Impact of Big Data on Freedom and Privacy

With the rise of the technological revolution in social world and benefits it creates, comes also concerns and issues about the range of use of those technologies. The use of big data is just a small, but nevertheless, important issue in the common world and raises many questions such as the process of data collection, how much information is gathered about a person and how that information is being used, whether there is a balance between benefits and costs and finally, how does it affect our every-day lives.

From Plato, Hobbes and Locke to the present, the rights of liberty and free expression have been considered as fundamental human rights. The advent and incessant development of computer technologies have profoundly changed the existence of man; new technologies, as initial tool and aid of human activities, have determined a revolution, characterized by social change and the emergence of new interests and needs. The spread of technologies concerns the life of individuals and organizations, influencing daily activities, relationships, scientific progress, social and economic growth, human mindset. After the explosion of the digital revolution due to the diffusion of personal computers and the Internet, nowadays one of the problems is posed by the increased use of big data.

Because data protection and privacy are commonly highlighted issues in recent history, this paper will research the impact of big data to such fundamental rights as freedom and privacy, questioning the relationship between law and morality concerning the use of data.

Big Data, Small Freedom

First of all, there is a need to understand what big data is and what are the implications for societal behavior, rooting the analysis in the political philosophy as part of the larger process of postmodernity. Our generation is truly defined by technology which changed the role in social life.

Technology has turned into a means of control. Everything we do in the digital world leaves behind a footprint of our choices, preferences and hobbies.

Quoting Aristotle, “man is a social animal”, which means that he is naturally led to establish social relations with his similes. The Internet is a place where a multitude of relations can be created, becoming, in the digital era, one of the main places where people keep in touch. Therefore, it is through a good govern of innovation technology that there is a possibility to construct a more secure world, where man can express himself fully.

The word ‘big data’ refers to the number of technologies and analysis methodologies of massive data from which states, businesses and people with interest, try to examine and to correlate in order to find some kind of relationships between different phenomena, to forecast future events and to market behavior. Traditional privacy is, thus, threatened: today, large multinational companies are rushing to grab more data as possible, both in explicit and implicit ways, from citizens-users who do not know to have been manipulated. In mostly of this big data market, transparency and ethical constraints are not included. It is clear that whether a lot is known about us, the less we are free in our choices before being made by others.

Logic questions come up in our mind: ‘Has our thinking been hacked?’, ‘Does big data restrict individual freedom?’. We can be defined as digital slaves: due to the manipulation of information, a sort of prison for our thinking is built, limiting de facto choice freedom and invading our privacy with the goal to earn from our known psychological weaknesses.

For instance, how many of us do know that our medical records can be sold? Although they are considered confidential because of the private information they contain, sometimes and for different reasons they can be released without a patient’s consent. By asking and under restricted rules, entities with scopes to do research or to conduct other education functions, can obtain them.

Big Data, Big Risk

Big data and artificial intelligence are undoubtedly important innovations. As assumed, big data is a sort of new tool to get power and to affect our lives, but this power is useless without a moral framework which should promote users’ protection and guarantee the rights of privacy and security, a sort of new ‘social contract’ in Hobbes’ words, where people would agree to accept new rules because of the mutual benefit, choosing rationality over their natural selfish instincts.

If on the one hand technological development has contributed to facilitate the dissemination of data and information and to improve state-citizens relation, on the other hand it has subdued people to several risks caused by lack of regulation of these new digital tools as well as adequate and aware knowledge of their use. While being a connective tissue of the new technological society, it could be a hypothetical and extremely dangerous instrument of subjection: citizens have been exposed to a series of damaging vulnerabilities. Moral responsibility in using data should need an ethical code of conduct. Nevertheless, from the perspective of legal positivism, Herbert Lionel Adolphus Hart emphasized on the separability of law and morality. Today, law is called to legislate in technological sector itself because of the need to safeguard the rights and to heal conflicts caused by human digital existence, increasingly an integral part of real life. Moreover, from Mill’s point of view, there is the necessity that acts and rules be assessed using a utilitarian calculus where the good and bad of big data are weighed on a scale.

As recently confirmed by the EU General Data Protection Regulation, risk assessment models play an increasing role in data protection. Indeed, personal data security assessment is a complex activity not only because of the consequences resulting from an accidental loss or theft, but also because one has to avoid provision of sensitive data which could lead to a sort of control of users and, thus, to a limitation of freedom in our so called ‘society of information’. Big data may incorporate information that infringes upon people’s privacy, giving firms information that they do not intend to collect. Less known is who buying data companies are: for example, in Vermont, thanks to a new law, the first of its kind, willing companies have to register with the Secretary of State. Be conscious of digital security social relevance and of the dangers of artificial intelligence brings more physical security for individuals.

We are all in the focus of institutional surveillance. How do people can achieve self-development, if they are not totally free in their own decisions? As Immanuel Kant noted, to reach that aim, one has to have control over his life and self-determinate himself, possessing the right to know and control what others know about him. Existing constraints define a despot society and an incompatible use of data with current democracy. Transparency of the aims for which personal data are used should be a requirement. Indeed, whenever people feel free to make decisions for themselves, in general regarding their data uses, in particular regarding who can use it and how much they can know about us, a higher level of trust in institutions can be attained. To ensure greater privacy and to prevent discrimination, all morally wrong and offensive human dignity uses of data, for instance unauthorized and harmful ones, have to be punished by law. Furthermore, authorities have to ensure that the central aims of data use are to foster the peaceful coexistence of humanity and to create a fair system of social coexistence based on the principles of fairness, equality and justice, as said by Hobbes. Big data requires an examination of those that have control over big data, in order to reach the good common to all qua members of the society, which in this case is nothing more than the highest level of security possible.

Conclusion

Even if digital revolution is in full swing, this process has already produced upheavals which are there for all to see. Because there is a gap between the interests of consumers and those who are using big data as a tool for their own benefit, there will continue to be a tension and no common ground for both parties because both interests will be protected unless limits to the use of big data will be imposed by law and the misuse of collected data will have serious legal consequences. On the other hand, it should be as much of a big responsibility for consumers to not make themselves vulnerable to the misuse of their data. Possibly one of the issues is the lack of knowledge about data collection simply because this digital revolution has evolved and is still continuing to evolve so rapidly that it raises more questions than answers which is the issue that should be addressed and resolved globally.

