HNC BIG DATA 2024

  It is a powerful tool but it comes with many limitations that affects its accuracy and reliability. Here we have come examples: 1.     Data Quality : Predictive models rely on large, accurate, and relevant datasets. If the data is incomplete, inaccurate, or biased, the predictions will be flawed. 2.     Human Behaviour : Predictive analytics cannot always accurately predict human behaviour , which can be influenced by numerous unpredictable factors. 3.     Data Relevance : The data sets need to be consistently updated to remain relevant, as outdated information can lead to incorrect predictions. 4.     Clear Goals : Without clear goals, predictive analytics can produce results that are not actionable or relevant to the business’s needs. 5.     Complexity of Models : The more complex a model, the harder it is to interpret the results, which can lead to misunderstandings or misapplications of...

Big Data require many technological resources to effectively manage and analyse the vast amount of data generated by many modern systems so let’s mention some of the key requirements and some examples of them. 1.     Data Storage. (Data lakes and Hadoop) 2.     Data Processing. 3.     Data mining. (RapidMiner and Presto) 4.     Data Analytics. (Hadoop, Spark, NoSQL databases, etc) 5.     Programming Languages. 6.     Data Visualization. 7.     Machine Learning and AI. 8.     Scalability. 9.     Security. 10.   Data Governance. These requirements show us the complexity of all the tools and technologies needed to harness the full potential of Big Data.

  The technology is getting updated every day and big data is getting a lot across various sectors, let is mention some of them based on what we have so far. ·          Healthcare. ·          Automobile Development. ·          Smart Assistants. ·          Industrial Automation. ·          Advanced analytics and AI powered insights. ·          Privacy and Security. ·          Deep learning and Neural Networks. ·          Ethical AI and BIAS mitigation. Potential applications will be added but in the meantime I would say that big data will continue to be a driving force behind innovation and efficiency in the digital evidence.

  There are many similarities in the applications of Big Data in society compared to the other in science or business but lets mention some of them. Customer insights and personalization where business analyse large scale social media and browsing behaviour to create comprehensive customer profile. For example Netflix uses this method to improve or innovate products and take advantage to satisfy customers needs. In the society is also used to generate a positive impact, for example the misuse act can by pass privacy and data protection law Basically it helps to the society to generate reliable information that they can use later on   and get good value back.

As we mentioned in our previous post big data has many applications but today we are going to talk about the applications in science. One of the applications is the replicability and reproducibility. By analysing big data set, scientists can validate existing theories, discover new patterns and enhance their understanding of complex phenomena. Another to mention is the transition from RAW data to information products. We can also mention earth science where big data plays a crucial role by providing unprecedented opportunities to enhance our understanding of earth intricate patterns, such as climate change, natural disaster and ecosystem dynamics. In summary, big data is transforming the scientific landscape by driving innovation and providing valuable insights across various disciplines.

  Big data became a game changer across various industries, enabling organizations to get valuable insights and drive decisions making. Factors that we can mention Marketing, Transportation, Government and public Administration, business operation, health care, cybersecurity, etc. Let’s mention some of the applications examples. For example, In marketing they use the forecast customer behaviour to analyse customer information, predict market trends, and understand buyers habits so this helps prioritize products and service effectively. In transportation we have to mention assist in GPS navigation, traffic and weather alerts so big data helps to optimize routes, predict traffic congestion and provide real time weather information or traffic. Big data is also used in cybersecurity where helps identify security risks, detect anomalies, and protect against cyber threats. As we mention before in our previous posts, big data is not just about managing vast volumes of data but its...

  Let’s some of the most important characteristics of big data analysis. ·          Volume. Refers to the sheer amount of data. ·          Velocity. Refers to the speed at which data is generated and processed. ·          Variety. Refers to the different types of data. ·          Veracity. Relates to the accuracy and reliability of the data. ·          Value. Organizations invest in big data analytics to extract meaningful insights that drive business decisions, improve productivity and processes and of course create value. ·          Visualizations. It is a powerful tool which uses visual methods (such as charts, graphs, and maps) to represent complex data sets in a simple and understandable format. By doing this the companies can im...

