Khalid Nawaz

                                Application of big data techniques to a problem Just think, Big Data is not the load of data. The data are there mainly in order to respond to real world problems. A classical example is the application of Big Data for a telecommunication company in order to foresee customer churn. After analyzing customer interactions, the history of their bills, and usage patterns, the organizations can make a customer segmentation to identify who is most likely to leave. Once a churn risk model has been developed with help of machine learning methods, the enterprises that have the model at their disposal can get in touch with problematic clients by giving them personalized proposals, extra value for the services, or any special help required to them. This can highly contribute to the retention of customers while it leads to the reduction of the cost to attract new ones. The model continues ...

                                                            Data mining methods Data mining is one of the fundamental Big Data analytics techniques. It is used to exploit interest patterns and uncover the concealed insights in large datasets. The different functions it performs like predicting outcomes, dividing data into clusters, and showcasing hidden relationships provide data mining with a wide range of business and research applications. There are many data mining techniques, each performing a particular analysis based on the problem it is used to solve. Classification is among the most-widely used methods. It includes teaching models to categorize emails into specific folders like "spam" or "not spam." On the other hand, Clustering is an unsupervised approach by which data are grouped into similar clusters - it is applicable in cust...

                                                         Strategies for limiting the negative effects of big data Big Data is drastically reshaping society through various ways. It plays a pivotal role in the enhancement of public services, such as traffic control and in the smart cities or the health domain by predictive diagnostics and new therapies for treating diseases. Data are used by the public sectors and other institutions to arrive at the most appropriate decisions, respond to different crises, and tackle global challenges such as climate change and poverty. Big Data helps to improve people's lives quality and acts as a catalyst to social progress if used effectively. Furthermore, Big Data is a source of numerous problems. Firstly, privacy is a concern. Citizens cannot always figure out how much of their personal data is being t...

                                              Implications of big data for society The risk of big data growing in popularity is also growing. From data privacy to algorithmic bias, the misuse of big data has drastic consequences for both people and communities. Consequently, organizations have to implement strategic methods to prevent these harmful consequences and thus be able to maintain data use ethical and responsible. One such important strategy is the use of improved data protection through techniques like anonymization, i.e., the removal of personal identifiers, and encryption, i.e., data protection in storage and transfer. It is with the help of these measures that breaches or misuse can be prevented. Additionally, adherence to open data governance policies ensures that data are gathered, stored, and shared in a manner that is respectful of privacy and the law...

Privacy Concerns Every time individuals browse the web, use mobile apps, or interact with digital services, the idea of privacy changes significantly. The main idea is that people are the ones who release the data, even unknowingly during browsing, and these data are collected by accompanying this process cookies, sensors, and different types of user input. The companies and platforms use this data to operate more efficiently, as they now have detailed profiles--including the history of visiting certain pages, email, interests, location, and even the person's biometric information. The people are more concerned with the amount of data that is collected and it is opaqueness. Most individuals are not at all informed of the extent to which their data is tracked. They have no idea about it, and themselves, they are increasing the problem. There are a few cases of data that is something related to the bettering of the users' experiences, but the majority of it is handed over to adve...

Data dependency and Quality Issues  It illustrates the point of the best forecasts as the purity of the feedstock of data. When the data is defective in one of the following: whether the data is limited, old, inconsistent, fake, then the predictive capability will in many cases show a bias. A very smart algorithm cannot make correct predictions if the input data is not correct. On the other side, good cleansing and control of the data must be a recurring issue. Predictive analytics is a kind of forecasting and decision-making that heavily depends on data. In case of any error or omission up to the point that wrong data is put into the system, the result will definitely be wrong. This kind of situation is often referred to as "garbage in, garbage out." If, to illustrate, the sales data is outdated and is full of errors, the projected sales made could, from a future point, be very far away from the correct figures, maybe even go wrong. In reality, the predictive tools never exc...

  Technological requirements of big data Big Data management cannot be efficient if there is no powerful technology ground to rely on while handling a great number of different and changeable data. A first and important step toward that is scalable storage where technologies like the Hadoop Distributed File System (HDFS) or cloud-based storage offerings (e.g., AWS, Google Cloud, Azure) are the most frequently chosen solutions for storing both structured and unstructured data. In like manner, the power of processing to quickly deal with vast quantities of data is a must. Technological options, which are illustratively referred to as Apache Hadoop, Spark, and MapReduce, exist to accomplish parallel processing on distributed systems that are still potential enough to carry enormous workloads. The data integration software is an absolute must-have that can gather data from different sources, for example, IoT devices, web applications, and databases. Technologies like Apache Kafka, Apac...

Starbucks With the capability to reach an astonishing 90 million sales per week in 25,000 stores worldwide, Starbucks has reached a level near the mythical in recent years. The coffee retail brand fits big data and AI into various applications to aid them in their marketing, selling, and management. Principally, the coffee chain brand with a large market share utilizes a successful Loyalty Reward System and a mobile app to collect the purchase behaviours of millions of customers.  When analysing purchasing trends, BI tools allow customer data to be leveraged through regular delivery to mobile phones or email directly to app subscribers. These personalized offers are so attractive to the consumers and bring them to the stores at once. This knowledge of the customer also enables the company to create offers that are more personalized and hence more relevant to the customer's needs.  The very same information aids in the creation of offers and discounts that a client can easily r...