Why Do I Want to Be a Data Scientist Essay

In a world increasingly driven by data, a simple spreadsheet first sparked my fascination. As a teenager, I remember being captivated by how a collection of numbers could tell a compelling story, influencing decisions in business, science, and even daily life. This early encounter laid the foundation for my aspiration to become a data scientist. This field, a blend of statistics, computer science, and critical thinking, is where I see my skills and passions converging. In this essay, I will explore my journey towards this aspiration. I will discuss my motivation, highlighting how specific experiences have shaped my love for information technology. I will then delve into my educational and professional background, illustrating how each step has prepared me for a career in this dynamic field. I will also outline the specific skills and strengths I bring to the table and how they align with the information technology demands. Finally, I will share my goals and aspirations, reflecting on the impact I aim to make in the industry and the broader society through my work in information technology.

Personal motivation and passion

My journey toward information technology began not in a classroom but in the family business. As a child, I found it fascinating how my parents used straightforward data to make strategic decisions. From tracking sales to forecasting trends, they relied on numbers to guide them. This early exposure to the power of data in shaping business outcomes ignited my interest in the field.

This interest transformed into a passion as I delved deeper into information technology. What excites me most about this field is its unique blend of problem-solving and innovation. The idea of sifting through vast amounts of data to uncover patterns and insights that are not immediately obvious is thrilling. It’s like being a digital detective, where each clue unravels a part of a more giant, complex puzzle.

Moreover, the impact of information technology on decision-making is profound. The ability to drive change based on empirical evidence rather than intuition alone sets data science apart. It’s not just about handling big data; it’s about translating it into actionable insights with real-world implications, from optimizing business processes to influencing public policy.

The dynamism of the field is another aspect that captivates me. Information technology continuously evolves with technological advancements, presenting endless opportunities for learning and growth. This constant evolution ensures that my journey in information technology will be filled with continuous learning and adapting, keeping my passion for the field evergreen.

Educational and professional background

My educational path has been strategically tailored to build a strong foundation for a career in information technology. I pursued a Bachelor’s degree in Computer Science, which equipped me with essential programming, algorithms, and systems design skills. The coursework in statistical analysis and machine learning was particularly enlightening, providing me with a critical understanding of how data can be harnessed and interpreted. To further specialize, I completed a Master’s degree in Data Science. This advanced program deepened my knowledge of big data analytics, data mining, and predictive modeling, which are crucial for a data scientist.

Professionally, my journey has been equally enriching. I interned at a tech startup, where I was part of a team that developed a data-driven recommendation system. This experience was instrumental in understanding the practical applications of information technology in business. Following my internship, I worked as a Data Analyst at a renowned e-commerce company. Here, I honed my data visualization and statistical inference skills, regularly transforming complex data sets into actionable insights that informed marketing strategies and customer engagement models.

Each step in my educational and professional journey has solidified my interest in data science and provided me with a diverse skill set. From theoretical knowledge to practical application, my background has prepared me to embark on a career as a information technology, ready to tackle the challenges and opportunities that lie ahead in this dynamic field.

Skills and strengths 

In the realm of information technology, my technical skills are both varied and robust. I am proficient in programming languages essential for data analysis, such as Python and R, and I have extensive experience with SQL for database management. My expertise extends to utilizing data visualization tools like Tableau, allowing me to present complex data in an understandable and impactful way. I am also skilled in machine learning techniques, having applied algorithms for predictive modeling in various projects. A solid foundation in statistical analysis complements this technical toolkit, ensuring that my data interpretations are accurate and meaningful.

Beyond these technical skills, my soft skills are equally integral to my effectiveness as a data scientist. Critical thinking is at the core of my approach, enabling me to analyze problems deeply and develop innovative solutions. My communication skills are honed to translate technical data insights into clear, actionable recommendations for diverse audiences, bridging the gap between data and decision-making. Teamwork is another strength — collaborating with cross-functional teams on various projects. This collaboration has enhanced the quality of my work and fostered a diverse, inclusive, and productive work environment. These soft skills, coupled with my technical proficiency, make me a well-rounded candidate for a career in data science.

Goals and aspirations

As I embark on my professional journey in information technology, my immediate goals are twofold. Firstly, I aim to join a forward-thinking organization where I can apply my skills in real-world scenarios, focusing on projects that leverage data to drive innovation and efficiency. I am particularly interested in areas like artificial intelligence and predictive analytics. My second short-term goal is to continue enhancing my technical expertise, especially in advanced machine learning techniques and big data technologies. I plan to achieve this through ongoing education and hands-on experience, ensuring that my skills remain on the field’s cutting edge.

Looking towards the long term, I aspire to emerge as a leader in information technology. I envision myself leading a team of data professionals, driving strategic initiatives that harness the power of data for impactful decision-making. I am passionate about the potential of data science to transform industries, improve societal outcomes, and drive economic growth. Ultimately, I aim to contribute significantly by developing innovative data strategies or contributing to groundbreaking research. My long-term vision is to witness data science’s evolution and actively participate in shaping its future.

The bigger picture 

The profound effects that data science can have on various industries and society motivate me in the overall scheme of things. I aim to contribute to information technology by developing solutions that drive business innovation and address more considerable societal challenges, such as healthcare improvements, environmental sustainability, and enhancing educational methodologies. I envision my work in data science as a means to business efficiency and a tool for positive social change.

In this ever-evolving domain, I am committed to continuous learning. Staying abreast of emerging technologies, methodologies, and industry trends is vital. I plan to engage in ongoing professional development, participate in industry conferences, and contribute to professional communities. This commitment to lifelong learning is not only a personal goal but a professional necessity, ensuring that my contributions to the field of information technology remain relevant and impactful.

Conclusion

In conclusion, a combination of personal passion, rigorous education, varied professional experiences, technical and soft skills, and a clear set of goals has fueled my journey to becoming a data scientist. My path has been characterized by a deep appreciation for the power of data and a dedication to using this tool for positive impact, from early influences to long-term aspirations. My aim is not only to excel in the technical aspects of information technology but also to contribute meaningfully to industries and society. I am poised to make significant contributions to the ever-evolving information technology landscape by staying committed to continuous learning and adapting to new challenges. As I stand on the cusp of this exciting career, I am enthusiastic about the opportunities ahead and ready to embark on a journey that promises both professional growth and the potential to drive real-world change.

