Assignment Big Data

 Pie charts are a way to show how different parts make up a whole. The chart is a circle that is divided into pieces, where each piece's a category. The size of each piece shows how much of the whole it is. People use pie charts a lot in business, education and marketing because they are easy to understand. For example a company might use a pie chart to show how much of the market they have or how their sales are distributed or how they are spending their money. Schools use them to show what students think or how well they are doing. One good thing about pie charts is that they give you an idea of how things are divided up. They are simple. Look nice and they are good for showing small groups of data. You can use things like Microsoft Power BI and Google Sheets to make pie charts. Pie charts are not so good when there are too many categories because the pieces get too small and hard to compare. It can also be hard to see differences, between the numbers. With these problems pie cha...

 Bar charts are really common when it comes to looking at data. They use bars to show information and the length of each bar is the value of what you're measuring. You can have bar charts that go across the page or up and down it just depends on what you're trying to show. I think bar charts are useful because they make it easy to compare things. For example a company might use a bar chart to see how well different products are selling.. A school might use one to compare how students are doing in their classes. Even the government uses bar charts to show things like how many people live in a place or how the economy is doing. Bar charts are great because they are easy to understand and simple to look at. They help people see what is going on with the data like what's the highest or lowest value or what is different between groups. There are programs like Microsoft Excel and Tableau that make it easy to create bar charts. Sometimes bar charts can be hard to read if there is ...

 Association rule mining is a way to find connections between things in sets of data. It helps companies see how certain things are related to each other. Companies use association rule mining in stores, online shopping and advertising. For example stores use something called market basket analysis to see what people buy together. If people often buy bread and milk at the time the store might put these things near each other or have a special deal on them. Amazon uses association rule mining to suggest things to buy The Apriori Algorithm is a way to do association rule mining. It looks for things that people buy together a lot and makes rules based on what people do. This helps companies sell things make better advertising plans and keep track of what they have in stock. Association rule mining can get complicated when looking at really big sets of data. It can make many rules and some of them might not be important. The quality of the data and how powerful the computers are also m...

 Clustering is a way to group things together in data. It is different from classification because it does not need categories that are already defined. Clustering finds patterns and similarities in data on its own. Businesses use clustering to understand what their customers are like. For example stores can group customers by age. What they like to buy. This helps them make marketing plans that're just right for certain customers. Clustering also helps social media sites suggest friends or things to look at based on what people do on the site. There are a ways to do clustering. One way is called K-Means Clustering, which puts data into groups. Another way is called Clustering, which makes groups that look like a tree. Clustering is used in fields, including healthcare recognizing pictures and doing research. The good thing about clustering is that it helps us find patterns in data that we might not see otherwise. This can make business plans better make customers happier and help ...

 Classification is something that people use a lot to look at data. It helps to put data into groups based on what we have seen before. We train a model with the data we already have. Then we use that model to figure out where new data fits in. Classification is used in different areas. For example in healthcare classification can help doctors figure out if a patient is sick based on what's wrong with them and what has happened in the past. In banking classification helps to find transactions that're not normal so we can stop bad people from stealing money. Email services use classification to keep spam emails out of our mailboxes and make sure we see the messages. We use computer programs like Decision Trees, Naive Bayes and Support Vector Machines to do classification. The good thing about classification is that it helps people make decisions faster and more correctly. Classification needs a lot of good data to work well. If the data we use to train the model is not complete ...

  Big data is used a lot in transportation systems to make traffic flow reduce congestion and make roads safer. Big data is collected from things like GPS devices, traffic cameras, road sensors, mobile apps and public transport systems. Then this big data is worked on using tools to find patterns and make decisions right away. Navigation apps like Google Maps use data to give people live traffic updates and suggest the fastest routes. They look at how fast traffic's moving, where accidents are happening and where roads are closed to help people get where they are going faster. City planners and governments use data to make transportation systems smarter. Traffic lights can be changed based on what is happening with traffic right now which helps reduce congestion. The schedules for transportation can be improved by looking at when people are traveling the most and when they need to get somewhere. Big data also makes roads safer by finding places where accidents happen a lot. The peo...

 Big data is really important for companies that sell things online. It helps them understand what customers like and do not like. It helps them make their businesses better. When people search for things click on things buy things write reviews and spend time looking at things on websites all of that information is collected. Then it is looked at closely to figure out what customers like. What is popular. One of the ways that big data is used is to suggest products to customers. For example Amazon uses data to show customers things they might want to buy based on what they have done on the website before. This helps Amazon sell things and it makes customers happier. Big data also helps companies show customers ads that're actually interesting to them. Big data is also very helpful for managing the products that companies have in stock. Companies can use data to guess how many of each product they will need so they do not run out or have too many. Companies can also use data to rea...

