big data

Bilal Hussain Malik 12/4/25                                   Contemporary applications of big data in business(1)                                                                                                               American Express GBT  American Express Global Business Travel (Amex GBT) with operations in over 120 countries and over 14,000 employees sought to amplify online travel program capabilities to gain market share and differentiate core services. The company had main challenges regarding scalability, performance, and data governance. Creating a single portal for 945 separate data files using existing BI to...

 Bilal Hussain Malik 22/3/25                                                                Types of Visualization                                                                        Charts Graphs offer visual displays of data to highlight comparisons, distributions, and trends. They include bar charts, line charts, pie charts, and area charts. Bar charts use rectangular bars in comparing values across categories and are most appropriately used to display differences in amount. Line charts connect data points by lines and are most appropriate to display trends over time like sales growth or temperature changes. Pie charts divide a circle into sl...

 Bilal Hussain Malik 21/3/25                                                             Data Mining Methods                                                          Classification Classification is a supervised learning method that categorizes data into known categories. Using algorithms like decision trees, SVM, or neural networks, it is trained using labeled training data to predict class labels for novel cases. Common applications include spam filtering (spam or ham labeling of emails), medical diagnosis (disease diagnosis from symptoms), and credit card fraud detection. It involves feature selection, model training, and validation in order to get accuracy. Some of the greatest chall...

Bilal Hussain Malik 21/3/25                                            Types of Problem Suited to Big Data Analysis                                                   Pattern Recognition in Massive Datasets Big data analytics revolutionizes pattern recognition by processing vast, complex datasets that traditional methods cannot handle. In financial sectors, banks employ big data technologies to detect fraudulent transactions by analyzing millions of operations in real time. Machine learning algorithms scrutinize spending patterns, flagging anomalies such as unusual transaction locations or amounts, which might indicate fraud. Without big data tools like Hadoop for storage and Spark for real-time processing, identifying these subtle, global patterns would be...

 Bilal Hussain Malik 21/3/25                            Strategies for Limiting the Negative Effects of Big Data As big data continues to seep into every aspect of life, it is important to establish strategies to curb its negative impacts while speeding up its advantages. One of the most important strategies is establishing sound data governance models. Organisations must develop robust policies and guidelines for data collection, storage, and utilisation, observing privacy laws and ethical standards.  Another key is to prioritise data privacy and security. Organisations must implement strong security procedures, such as encryption and access mechanisms, to protect sensitive data from being compromised. Ongoing auditing and monitoring of data security controls can help identify vulnerabilities and ensure compliance with data protection legislation, such as the General Data Protection Regulation (GDPR). Addit...

 Bilal Hussain Malik 19/3/25                                                Implications of Big Data for Society                                                 Enhanced Governance and Public Policy Big data is transforming the way governments operate, allowing for more effective, responsive, and informed governance. Through the collection and analysis of vast amounts of data from numerous public and private sources—such as social media, sensor networks, satellite imagery, and administrative records—governments are in a position to better identify trends, maximize resource efficiency, and deliver higher-quality public services. This shift toward evidence-based policymaking enhances accountability, transparency, and civic engagement. For example, ...

 Bilal Hussain Malik 19/3/25                                                 Implications of Big Data for Individuals                                       Implications of Big Data for Individuals                                                        Personalized Services Big data allows companies and organizations to customize services and experiences according to individual users. It is accomplished by collecting and analyzing huge amounts of data such as browsing activity, purchase history, social media engagement, and location. For instance, streaming services like Netflix utilize data to customize shows and films according to a...

 Bilal Hussain Malik 19/3/25                                               Limitations of Predictive Analytics                                                        Data Quality Issues Predictive analytics is just as good as the data quality upon which models are trained and built. Data quality comprises a number of dimensions such as accuracy, completeness, consistency, and timeliness. If data fed into predictive models is corrupted, the output will be inconclusive or misleading no matter the level of sophistication of the algorithm. Another of the greatest issues is from inaccurate data, which can vary from human entry mistakes, outdated data, or faulty sensors in automated systems. Inaccurate data distorts patterns, leadin...

Bilal Hussain Malik 18/3/25                                      Technological Requirements of Big Data To effectively harness the potential of big data, organisations must meet certain technological requirements to enable the storage, processing, and analysis of massive volumes of data. Stable data storage mechanisms are one of the minimum requirements, which are capable of holding the humongous volume of data being generated. Cloud storage infrastructures, scalable such as Amazon Web Services (AWS), Google Cloud, or Microsoft Azure, provide secure and dynamic environments to store data, allowing organisations to expand their capacity for storage depending on their fluctuating requirements. High-performance computing infrastructure is required to process and analyse big data efficiently. Distributed computing platforms like Apache Hadoop and Apache Spark enable organisations to process data o...