Using Big Data to Reduce Dropout in Schools

Dropouts in school is a big issue, because there is always a reason behind it that can be solved, if even we manage to identify this reason, and that’s precisely the challenging part. Education is very important, in any society. It enables to learn and develop our skills and competencies, to have a better job, it makes us self-dependent, helps to be socially integrated, enhance one’s confidence, and it makes the world a safer and better place. So, managing to link academical issues with unfavorable external factors or environments thanks to recent technology, would have an important positive impact on, of course, the individual itself, but also the society as a whole, and it would represent an important innovation in the schooling system.

But there are limitations to big data, such as the availability of big data, because we do not have data for everything. Plus, “in addition to issues of availability, we also typically only have access to certain kinds of data. Big data is essentially capturing and examining patterns, and typically it tells us more about what people actually do, but even if it is of great value, this is not sufficient for all kinds of social science research. We also need to understand the meanings of that behavior which cannot be inferred simply from tracking specific patterns” (Rebecca Eynon, ‘The Rise of Big Data: What Does It Mean for Education, Technology, and Media Research?’, pp.237-240).

So how and in which fields, can social entrepreneurs, using big data help reducing dropouts in schools? In this essay, using research methods such as interviews, I will show how social entrepreneurs in the educational sector can act and implement management implications into schools by using big data to improve educational methods, develop new programs and find and face the problems of struggling students to avoid dropouts. First, I will talk about how big data enables to customize school programs for each student, then how to find the right teaching method thanks to surveillance technology and how big data helps analyzing behavior and do predictive analysis to increase efficiency, and finally how it can also be used for career predictions.

Unique Individuals with Their Own Datasets

Indeed, every person has its own personality, hobbies, likes, dislikes and also learning preferences. Of course, teachers already try to adapt their learning style to everybody, but in a class, everyone is still so much different that differences will persist, and some will have more difficulties than others, and some, on the contrary, will get bored, etc. Of course, the more students there is in one class, the more this phenomenon will be observed and problematical.

Big data allows to have information about each student’s individual intellectual capacities, personal knowledges but also about its hobbies and personal life. So, this information can be used to offer a personalized learning process for each student. Students would more be seen as unique individuals, than as a ‘class’. Big data would create a new schooling and teaching system, far from the actual obsolete tailored one. Thanks to big data, the teachers easily have access to students’ data and statistics. So, they can intervene rapidly to compensate student’s weaknesses. Indeed, teachers have a real-time trace of each student’s performances, so they can help them more easily to improve their results and notes.

But in order to put this in place, schools need have a way to analyze each student and find out how they learn, their strengths and weaknesses. And this has to be made on a long-term period, with other specific factors, to have the most accurate and trustworthy information. The more data educators have, the more tools they have to help their students.

The Right Teaching Method Thanks to Surveillance Technology

One of the reasons why some students fail and drop out of school can also be due to some classes and inappropriate teaching methods. But it is hard to evaluate a teacher’s class and effectiveness, as the only measurement is the student’s involvement, participation and grades in that class. But now, thanks to that surveillance technology, teacher could have access to data that actually show if their classes and teaching methods, and more specifically, which lectures or which activities are appropriate and working, or if not, which ones aren’t and why. This data can, for instance, show how well teacher manage to keep their student’s attention during their class, or how long it took for a student to start getting correct answers, or also identify at which moment a student started to be lost or confused. These kinds of information could help teacher to know where they can improve, and this analysis also permit to get performance statistics. Indeed, this would motivate teachers to improve or find alternative ways to capture the attention of their students and make the lecture be more interesting for them, because they would know specifically why and when something is not working or less efficient.

Big Data That Analyses Behavior

Behavior can also tell a lot about a student’s situation, and whether or not she is going to potentially drop out of school or not. So, big data will enable to gather information from the school and combine them in order to understand one’s behavior. For instance, if a student has problems at home such as with his/her parents, financial troubles, or if he/she gets bullied at school or simply has a lack of support at home or at school, those are factors that will very likely have consequences on the student’s behavior and academic success. Also, some of the most common psychological issues students face today are substance abuse, learning disabilities, attention/hyperactivity disorders, and autism-spectrum disorders. Today, educators simply make some assumptions about the causes of one’s behavior, but now with big data they could have real trustworthy sources to back up their theories. Indeed, big data enables to gather enough information and analysis to find trends that educators could use to intervene.

Predictive Analysis to Increase Efficiency

You can start making prediction analysis when you have collected data from a long enough period. Short-term data cannot give information accurate enough to predict a solid, reliable future. For example, thanks to surveillance data, teacher can notice that during certain periods (for instance, before holidays) students are less concentrated and more distracted. So, during these periods, teachers should adapt and make lectures plans to get the most out of this time to bring students to focus. It would, for example, not be the right time to start a new topic because students will be distracted and therefore miss some important information about the beginning, so, the basis of the new topic, and this will probably be needed to be catch up later, and so, a loss time. It would be better to do reviews or activities created to help students concentrate more easily. Big data could also be used to predict if a student could potentially have difficulties or fail a subject. The more information is collected on a student, at school, at home, his/her personal background, the easiest it will be to foresee if he/she is going have difficulties in certain classes.

Career Predictions

Understanding a student’s behavior and managing to make him/her concentrated and interested in lectures is a really good thing. But, if a student still doesn’t understand what this could bring to them in the future and what they could do afterwards, it would not make much sense for him/her and still could be a factor for dropping out of school. Therefore, if social entrepreneurs would bring big data into school not only for the academical part but also future, destiny and career-oriented part, it would assuredly help students find an aim to what they are currently learning and strongly motivated them.

This will be possible thanks to big data through analyzing almost all the characteristics of a person, not only academical, but also personal. And by combining those, we could find what lecture the students paid the best attention in, their abilities and limits, what they liked and disliked the most, what kind of careers they are interested in, why and what would actually best fit to them. Plus, as they would sooner or later have to make a choice regarding not only career, but also potentially higher education, this deep and complete analysis of individual data will give accurate and very helpful answers that would help and guide them to make the right choice. Furthermore, social entrepreneurs could also use big data to predict future needs on the job market. For instance, a study shows that 85% of the jobs in 2030 do not exist today. So, big data could prepare students to future jobs that are not even existing now, and this can also give them hope by showing that the availability of jobs in the current market is not an exhausted list, and that they might be made for something that is not to be found in the market yet, but would be needed in the future. Indeed, big data could inform and anticipate predictive needs for a future job market. This would enable students to better visualize the future market they will be working in, learn and focus in consequence, and not just according to simple, unprecise assumptions.