 Big data is used a lot in healthcare to make diagnosis better to make treatment better and to make hospital management better. Healthcare systems get a lot of data from things like health records, medical scans, wearable devices, lab results and systems that monitor patients. This data is really big and really complicated. We use big data tools like machine learning, data mining and predictive analytics to look at it. One big use of data is to find diseases early. For example we can use algorithms to look at a patients history and find people who're likely to get diseases like diabetes, heart disease or cancer. This means doctors can give them treatment early and they are more likely to survive. Big data is also used to make medicine personal, where the treatment is for that patient based on their genes and medical data. Hospitals use data to work better. It helps them guess how many patients will come in manage the beds make the waiting times shorter and make sure the staff are w...

  Big data analysis is well-suited for tackling a wide range of complex problems characterized by large volumes of data, high velocity, and diverse data types. Here are some types of problems ideally suited to big data analysis: Predictive Analytics: Big data analysis enables predictive modelling and forecasting by analyzing historical data to identify patterns, trends, and correlations. Predictive analytics is used in various applications, including demand forecasting, risk assessment, fraud detection, and predictive maintenance. Pattern Recognition: Big data analysis can uncover hidden patterns, anomalies, and trends within large datasets that may not be apparent through traditional analysis methods. Pattern recognition techniques, such as clustering, classification, and anomaly detection, are applied in areas such as image recognition, natural language processing, and cybersecurity. Recommendation Systems: Big data analysis powers recommendation systems that provide personali...

  Big Data analysis excels in scenarios where traditional data processing tools cannot handle the volume, velocity, and variety of information being generated. These problems typically involve extracting actionable insights from massive, complex, and fast-changing datasets. 1. Large-Scale Pattern Recognition When datasets span petabytes or more , such as in e-commerce clickstream analysis or social media sentiment tracking , Big Data tools like Apache Spark or Hadoop can uncover correlations and trends that smaller samples would miss. This is especially useful in fraud detection , where subtle anomalies in millions of transactions must be identified in near real-time. 2. Real-Time Decision Making Industries like finance , IoT monitoring , and cybersecurity require low-latency processing of high-velocity data streams. Frameworks such as Apache Kafka and Apache Flink enable instant analysis, allowing businesses to respond to market changes, detect intrusions, or adjust m...

 Promoting Digital Literacy and Ethical Data Use The widespread adoption of big data collection and analysis across industries has created major benefits for businesses, governments, and healthcare organizations by improving decision-making, increasing efficiency, and generating valuable insights. Organizations use big data to understand customer behavior, predict market trends, improve healthcare treatments, and optimize business operations. Despite these advantages, the growing use of big data has also raised serious ethical concerns that must be addressed to ensure responsible and fair use of information. One major ethical issue is privacy. Organizations often collect large amounts of personal information such as browsing history, financial records, location data, and social media activity. In many cases, users may not fully understand how their information is being collected or shared. This creates concerns about consent and data ownership, as individuals may lose control over ...

Reducing Algorithm Bias The widespread adoption of AI-powered business analytics applications has transformed decision-making processes across industries, enabling organizations to analyze massive datasets, automate operations, and generate predictive insights. Businesses increasingly rely on machine learning and big data analytics for customer profiling, credit scoring, fraud detection, hiring decisions, healthcare diagnostics, and personalized marketing strategies. While these technologies improve efficiency and competitiveness, they have also introduced major concerns related to algorithmic bias, data ethics, governance, and regulatory compliance. As AI systems become more influential in decision-making, ensuring fairness, transparency, and accountability has become a critical challenge for modern organizations. Algorithmic bias often occurs when AI models are trained using biased historical datasets, flawed assumptions, or incomplete information. These biases can produce discrimin...

 Improving Cybersecurity Systems The increasing reliance on big data in business intelligence (BI) systems has created significant opportunities for organizations to improve decision-making, increase operational efficiency, and gain competitive advantages. Businesses across industries now depend on large volumes of structured and unstructured data to identify trends, predict customer behavior, optimize supply chains, and improve overall performance. However, the rapid growth of big data has also introduced major cybersecurity risks that threaten the confidentiality, integrity, and availability of valuable information. These risks include data breaches, unauthorized access, ransomware attacks, insider threats, and advanced persistent threats that can disrupt operations and cause financial and reputational damage. This paper explores advanced cybersecurity strategies designed to protect big data within BI systems. One key approach is the use of artificial intelligence in cybersecurit...

Strengthening Data Privacy Laws One of the most important strategies for limiting the negative effects of big data is strengthening data privacy laws and regulations. Today, companies collect massive amounts of personal information from users through websites, apps, social media platforms, and online purchases. This information may include names, phone numbers, locations, search history, shopping behaviour, and even personal preferences. While businesses use this data to improve services and target advertisements, problems arise when data is collected without consent or sold to third parties. This can lead to privacy violations, identity theft, and misuse of personal information. Governments can reduce these risks by creating stronger laws that protect citizens’ personal data. A good example is the General Data Protection Regulation in the European Union, which gives people more control over how organizations collect and use their information. Similar laws can be introduced worldwide....

  Blog Post 44: Social Media, Business, and Society Social media platforms generate massive amounts of data every second. