Big Data

 value of data  The value of data refers to the benefits and advantages that organizations can derive from their data assets, such as innovations, services, security measures, improved decision making, better customer experiences, increased operational efficiency, and new revenue streams. Value refers to the benefits that big data can provide , and it relates directly to what organizations can do with that collected data. Being able to pull value from big data is a requirement, as the value of big data increases significantly depending on the insights that can be gained from it. The 5 V's: Velocity is the speed at which the data is created and how fast it moves. Volume   is the amount of data qualifying as big data. Value is the value the data provides. Variety   is the diversity that exists in the types of data. Veracity   is the data's quality and accuracy.

Contemporary applications of big data in science      Data science is used for a wide range of applications, including predictive analytics, machine learning, data visualization, fraud detection,recommendation systems and decision making  in various industries like healthcare, finance, marketing, and technology. some examples:  Healthcare  could have a significant effect on wellness, enhancing diagnostics and tailoring treatment strategies. Predictive research helps doctors and nurses to concentrate on optimal quality and patient treatment, providing a strategic system to tackle medical concerns before they get ill. Improving sports performances the use of videos to track players performance during games, sensors in the sports equipment to allow feedback via smartphones and cloud servers.  Data science is the study of data to extract meaningful insights for from businesses to science labs  .It is a multidisciplinary approach that combines pri...

  Contemporary applications of big data in business    In public services, Big Data has an extensive range of applications, including energy exploration, financial market analysis, fraud detection, health-related research, and environmental protection.  Big data analytics helps businesses make improved decisions faster allowing companies to develop effective strategies to maximize profit .The influence and benefits of these decisions are felt across different sections of the business, from higher management to operations management and the supply chain. Some examples in businesses :  Netflix is unarguably the biggest online platform for streaming movies and TV shows, and it owes its success to Big Data. Because they know their users, their retention rate is 93% which, compared to their main competitors, is a huge number. Spotify , on-demand music-providing platform, uses Big Data Analytics, collects data from all its users around the globe, and then uses the ana...

  Characteristics of big data analysis There are 7 Vs of Big data analysis they are :  Volume Velocity  Variety  Variability Veracity  Value  Visualization Volume  - As the term implies, big data analytics entails handling and analyzing vast amounts of data. Velocity  - Denotes the speed at which data is generated, Variety  - Refers to the diversity of data types and sources. Variability  - Big data often contains noisy and incomplete data points, which can obscure valuable insights. Veracity  - Veracity pertains to the accuracy and authenticity of the data. Value  - A successful big data analytics strategy must generate value. V isualization  - Plays a vital role in data analytics, as it involves presenting the analyzed data in a visually comprehensible manner. 

  Limitations of traditional data analysis    When analyzing data, it can have some limitations that will affect the overall usefulness or of the data. The data’s quality is based on its limitations. Data quality — The data’s quality is measured based on if there are any biases, mistakes or missing data. Sample size — The data size is measured based on the actually size of the amount of data collected from the people questioned. The more people that are questioned the more valuable the data is due to it being more accurate. This is often hard though costing a lot of time or money. Limited scope —  f they have a limit scope of what the data should be the data will be limited in scope. This will make the data useless.  Assumptions — In most data analysis methods people make assumptions about there data. A common assumption made during these data analysis is that the questions were handed out equally to people and there was no bias in who they choose or how diff...

  Traditional statistics Tradition statics is where the science that deals with the collection, classification, analysis of numerical facts or data  and that, by use of mathematical theories of probability, imposes order and regularity on aggregates of more or less disparate elements. There are 3 main Types of descriptive statics Frequency distribution, central tendency and variability of a set of data, The frequency distribution records how often data occurs. Central tendency records the data's center point of distribution.  variability of a data set records its degree of dispersion. inferential statics allow you to assess whether patterns in your sample are likely to be present in your population. Some common inferential statistical tests include t-test, ANOVA, chi-square, correlation and regression.  Is hypothesis testing , where sample data is used to test a claim or hypothesis about a population and make conclusions about the population based on the results.

  The reason for Data Growth  There are many reason for data growth but one of the main reason is the rise in mobile phones and other electrical devices as well as loT that can be led to new types of data being collected, organized, and analyzed. Some examples include:  Sensor Data,  data collected by internet-enabled sensors to provide valuable, real-time insight into the inner workings of a piece of machinery.  Transactional Data , data from online web stores including receipts, storage records, and repeat purchases.  Health-related data heart rate monitors, patient records, medical history With this information, companies could now dig deeper than ever into previously unexplored details, such as customer buying behavior and machinery maintenance frequency and life expectancy. Other main reason is the amount of data is increasing due to factors such as rapid growth, spread of mobile devices, network services, and online users on the internet.  ...

 The Growth of Data   Data is created every day  each year, figures are growing at an ever-increasing rate.It is estimated that 90% of the world's data was generated in the last two years alone.I n the space of 13 years, this figure has increased by an estimated x60  from just  2 zettabytes  in 2010. T he total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. The  120 zettabytes  generated in 2023 are expected to increase by   over 150%  in 2025, hitting  181 zettabytes . 

Historical development of big data The history of big data  dates back to the 1960s and 1970s, when computers were first introduced for data processing. However, it was not until the 1990s that the term "Big Data" was coined to describe the growing volume, variety, and velocity of data being generated by various sources. there are 3 big data phases that have been developed through the years : Big Data Phase 1 – Structured Content Data analysis, data analytics and Big Data originate from the longstanding domain of database management. It relies heavily on the storage, extraction, and optimization techniques that are common in data that is stored in Relational Database Management Systems started to professionalize in the 1970 s.    Big Data Phase 2 – Web Based Unstructured Content From the early 2000 s, the internet and corresponding web applications started to generate tremendous amounts of data. In addition to the data that these web applications stored in relational data...

  what is big data  Big Data is also commonly known as  data sets that are too large or complex to be dealt with by traditional  data-processing application software. Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is a collection of data from many different sources and is often describe by five characteristics:   volume, value, variety, velocity, and veracity . example of big data is :  Tracking consumer behavior and shopping habits to deliver hyper-personalized retail product recommendations tailored to individual customers There are 3 main types of big data;  structured - which means the data is highly organized semi structured - which means that the data is fairly structured   unstructured - which means that the data lacks a predefined structure