  Here you have some of the most important limitations that I can mention, specially when compared to more advanced approaches like big data analytic and artificial intelligence. One of the limitations is the poor possibility to anticipate future trends and take proactive measures, manual resources and resource intensiveness, limited scalability, static models, etc. Traditional data analysis often requires significant manual effort that means that analysts spend considerable time collecting, cleaning and analysing data and this process can be time consuming and resource intensive, specially in time sensitive situations. In summary, while traditional data analysis has its place, organizations are increasingly turning to big data analytics and AI to overcome these limitations and gain deeper insights from their data.

Here you have a quick view of traditional statistics, encompassing descriptive and inferential statistics that are fundamental tools for analysing and interpreting data. Descriptive statistics summarize and describe the main features of a dataset, including measures of central tendency (such as mean, median, and mode) and measures of dispersion (such as range, variance, and standard deviation), these statistics provide insights into the characteristics and distribution of the data. In contrast, inferential statistics involve making inferences and predictions about a population based on sample data and this includes hypothesis testing, where statistical tests are used to determine if there is a significant difference or relationship between variables. Additionally, inferential statistics include estimation techniques, such as confidence intervals, which provide a range of plausible values for population parameters.   Together, descriptive and inferential statistics form the foun...

  Data value depends on different factors however we can mention one related to a business context where the data does not have a specific value like tangible assets but based on the potential of the data which contributes to a business and how you use it to generate revenue or save costs then it has a big value. This factor is the most important since it is going to impact the future so they can estimate how much an investment will be worth in the future. AI is an example of the value of data for the future since a big data base with more information can be connected to an AI so the more data the more reliable will be an answer. In summary, the value of data extends beyond the immediate financial and business gain and the more information the more value will be.

  There were and I would say there are many reasons for the fascinating phenomenon of the growth of data but in this opportunity I will mention some key factors which contribute to this event. The first thing to mention is the increased data usage, as the needs for data increased, so did the speed of data increased too and factors like covid19 make this needs increased a lot too. Another thing to mention is unstructured data explosion since the needs of data usage increased then the nature of data has changed too and there different sources of data like data bases, social media content, sensor data, images, videos and more and that’s why it was predicted that by 2026, more than 80% of global data is going to be unstructured due to the way how we use and consume data. We do not have to forget we have technological advancements and new devices, there are many things that contribute on this scenario such as AI, encourage data collection or utilization, cloud computing, smart dev...

  The fascinating phenomenon of growth of data driven by our increasingly interconnected world has some important aspects like data explosion, factors fuelling data growth or storage capacity. For example, in 2020, this data volume reached an impressive 64.2 zettabytes and in the next five years global data creation is projected to soar to more than 180 zettabytes. We have to mention that other factor like covid19 accelerate data growth process too due to increased remote work, online learning and home entertainment options. In summary, our digital universe is expanding at an astonish pace, an managing this data deluge remains a critical challenge for enterprise and individuals alike.

  The technology is getting updates very quickly, and Big Data is a big journey trough several decades so we can mention some key moments in the history of big data evolution starting with the first event in 1944 where Fremont Rider speculated that by 2040, the yale library would house approximately 200,000,000 volumes, spanning over 6,000 miles of shelves. So this prediction was known as the “Information Explosion”, highlighted the growing volume of data. After this event the concept of storing and analysing vast amount of data began to take shape, however, it was not until several decades later that the term “BIG DATA” gained popularity and significance. After 1960s main frame computer emerged offering solutions for data storage process and then the 1980s saw a significant leap forward with the advent of relational database which provided a better solution for managing data. The 1990s marked a revolutionary shift with the rise of internet and the proliferation of digital tech...

It is a diverse set of information that grow at ever-increasing rates. It encompasses three different aspects which often referred to as “The Three Vs” (Volume, Velocity and Variety). So Basically, Big Data is how the large, complex and variety of data is handled so this allow organizations to address business challenges and manage this information in the most efficient and proper way possible.