Conclusion

Education is one of the most important sectors in the society, it shapes the future. Therefore, many social entrepreneurs invest in education and dedicate their time and work in order to improve the school system, which is a priority. And improving the education system means using all the tools available like the latest advanced technology such as big data. This enables social entrepreneurs and educators to use their time more efficiently, and to be more productive and innovative, while finding new methods to improve the educational system by finding cause to effect relation in order to reduce or avoid dropouts. So, big data could be a very useful, if not an essential tool in schools, because it enables teachers and educators in one click to have access to a wide range of school driven, but also personal data about each student. This enables to act fast and efficiently in order to customize programs and orient in the best way each student individually and avoid dropouts. Indeed, we retain from big data that it is a real-time follow up of student’s performances which helps them to improve their results and interests by acting more intelligently and by detecting, and so, avoiding problems. But one of the important limits of big data is the ethical issue. Indeed, some consider that big data is good for many things like market studies and science, but that it doesn’t deserve its place in education, because of privacy issues. Indeed, they consider that privacy and big data cannot coexist, because the data is so big that everyone is personally identifiable from multiple angles, even once you remove ‘personally identifying information’. This means it is ultimately a threat to pupils and students. But if this privacy policy barrier is removed, and so, respected, there is definitely a place for big data in education. The question is, will schools get the funding they need to implement it? Without methods to accurately measure data and compile it, with professionals, social entrepreneurs to implement and analyze it, big data will never find a place. But if it does, the educational experience could be revolutionized and improved greatly.

Using Big Data to Reduce Dropout in Schools

Dropouts in school is a big issue, because there is always a reason behind it that can be solved, if even we manage to identify this reason, and that’s precisely the challenging part. Education is very important, in any society. It enables to learn and develop our skills and competencies, to have a better job, it makes us self-dependent, helps to be socially integrated, enhance one’s confidence, and it makes the world a safer and better place. So, managing to link academical issues with unfavorable external factors or environments thanks to recent technology, would have an important positive impact on, of course, the individual itself, but also the society as a whole, and it would represent an important innovation in the schooling system.

But there are limitations to big data, such as the availability of big data, because we do not have data for everything. Plus, “in addition to issues of availability, we also typically only have access to certain kinds of data. Big data is essentially capturing and examining patterns, and typically it tells us more about what people actually do, but even if it is of great value, this is not sufficient for all kinds of social science research. We also need to understand the meanings of that behavior which cannot be inferred simply from tracking specific patterns” (Rebecca Eynon, ‘The Rise of Big Data: What Does It Mean for Education, Technology, and Media Research?’, pp.237-240).

So how and in which fields, can social entrepreneurs, using big data help reducing dropouts in schools? In this essay, using research methods such as interviews, I will show how social entrepreneurs in the educational sector can act and implement management implications into schools by using big data to improve educational methods, develop new programs and find and face the problems of struggling students to avoid dropouts. First, I will talk about how big data enables to customize school programs for each student, then how to find the right teaching method thanks to surveillance technology and how big data helps analyzing behavior and do predictive analysis to increase efficiency, and finally how it can also be used for career predictions.

Unique Individuals with Their Own Datasets

Indeed, every person has its own personality, hobbies, likes, dislikes and also learning preferences. Of course, teachers already try to adapt their learning style to everybody, but in a class, everyone is still so much different that differences will persist, and some will have more difficulties than others, and some, on the contrary, will get bored, etc. Of course, the more students there is in one class, the more this phenomenon will be observed and problematical.

Big data allows to have information about each student’s individual intellectual capacities, personal knowledges but also about its hobbies and personal life. So, this information can be used to offer a personalized learning process for each student. Students would more be seen as unique individuals, than as a ‘class’. Big data would create a new schooling and teaching system, far from the actual obsolete tailored one. Thanks to big data, the teachers easily have access to students’ data and statistics. So, they can intervene rapidly to compensate student’s weaknesses. Indeed, teachers have a real-time trace of each student’s performances, so they can help them more easily to improve their results and notes.

But in order to put this in place, schools need have a way to analyze each student and find out how they learn, their strengths and weaknesses. And this has to be made on a long-term period, with other specific factors, to have the most accurate and trustworthy information. The more data educators have, the more tools they have to help their students.

The Right Teaching Method Thanks to Surveillance Technology

One of the reasons why some students fail and drop out of school can also be due to some classes and inappropriate teaching methods. But it is hard to evaluate a teacher’s class and effectiveness, as the only measurement is the student’s involvement, participation and grades in that class. But now, thanks to that surveillance technology, teacher could have access to data that actually show if their classes and teaching methods, and more specifically, which lectures or which activities are appropriate and working, or if not, which ones aren’t and why. This data can, for instance, show how well teacher manage to keep their student’s attention during their class, or how long it took for a student to start getting correct answers, or also identify at which moment a student started to be lost or confused. These kinds of information could help teacher to know where they can improve, and this analysis also permit to get performance statistics. Indeed, this would motivate teachers to improve or find alternative ways to capture the attention of their students and make the lecture be more interesting for them, because they would know specifically why and when something is not working or less efficient.

Big Data That Analyses Behavior

Behavior can also tell a lot about a student’s situation, and whether or not she is going to potentially drop out of school or not. So, big data will enable to gather information from the school and combine them in order to understand one’s behavior. For instance, if a student has problems at home such as with his/her parents, financial troubles, or if he/she gets bullied at school or simply has a lack of support at home or at school, those are factors that will very likely have consequences on the student’s behavior and academic success. Also, some of the most common psychological issues students face today are substance abuse, learning disabilities, attention/hyperactivity disorders, and autism-spectrum disorders. Today, educators simply make some assumptions about the causes of one’s behavior, but now with big data they could have real trustworthy sources to back up their theories. Indeed, big data enables to gather enough information and analysis to find trends that educators could use to intervene.