Bilal Hussain Malik  18/3/25                                                    Future Applications of Big Data                             Personalized Healthcare & AI-Based Healthcare Big data is transforming healthcare through precision medicine, with AI analyzing genetic data, medical history, and readings from wearable devices to predict diseases like cancer or diabetes ahead of time. Machine learning tools can identify high-risk patients through biomarkers, enabling preventive intervention. Big data is used by pharmaceutical firms to accelerate drug development, simulating drug interaction to compress the development timeframe. In real time, tracking via IoT devices enables doctors to adapt treatments dynamically. Challenges are algorithmic bias in diagnosis and data privacy ...

Bilal Hussain Malik 18/3/25                             Contemporary applications of big data in science  Big data is transforming scientific research across various disciplines, enabling groundbreaking discoveries and advancements. In genomics, researchers analyse vast datasets to understand genetic variations and their links to diseases. This knowledge paves the way for personalised medicine, tailoring treatments to individual genetic profiles. Genomic data analysis has led to significant breakthroughs in disease diagnosis, treatment, and prevention. In astronomy, big data plays a crucial role in processing information from telescopes and satellites. By analysing massive datasets, scientists study celestial objects and phenomena, gaining insights into the universe's origins, evolution, and mysteries. Astronomical big data has led to numerous discoveries, including exoplanets, dark matter, and dark energy. Clima...

Bilal Hussain Malik 18/3/25                              Contemporary applications of big data in business  Big data is revolutionising the way businesses operate, offering a wide range of applications that drive growth, efficiency, and innovation. One major application is customer insights, where businesses analyse customer behaviour, preferences, and needs to personalise marketing strategies and improve customer experiences. By leveraging data analytics, companies can gain a deeper understanding of their target audience, tailor their offerings, and enhance customer satisfaction. Supply chain optimisation is another key area where big data is making a significant impact. Companies use data analytics to streamline logistics, reduce costs, and improve efficiency. By analysing data from various sources, businesses can optimise their supply chain operations, predict demand, and respond to changes in the market....

 Bilal Hussain Malik 18/3/25                                        Characteristics of big data analysis Volume: The massive amount of data being generated second by second by sources like social media, sensors, transactions, etc. Big Data tools like Hadoop, Spark are designed to hold terabytes or petabytes of data. Example: Facebook Processec 500+ TB of data  daily from posts, images, and videos. Velocity: It describes how rapidly the data is generated, processed, and analyzed. For example, real-time data streams from IoT devices or stock market tickers must be processed extremely fast to be of use. Example:Stock market algorithms analyze real-time ticker data in milliseconds for trading decisions. Veracity: Addresses data quality, accuracy, and believability. Noisy, missing, or inconsistent data can lead to unreliable conclusions. Example:  Twitter uses AI to filter fake ...

 Bilal Hussain Malik 17/3/25                                         Limitations of traditional data analysis Traditional data analysis methods face significant limitations when applied to big data, primarily due to its enormous scale, diversity, and complexity. One major limitation is scalability, as traditional tools are often overwhelmed by the sheer volume of big data, struggling to process and analyze it efficiently. Additionally, the velocity of big data, which refers to the speed at which data is generated and needs to be processed, poses a challenge for traditional methods that are not designed for real-time analysis. Another limitation is the inability of traditional methods to effectively handle unstructured data, such as text, images, and videos, which constitute a substantial portion of big data. Traditional tools are typically designed for structured data and lack the ca...

 Bilal Hussain Malik 17/3/25                                                        Traditional statistics (descriptive and inferential) Case study:  This data has information of 8 students their age, gender, study hours, attendanceand test score  Descriptive statistics help summarise and highlight the key characteristics of a dataset. They provide a precise description of the data without predicting or concluding anything beyond the immediate observation. Average Mean Test Score: The average score for all the student is 75.875.  Median Test Score: When test scores are arranged in order, the middle value is 77.5. The median is a consistent measure of central tendency because it is resistant to very high or very low scores. Mode of Study Hours : The most common number of study hours for the students is 6. This shows that studying...

 Bilal Hussain Malik 17/3/25                                       Value of data (including future value) Data has become a critical asset for organizations and society, offering immense value in various ways. By analyzing data, organizations can uncover patterns, trends, and correlations that inform decision-making and drive innovation. Data is the foundation of advancements in artificial intelligence, machine learning, and automation, enabling new technologies and applications. Businesses use data to: 1. Optimize operations: Streamline processes and improve efficiency. 2. Personalize customer experiences: Tailor services to individual needs. 3. Predict market trends: Stay ahead of competitors. The value of data extends beyond business applications. Data-driven insights can: 1. Improve healthcare: Enhance patient outcomes and disease prevention. 2. Enhance urban planning: Create smarter,...

 Bilal Hussain Malik 17/3/25                                              Reasons for the growth of data  The growth of data is driven by several key factors that have transformed the way information is generated, collected, and utilized. One major driver is the digital transformation across industries, where businesses, educational institutions, healthcare providers, and entertainment platforms increasingly rely on digital solutions to streamline operations, enhance customer experiences, and improve decision-making. This shift has led to an explosion of data generated from various digital platforms. Social media platforms like Facebook, Twitter, and Instagram have also played a significant role in data growth, as users create and share vast amounts of content, including text, images, and videos. The proliferation of Internet of Things (IoT) devices, such as smart...