are fiercely competitive, and getting the upper hand is crucial for small businesses. By finding out what consumers want in an ever changing climate, businesses can take advantage of these trends and make more profit. One example would be face masks during the COVID-19 pandemic. Companies would have been made aware from analytics and clicks on social media and e-commerce sites. Big Data can allow businesses to get an early lead ahead of the competition.

Predictive analytics are essential for future applications in modern society as a whole, allowing the government to find key areas for future development, for example housing and new institutions. It can find out what users would like to see, their hobbies, and their spending habits. This lets the government cater to their citizens needs and wants allowing society to grow and prosper. 

References:

https://ilearn.fife.ac.uk/course/view.php?id=9751

Growth of Big Data

The scale of big data is unprecedented and the rate it is being collected is faster than ever. When early computers were first produced, kilobytes were the biggest form of memory used. Now, computers can have storage in terabytes, not including any of the other components. Other units of measuring memory are as follows:

• Nibble
• Byte
• Kilobyte
• Megabyte
• Gigabyte
• Terabyte
• Petabyte
• Exabyte
• Zettabyte
• Yottabyte
• Brontobyte

The size of the internet and all its data is estimated to be around 175 Zettabytes, and is expected to increase not just in size, but velocity as well. The rapid production of mobile devices has contributed to this surge. Furthermore, the globalization of technology means that even those in poorer parts of the world can now access the internet. People could even be reading this blog in rural India! How cool is that? 

The COVID-19 Pandemic also contributed to the growth of big data, as everyone was stuck indoors with nothing to do. People had to find new ways to entertain themselves, whether it was making videos, playing online games, or even creating blogs! All of these fed into the data pool, causing an unforeseen influx of memory.

References:

https://ilearn.fife.ac.uk/course/view.php?id=9751#section-4