Predictive Analysis to Increase Efficiency

You can start making prediction analysis when you have collected data from a long enough period. Short-term data cannot give information accurate enough to predict a solid, reliable future. For example, thanks to surveillance data, teacher can notice that during certain periods (for instance, before holidays) students are less concentrated and more distracted. So, during these periods, teachers should adapt and make lectures plans to get the most out of this time to bring students to focus. It would, for example, not be the right time to start a new topic because students will be distracted and therefore miss some important information about the beginning, so, the basis of the new topic, and this will probably be needed to be catch up later, and so, a loss time. It would be better to do reviews or activities created to help students concentrate more easily. Big data could also be used to predict if a student could potentially have difficulties or fail a subject. The more information is collected on a student, at school, at home, his/her personal background, the easiest it will be to foresee if he/she is going have difficulties in certain classes.

Career Predictions

Understanding a student’s behavior and managing to make him/her concentrated and interested in lectures is a really good thing. But, if a student still doesn’t understand what this could bring to them in the future and what they could do afterwards, it would not make much sense for him/her and still could be a factor for dropping out of school. Therefore, if social entrepreneurs would bring big data into school not only for the academical part but also future, destiny and career-oriented part, it would assuredly help students find an aim to what they are currently learning and strongly motivated them.

This will be possible thanks to big data through analyzing almost all the characteristics of a person, not only academical, but also personal. And by combining those, we could find what lecture the students paid the best attention in, their abilities and limits, what they liked and disliked the most, what kind of careers they are interested in, why and what would actually best fit to them. Plus, as they would sooner or later have to make a choice regarding not only career, but also potentially higher education, this deep and complete analysis of individual data will give accurate and very helpful answers that would help and guide them to make the right choice. Furthermore, social entrepreneurs could also use big data to predict future needs on the job market. For instance, a study shows that 85% of the jobs in 2030 do not exist today. So, big data could prepare students to future jobs that are not even existing now, and this can also give them hope by showing that the availability of jobs in the current market is not an exhausted list, and that they might be made for something that is not to be found in the market yet, but would be needed in the future. Indeed, big data could inform and anticipate predictive needs for a future job market. This would enable students to better visualize the future market they will be working in, learn and focus in consequence, and not just according to simple, unprecise assumptions.

Conclusion

Education is one of the most important sectors in the society, it shapes the future. Therefore, many social entrepreneurs invest in education and dedicate their time and work in order to improve the school system, which is a priority. And improving the education system means using all the tools available like the latest advanced technology such as big data. This enables social entrepreneurs and educators to use their time more efficiently, and to be more productive and innovative, while finding new methods to improve the educational system by finding cause to effect relation in order to reduce or avoid dropouts. So, big data could be a very useful, if not an essential tool in schools, because it enables teachers and educators in one click to have access to a wide range of school driven, but also personal data about each student. This enables to act fast and efficiently in order to customize programs and orient in the best way each student individually and avoid dropouts. Indeed, we retain from big data that it is a real-time follow up of student’s performances which helps them to improve their results and interests by acting more intelligently and by detecting, and so, avoiding problems. But one of the important limits of big data is the ethical issue. Indeed, some consider that big data is good for many things like market studies and science, but that it doesn’t deserve its place in education, because of privacy issues. Indeed, they consider that privacy and big data cannot coexist, because the data is so big that everyone is personally identifiable from multiple angles, even once you remove ‘personally identifying information’. This means it is ultimately a threat to pupils and students. But if this privacy policy barrier is removed, and so, respected, there is definitely a place for big data in education. The question is, will schools get the funding they need to implement it? Without methods to accurately measure data and compile it, with professionals, social entrepreneurs to implement and analyze it, big data will never find a place. But if it does, the educational experience could be revolutionized and improved greatly.

Analysis of Walmart Activity Using Big Data

Introduction:

Selling goods or services to customers through several channels and to earn a profit is a complex concept of knowledge and skills. Walmart stands first in the world as a retailer serving more than 324 million customers with 20,000 stores in 28 countries. Whether it is in-store purchases or social mentions or any other online activity, Walmart has always been one of the best retailers in the world. Walmart ensures its best service to the customers by keeping customer satisfaction as its highest priority.

On the other hand, it is also well-known for its successful operation in more than 10 websites, being operated all over the world. On knowing these facts, it is evident that Walmart handles an enormous amount of data varying in its types and sources of it.

Though the retailers satisfy demand identified through a supply chain, data plays an important role in making decisions based on facts, trends, and statistics. Thus, the business must be able to float through the noise of the increased size of data and extract the right information so that they can make the right and best possible strategy. Also, data helps to understand and improve the business by reducing the wastage of time and money.

Likewise, modern Walmart’s business marketplace is a data-driven environment and it is in the process of building the world’s largest private cloud, capable of coping with 2.5 petabytes of data every hour.

Here are more details of how the data is being collected, stored, transformed, and used by Walmart in this report.

Types of data:

As mentioned earlier Walmart operates with more than 20,000 stores, serving more than 1.1 million customers per hour. Any data that is extracted out of these stores(online and offline) and customers are stored in a Neo4j(reference 1) database consuming 200 billion rows, ‘representing data of only a few past weeks’. The information collected has more than one source, including meteorological data, economic data, telecom data, social media data, product price and details of local events. Walmart has an immense amount of data at its fingertips. Vast and varied datasets are used to tackle all the data and to generate a real-time solution in microseconds through designed algorithms.

Chart 1: Overview of Data Sources.

As it is observed from chart 1, Walmart’s data universe is filled with varieties of data from three different categories.

1. Online data:

The Walmart registered mobile app users are more than 26 million as of June 2016. It is observed that mobile application users spend 40% more compared to those customers in stores. (reference 2)Another interesting fact is that more than half of the customers enter the Walmart store with a smartphone leading to another opportunity for Walmart to promote its digital and online payments. Therefore, Walmart makes future predictions and decisions based on the records extracted through the data of all mobile and website-registered customers. A record is a set of email ID, contact numbers, addresses, usernames and passwords so all the details provided by the customers are authenticated by Walmart before saving the records.

On the other hand, social media has inexplicable followers for Walmart with 1.1millions on Twitter, 2.3 million on Instagram and 32 million on Facebook. It has become one of the most important platforms for marketing, advertising and preparing a strategy for Walmart’s business.

