Big Data

  Big data has been changing various industries already, however future applications are bound to expand more than they already have, i have listed some potential future applications of big data below 1. Smart cities: Big data can be used to create smarter and more efficient cities by analysing data from various sources like sensors, cameras and social media to optimize traffic flow. 2. Retail: Retailers can leverage off of big data to analyse customer preferences and behaviours, optimize inventory management, personalize marketing campaigns. 3. Education: Big data analytics can personalize learning experiences by analysing student performance data to identify areas for improvement, tailor educational content and provide real time feedback 4. Cybersecurity: big data analytics can enhance cybersecurity by analysing large volumes of data to detect and prevent cyber threats in real time, identify vulnerabilities and improve incident response 

Since big data is so versatile, it can be used in a variety of industries and settings. Below i have listed ways big data is used in a social aspect. Within a country, it is difficult to keep track of livestock and land. keeping track of the many types of crops and livestock would be difficult for the government, however the use of big data analysis simplifies this process to make it much simpler Transport- Local governments are able to use traffic flow data on a variety of routes, using road sensors, video cameras and gps devices. they are able to identify any road risks using this information. Education- A better understanding of educational needs is done by using big data. Many children can benefit from this in the long run. The government can get a better understanding of the educational needs with the help of big data.

  Over the years Healthcare analytics has became increasingly more important in the world. Below are some examples of how the health care industry can get an advantage of health care analytics. Improving healthcare: Big data analytics allow for DNA strings to be decoded in minutes, this leads to the chance of new cures and a better understanding of disease patterns. Electronic health records (EHR's): EHR's are one of the most important applications of big data in healthcare. Every person has a digital record, which includes medical  history lab test results and more Advanced disease and risk control: Big data has the potential to change the healthcare industry. by collecting and analysing data. Healthcare providers can better understand the hospitalization risk for patients with chronic conditions.

  Contemporary applications of big data in business cover various industries. Below i have gave examples of how big data is used in businesses. Customer analytics: Business analyse large volumes of customer data to gain insights into consumer behaviour, preferences and sentiment. By using big data analytics, companies can personalise marketing campaigns, improve customer segmentation and enhance customer experiences. Supply chain optimisations: Big data analytics helps optimise supply chain operations by analysis large amounts of data which is related to inventory levels, productions processes and transportations routes. Fraud detection and risk management: big data analytics is used to detect fraudulent activities, mitigate risks, ensure compliance with the regulatory requirements. by analysis's transaction data, behaviour patterns, businesses can identify potential fraudsters, detect suspicious activities and prevent financial loss.

Big data analysis is a process of looking at datasets for useful information, every dataset has their own characteristics Volume- This refers to the datasets that are voluminous (large in size) when there is large volumes of data to analyse traditional big data analysis becomes ineffective as it works better with small amounts of data Varity- This refers to different types of data that is found when doing data analysis. this could either include non-structured and semi structured data. Velocity- Velocity refers to the speed of the data analysis. since data can become old or less valuable over time it is important that it is analysed quickly. Visualization- Visualisation is a form of analysis using visual methods to represent datasets for people. Data presented in this way is simple and easy to understand. Machine learning- Machine learning is about the use of machine learning algorithms to spot patterns in the big data. after a while you will be able to use the data gathered for predic...

  Limitations of tradtional Data Analysis come in a number of different ways Data quality- The datas quality is measured based on if there are biases or missing data. Sample size- the size is measured based on the actual size of amount of data collected from the people questioned. The more people that are questioned the more valuable the data becomes. Limited scope- the limited scope is really something that can be changed by the people doing the data analysis . a common assumptions made during these data analysis is that the questions were handed out equally to people. human error- This is just when someone makes a mistake in there working. this could be at the gathering of the analysis of the data. If the data is wrong due to human error it will make it useless and cannot be used.

  What are Tradtional Statistics? All statistical techniques are divided into two broad category's, these being descriptive and inferential statistics  What are statistics? Statistics is the area of applied math that deals with the collection, organisation, analysis, interpretation, and presentation of data. There is three different main types of descriptive statistics. Distribution- this shows us the frequency of different outcomes Ina  population or sample. Central tendency - this is the name for measurements that look at the typical central values within a data set. This does not just refer to the central value within an entire dataset. variability- this refers to how values are distributed or spread-out. identifying variability relies on understanding the central tendency. What is inferential Statistics? Inferential statistics focus on making generalizations about a larger population based on a representative sample of that population, however since inferential statis...

  To begin talking about the value of big data we need to talk about the 5Vs of big data before anything.  Volume- Stands to the amount of data that actually exists, volume is the base of big data as its the initial size and amount of data that is collected Velocity - refers to how quickly data is generated and how fast it moves, this is important aspect for organisations that need their data to flow quickly. Variety- Refers to the diversity of data types, An organisation might obtain data from several data sources, which might vary in value Veracity- Veracity refers to the quality, accuracy, integrity and credibility of data, data that is gathered could have missing information, could be inaccurate information, or could turn out not to be real, veracity, overall refers to the level of trust there is in the collected data Now that we have discussed the 4Vs we can talk about the final one which is value! Value- Value refers to the benefits that big data can provide, it relates...

  Businesses are starting to use big data mining services to analyse assess and convert big data into actionable information.  3 Reasons for Data growth 1. Business models: Permanent retention of data is becoming a popular business model, google is a prime example of this. 2. Infrastructure capacity: The cost of data storage has come down as a capacity has shot up, right now capacity is doubling every two years! 3. Business Analytics: Business Analytics has experienced rapid acceleration in recent years and is now a 100 billion dollar market and growing at a rate of 7% a year

  Big Data is primary intended to analyse, process and extract information from massive amounts of data and extremely complex structures. An average internet user generates about 1.7MB per second, as of January 2024 there are over 5 billion internet users world wide In 2010 the volume of Data created, captured, copied and consumed worldwide was 2 zettabytes, however in 2020 it was 64 tb seeing a rise of 32 times! in 2023 alone there was 120 zettabytes which is nearly double from 2020 in only 3 years. It is expected to be another rise in 2025 with it hitting over 180 zettabytes!

  The term "Big Data" has been used since the early 1990s, The history and evolutions of Big Data can be split into 3 separate phases where each phase was driven by advancements in technology! Big Data Phase 1- Structured Content Data Analysis, Analytics and Big Data originate from the domain of database management. It relies heavily on storage, extraction and optimization techniques that are common in data that is stored in Relational Database Management Systems (RDBMS) Big Data Phase 2 - Web based  Unstructured  Content  From the early 2000s, the internet and web applications started to generate large amounts of data, in addition to the data that these web applications stored in relational databases, IP search and interaction logs started to generate web based unstructured data. Unstructured data sources provides these organisations with new knowledge, such as the needs and behaviours of internet users. Big Data Phase 3 - Mobile and Sensor-Based Content The third a...