Connor Maxwell

 Characteristics of Big Data (Including Visualizations) activity Big data analysis involves the interpretation and examination of large and complex datasets to extract valuable insights and make well informed decisions and there are some key characteristics of big data analysis and i will even show some visualizations.  The first characteristic i will go over is Volume and big data involves large volumes of data that cannot be effectively managed and processed using standard traditional tools and databases. Velocity is another key characteristic of big data as it refers to the speed at which data is being generated, processed and updated and is often necessary to jeep up with the fast paced nature of the data generation. Variety is another big characteristic when it comes to big data analysis and this is because big data comes in various formats including structured, unstructured and semi-structured data and so because of this analysis tools must be able to handle the diverse data type

 Traditional Data Analysis Limitations  Traditional Data analysis has many limitations including the fact that it cant handle complex or big data sets like big data can but it has other limitations too such as the fact that it does not have a big scope making it very limited and unable to handle complex data and data that changes in real time. Another limitation it has is that many traditional statistical methods assume the data follows a normal distribution so if the data deviates even slightly then the data will be unreliable to us instead of being useful. Also traditional data analysis lacks flexibility in adapting to unconventional data types, distributions or complex structures which limit their applicability in some situations. Traditional Data Analysis is not good for big data either as traditional methods will most likely struggle with large datasets due to computer limitations and they may not be optimized for dealing the complexity of big data. Finally humans are another reas

 Traditional Statistics Activity There are two types of traditional sta tistics which draw conclusions from data and also analyse the data however they are quite different. First are descriptive statistics which involves the organisation, summarization and presentation of data in a meaningful way and it basically aims to describe the main features of a dataset without making any kind of interference's about the larger population for example graphical representations with bar charts, pie charts and histograms. The second type is called Inferential statistics which involves drawing conclusions and making predictions about a population based on a sample of data from said population but in other words it uses probability to make inferences about the characteristics of a population from a limited set of observations or data and a good example of this is Hypothesis testing which is examining whether there are significant differences or relationships between variables in a population base

 Value of Data Activity  Data has always been a valuable resource however nowadays it has become bigger than anyone could have thought and this is because of big corporations using data to analyse specific datasets and using their own methods to extract data from the bigger datasets which in turn will generate bigger revenue for corporations. The value of data often depends on the relevance of the data, the quality, demand for the data and how much money you are able to get from the data itself. The reasons for these big companies utilizing and analyzing all this mass amounts of data is so they can develop new ways to make products, improve the work place by improving decision making and optimize the workplace also. For example we gather data all the time for public transportation specifically for buses and the benefits of this are that using the data you can make dynamic routing for the best possible routes, predictive analytics for arrival times, traffic optimization and fare cost op

 Historical Developments Of Big Data Activity There has been a lot of historical developments of big data and this has been a thing since well before the year 2000 and i will go over these developments here, starting with before 2000's the concept of handling large volumes of data had been around for decades especially in the research and scientific fields and early management systems aka (DBMS) started to show up and handle larger datasets. Now in the early 2000's the term 'big data' actually started to gain a lot more popularity as a term to describe datasets that were too large and complex for the traditional systems to handle and in the early 2000's an industry analyst named Doug Laney decided to introduce a concept called the 'three V's' volume, velocity and variety and named these as defining characteristics of big data. in the mid 2000's became Google's publication on MapReduce and the development of the Hadoop framework such as the NoSQL

Reasons For the Growth Of Data Activity  There are many reasons as to why data is growing and only keeps growing and i will cover some of these points here, for starters the digital world has only gotten more big and prominent over the last 10 years and so because of the digitization of everything now it has lead to a significant increase in data growth as technological advancements are made for example this can be from social media, mobile devices, online transactions etc. As i stated before the technological advancements more particularly in processing and storage make them have a lot more capabilities and not only that the the development of high performance devices nowadays makes it a lot more feasible to actually be able to handle all of this data growth without much worry. One of the biggest reasons for the growth of data actually comes from the growth of the internet and social media itself as the amount of people using all of these social media platforms and increasing the use

 Growth Of Data Activity  The use and growth of Big Data has only gotten bigger over the last few years and mainly started to happen when the COVID 19 pandemic started and in 2020 only 2% of data was saved even though there was over 64.1ZB created however in the next few years the growth of data creation will be increased to around 23% in around 2025 which would mean 181ZB would have been generated by this time but with that being said The overall growth is expected to impact several different data measures including data storage, market spending and data generation and in the coming years the amount of generated data is expected to be around twice of all data created since the beginning of storage. As you can see below the Global Big Data Market is Growing day by day and does not show any signs of slowing down as the revenue only gets higher and higher globally (not just in the US) and with the global adoption of cloud computing it only keeps boosting the big data analytics further as

 Definition of Big Data Activity I believe Big data is essentially large and diverse sets of information that even traditional data processing tools struggle with. It has a few characteristics such as the sheer volume of it, velocity and variety and it is mostly used by big corporations to gain valuable insights so they are able to make more informed decisions than with traditional data. The variability of the data can be an issue as it will be unpredictable and have various different formats, the volume can also be a substantial problem as most big data is often terabytes to exabytes in size and finally the velocity of big data is an issue as data comes in at speeds that are unprecedented.  Big data can be collected from publicly shared comments on social media networks and websites, gathered from apps and electronic devices  voluntarily. A use of big data includes the fact that Data analysts look at different types of data and then look at the relationship of them to then determine w