Overall, mobile applications, websites, and social media contribute to the data stored by Walmart in building its business strategies.

2. Offline Data:

Walmart’s in-store data has a wide variety of sources. Some of them are, sales tracking through the scanned UPCs, Item stocks and inventories through off-shelf and on-shelf items count, data on lost and shrink items, PLU for the cashier’s usage, item discovery and it’s positioning through the tele-zone usage, credit, and debit card track records, Walmart master card registrations, user details collected due to western union transactions and employee everyday login records.

Streamlining customer experience, Walmart also collects data on in-store customers through connection to its Wi-Fi connection spots, and tracks and stores data from call centers, feedback and experiences.

3. Others:

Walmart has a great relationship with weather forecast companies to predict climate data for years so they make likely correlations between weather and store sales on a zip code level. Even when those correlations make no obvious sense sometimes still it might be useful in most of the cases. Walmart makes better business decisions with the available weather data to transform the customer experience.

Some of the facts that declare that Walmart operates with big data:

  • a typical supercenter sells approximately 46 million items in store.
  • with 12 million items in the store compared to 35 million products in the online store.
  • serves 1.1 million customers per hour. [exhibiting the high velocity in ETL of the data]
  • operated with the help of 2.2 million employees.
  • Walmart is successful in handling such huge data also with the global net sales amounted to about 500.34 billion U.S. dollars being one of the largest employers in the world. reference 3 for the facts and charts.

Based on the above statistics Walmart stores massive volumes of data for their operations and forecasts.

Types of big data analytics techniques:

The analytics systems at Walmart cover millions of products and customers from different sources. Thus, the system analyses around 100 million keywords on a daily basis to optimize each keyword. implements the following technologies in handling big data.

A single day’s data in Walmart account to multiple terabytes of new data. And, historical data sums up to a few petabytes making it complex and large data to be analyzed.

Hence, to handle such massive data, the big data ecosystem came into the picture with the collection of infrastructure, applications and analytics. The main objective of analyzing this ecosystem is to optimize the shopping experience for customers when they are in a Walmart store, or browsing the Walmart website or mobile devices.

Here we understand how Walmart brings data analytics and data mining techniques in practice.

Data analytics:

Firstly, it is a fact that Walmart made use of Big Data since the technology came into existence. A migration from 10 node Hadoop cluster to 250 node Hadoop cluster by Walmart in 2012 was a stepping stone to have a transition from its aged methodologies to newborn technical knowledge. This transition corroborated to combine more than five different websites into one single website successfully to let a new latest cluster collect all the generated unorganized data.

A transformation from the relational database to the Neo4j database(reference 1), a graph database was much in need since the relational database did not satisfy the requirements, due to the complexity of the queries.

To know more about Neo4j, it is a graph database that could quickly query customers’ purchases and real-time recommendations. With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph.

In the very beginning of Walmart’s journey with Hadoop, they developed few applications as mentioned below.

  • A mapping application that lets its users identify even the tiniest product’s location in the store along with the store’s location.
  • eReceipts application provides customers with electronic copies of their purchases.
  • Savings Catcher application alerts the customers whenever its competitor reduces the cost of an item the customer already bought. This application then sends a gift voucher to the customer to compensate for the price difference.

Map update application:

To process and generate big data with parallel, distributed algorithms on a cluster. A cluster here is a set of connected computers that work together which can be viewed as a single system.

Map update application workflow

Walmart developed Mupd8 to tackle issues like performance and scalability while dealing with real-time processing applications which could emphasize on the quality of generated data.

Mupd8 allows developers to write applications easily and process them using the Map Update framework, an easy way to express streaming computation. Writing an application as a combination of customized map and update operators, big data developers can focus on the business logic of the application and let Mupd8 handle load and data distribution across various CPUs.

To address high availability, low latency and scalability, the current Mupd8 architecture leverages open-source solutions, including Cassandra. To conclude, at Walmart Mupd8 platform has already supported more than a dozen sophisticated stream applications processing over 300 million status updates per day, gathering real-time information (reference 4).

Data mining:

Data mining helps Walmart find patterns that can be used to provide product recommendations to users based on which products were bought together or which products were bought before the purchase of a particular product. Effective data mining at Walmart has increased its conversion rate of customers.

Moreover, Walmart tracks and targets every consumer individually. It gathers information on what customers buy, where they live, and what products they like through in-store Wi-Fi. The big data team at Walmart Labs analyses every clickable action on Walmart.com. To conclude, all these events are captured and analyzed intelligently by big data algorithms to recognize meaningful big data insights for millions of customers.

Actions taken by Walmart using Big data – (references 5 & 6)

1. Social media survey:

Big data helped Walmart launch new products into market analysis based on data collected from social media. For example, social media data let Walmart understand that the users were interested in ‘Cake Pops’. This data allowed Walmart to increase its sales by stocking the Cake Pops quickly in stores. Likewise, the likes and dislikes of customers concerning every product were well understood through big data analytics.

2. Scan & go:

A focus by Walmart in the past 2.5 years has been to improve the checkout process for 140 million customers each week. The Walmart Pay app rolled out nationwide last year allowed the consumer to scan their phone at checkout instead of having to pull out cash or credit cards. Scan and Go allows consumers to skip the checkout lane and is being tested in Wal-Mart’s U.S. stores, and is completely rolled out at Sam’s Club.

“By using predictive analytics, stores can anticipate demand at certain hours and determine how many associates are needed at the counters. By analyzing the data, Walmart can determine the best forms of checkout for each store: self-checkout and facilitated checkout,” Wal-Mart noted in the post.

3. Customized recommendations:

​Walmart’s big data algorithms provide recommendations to its customers based on the purchase history analysed through credit card purchases.

4. Social media big data solutions:

A big part of Walmart’s unstructured, informal and generally ungrammatical data-driven decision is based on social media data- Facebook comments, Pinterest pins, Twitter Tweets, LinkedIn shares and so on. WalmartLabs is leveraging social medial analytics to generate retail-related big data insights.

Through the Social Genome analytics solution which combines public data from the web, social media data and proprietary data like contact information, email address and customer purchasing data, this data helps Walmart is reaching customers or friends customers who tweet or mention something about the products of Walmart to inform them about the product and provide them a special discount.

5. Inventory management at Walmart using predictive analytics:

Inventory plays a vital role in Walmart’s triumph. Walmart reduces losses, risk and wastage that takes place due to overstocking. Thus, big data helped Walmart to have a well-planned inventory management system that helps it stay stocked according to the in-demand products.

This, in turn, facilitated suppliers to plan the inventory supply based on the real-time data. So, both the retailer and supplier are benefited by saving funds.

6. Mobile big data analytics solutions:

Mobile users in Walmart account for 1/3rd of its traffic every year and Mobile phone customers are extremely important to Walmart as smartphone shoppers spend 77% more in-store.

Walmart is leveraging big data analysis to develop predictive capabilities on their mobile app. The mobile app lets a customer use the geofencing feature of Walmart’s mobile app. This app asks the user to enter into the “Store Mode” when the shopper enters the store. The store mode of the mobile app helps users to scan QE codes for special discounts on products they would like to buy.

7. Consumers in the produce department:

Walmart is using big data and IoT sensors to find out how long people laze around in the fresh produce department. This analysis has helped them find that if the fresh produce looks fresh enough then people loiter for longer and this is the secret to make customers buy more things from the Walmart stores.

8. Lists:

It’s an old practice that people make paper lists before heading to the shopping. Walmart attempts to translate paper lists to digital tools which have not yet been widely embraced by shoppers. However, this new list feature of Walmart using data analytics lets users enter items in a natural language like ‘popcorn’ and then this app pushes a limited number of brands and lets the user choose a specific product. And the lists are integrated to the new store maps, each item can be located down to the four-foot-wide section on the shelf.

9. Restore & returns:

The shopper purchase data is integrated and managed in the app, giving authority to the user to scan the paper receipt using which the shopper will be able to initiate a return process within the app, select an item purchase from the history and create a barcode on the mobile device. Where in which this code can be presented at the customer service counter and simply drop off the return. Then the customer gets acknowledged regarding the return. However, there is no extensive usage of this application currently.

10. Use market basket analysis to classify shopping trips:

Walmart enhances customer experiences by understanding their store visits based on different trip types. Regardless of whether a customer is on the last minute run or taking a stroll down the store. Classification of this kind helps Walmart enhance the customer shopping experience. However, these trip types are created based on previous purchase history.

Conclusions & views:

Minimizing the operating costs: Data analytics in Walmart helps in its planning, scheduling, and control of activities which are involved in transferring goods from its warehouses to the stores. Thus, data analytics assists Walmart to best position its warehouses nearer to its stores minimizing operational costs.

Advanced supply chain management: By Walmart’s best practices in supply chain management, and technological advances such as the barcode and RFID, it has assured that it remains competitive regarding its prices in the market.

Dealing directly with suppliers: Walmart stores details of how many products need to be shipped and produced. Walmart presented it as a way for suppliers to partner with it to improve efficiency in inventory management and meeting customer needs. Walmart shifts its shipping responsibility of inventory management to the supplier.

References:

  1. https://neo4j.com/case-studies/walmart/
  2. https://books.google.ca/books?id=7-aWDwAAQBAJ&lpg=PA289&ots=AB3iF9T7h7&dq=telecom%20data%20walmart&pg=PA286#v=onepage&q&f=false
  3. https://www.statista.com/statistics/183399/walmarts-net-sales-worldwide-since-2006/
  4. http://walmartlabs.blogspot.com/2012/09/mupd8-walmartlabs-real-time-platform.html
  5. https://www.retaildive.com/news/7-ways-walmart-is-innovating-with-technology/525154/
  6. https://futurestoreseast.wbresearch.com/walmart-harnessing-technology-to-create-next-gen-superstore-ty-u

Big Data and Its Impact on International Trade

The main aim of the essay is to explain what big data highlighting the key opportunities and challenges for associated with using this system, as well as how it affects international trade. International business is the exchange of goods and services among individuals and businesses in multiples countries, such as when multinational companies or international business engage in businesses with different countries. Globalization as allowed business to trade international markets which as very positive to as they increased their target segmentation, however as a result companies were exposed to more information, they were able to fare thus requiring a system which was able to store and manage all that material which leads to the creation of big data. Furthermore, I will provide my opinion, on weather I think big data is beneficial or harmful for international trade by presenting facts, statistics and articles.

Big data is a term usually applied to substantial data sets that are generated through a range of sources including mobile devices, electronic medical records, environmental and body sensors, imaging and laboratory studies, and administrative claims data (Barton, A.J. 2016). Big data is the combination of the last 50 year of technology evolution (Kaufman, 2013). This data can be both categorized in structured or unstructured, internal or external. With this information businesses start to recognize patterns of consumer activity that had before would be impossible to understand or act upon. Structured data refers to data fields with social security numbers, phone numbers or even ZIP codes which may be human-machine generated RDBMS (relational database management system) structure. The format is easily searchable both with human generated questions and via algorithms using type of data and field names, such as numeric, alphabetical or, currency or date (Brandauer, S., 2018).

Unstructured data is fundamentally every other type of data. Unstructured data it may be textual or non-textual, and human- or machine-generated. As well as be kept within a non-relational database like NoSQL. Usual human-generated unstructured data includes text files (spreadsheets, presentations, and email), social media (Facebook, Twitter and LinkedIn data), websites (YouTube, Instagram, photo sharing sites) etc., different from machine-generated unstructured data that includes satellite imagery (land forms, military movements, and weather data), scientific data (atmospheric data, oil and gas exploration, space exploration), sensor data (oceanographic sensors traffic, weather).

“Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making” (Raguseo, 2018). Big data is categorized in four type such as strategic which is the ones who change the way the companies operate or the nature of their products, informational which provide knowledge and material that improves the decision making, transactional which helps businesses cutting costs incurred and transformational refer to the results of changes that a firm has to make to the structure and to the capacity of implementing a technological investment each one having different perks.

The most essential perks of big data are the enables international companies to gather, manage, and use abundant amount of data at a fast speed, at any time which allowed to gain the right insights into their customer behavior. This information is exceptionally significant to companies as it will dictate the approach they will take to create and manly promote their products and services.

Business manage data in different ways depending on their necessity and to try solving specific types of data management problems.

Opportunities of Big Data for International Businesses

For start-up businesses or businesses looking to trade internationally, the adoption of big data technology offers several types of benefits which will assist them in making decisions for the business success. The following are:

  1. Cost reduction. One of the main benefits of implementing big data for firms trading internationally is related to the financial aspects. Big data brings significant cost advantages when it comes to storing large amounts of data reducing burden in the company IT department which can free resources, as well as they can identify more efficient ways of doing business. Although the implementation of this technology will be expensive in the beginning, but eventually they will save a substantial sum of money. E.g. one way big data helps to reduce costs is by cutting logistics costs, on average, the cost of product returns is 1.5 times that of the actual shipping, thus by firms integrating big data analytics they can identify the goods that are most likely to be returned allowing them to easily calculate the likelihood of products being returned and take the necessary steps to reduce losses and costs. Additionally, another department companies using big data can reduce costs is in their marketing strategies. Big data technologies allow companies to understand customer behavior thus knowing through which channels it will most effective to launch marketing campaigns (Heidrich, J., 2016).
  2. Faster and better decision. Big data allows businesses to analyses high amount of information, short period of time. The system leads to more accurate decisions, continuous productivity improvements through automation, leaner operations, and optimized servicing through predictive analytics as well as reducing risks of operations. Since as by analyzing big data, company management gets a better understanding of the state of foreign market condition, e.g. if market is booming or declining, market gap, families disposable income. Through this information companies can make a calculated decision on whether to leave or move to a new market. For example, give an example of industries that are booming in developed countries which several countries are planning to invest. Amazon is an example that company which as identified through big data that India is a booming economy that encourage them to invest heavily in the country. Currently India it’s the sixth on the International Monetary Fund (IMF) world GDP rankings, with a GDP of over $2.26 trillion Shri K. K. Bajaj 2017). Amazon announced a they would make a $2bn investment in India as well as announced plans to build five new fulfilment centers in Indian cities (The Bookseller, 2014). The company entered this venture as their statics suggest they will have success as the economy is growing, population has more disposable income thus they will be able to have high sales figures leading to more profit.
  3. New products and services. Big data lets firms know the most recent trends of customer needs and satisfaction through analytics thus can create products according to it. Furthermore, by having better insights on customer’s wants business will increase their possibility of earning additional revenue. For example, one company which as seen the positive results of implementing big data technology brings its Wal-Mart’s as after of implementing sematic analysis search engine, Polaris which is a platform designed to produce relevant search results for customers. The implementation of the semantic search has increased the possibility of online customers finalizing their purchase by 10% to 15%.
  4. Predictive analytics. Big data allows companies to create predictive analytics which helps them arrive new techniques to become more profitable. As big data gathers, processes and spits out information that may be helpful and useful, thus predictive analytics uses that information, as well as insight into past buying patterns or replies to emails or likelihood of non-payment and uses that historical data to predict future behavior. Predictive analytics can help international businesses distribute resources, to the clients with the biggest likely return.

Challenges of Big Data for International Businesses

  1. Need for talent. As big data is a very difficult system to manage there is a shortage of specialists, who know how to operate these systems. Businesses may waste lots of time and resources on systems they don’t even know how to operate. There is a lack of big data skill set thus companies hiring or training staff can increase costs considerably, and the process of acquiring big data skills can take considerable time. Because without a clear understanding of big data, projects become increasingly risky and doomed to failure.
  2. Data quality. Researching and gathering the right information is another big downside of big data. Companies have to be extra careful when selecting information in order to making sure they are using accurate, relevant for their analysis, as a result this slows considerable the reporting process being disadvantageous, however if they don’t address these data quality issues businesses may find, the research results generated by their analytics are worthless and even harmful if acted upon.
  3. Privacy and security issues. These are the most prevalent risks companies using big data have to face, making venerable of cyberattacks. Companies are vulnerable to these types of attacks as they storing sensitive data, which forces companies to spend a lot resources in the security and protecting their data. For example, Facebook suffer a cyberattack where 30 million users had their information stolen, 14 million of which had their names, contact details and sensitive information such as their gender, relationship status and recent location check-ins exposed (Rodriguez, 2018). As a result of this security breach Facebook was forced to increase the security of their data by hiring 10,000 people. Big data is affected by state privacy laws, especially privacy laws to directly address online disclosures and record keeping. General Data Protection Regulation (GDPR), went into effect replacing the 1995 Data Protective Directive (DPD). This new law affects the EU and countries in the European Economic Area (EEA), and creates a new regulation for privacy in the digital age (because of Brexit the United Kingdom has a separate Data Protection Act 2018 that mirrors the rules in the GDPR). If the UK leaves the EU in March 2019 with no agreement surrounding data protection & data transfers, the UK Government has stressed, “there will be no immediate change in the UK’s own data protection standards. This is because the Data Protection Act 2018 would remain in place and the EU Withdrawal Act would incorporate the GDPR into UK law to sit alongside it” (Blanchard. S., September 2018). In terms of big data, the GDPR limits the type of data gathered by organizations. It also creates certain issues for data collection because individuals have the right to have their information removed from databases even after giving permission to have it include. The law has far-reaching effects because it not only affects organizations within the EU, but also applies to organizations offering goods or services to people residing in the EU (Myers. C., June 11, 2018).
  4. Regulations compliance. Another downside of using big data for companies is that they have to comply with all regulations implemented different government in rules companies have to follow with big data which every country they trade. Much of the information used by businesses is very sensible and personal to individuals thus firms may need to ensure that they are meeting industry standards or government requirements when handling and storing the data.

Conclusion

This essay has outlined the opportunities and challenges of the use of big data technology, and the adoption of it by multinationals companies. Through the paper we can understand what big data is to substantial data sets that are generated through a range of sources including mobile devices, electronic medical records. Due to the usefulness of this system businesses adopted it aiming to achieve better results as it presents several benefits such as is the enhancement of productivity growth, goals in terms of efficiency, reduction of the operating costs, and enhancement of the returns on financial assets, ability of proving better products and services, improving the internal processes of a company and overall the development of new business opportunities however the using big data presents its drawbacks such as satisfying different regulations around the world in the countries where the big data originated from, privacy and security issues, privacy risks, data quality and lack of information system structure support.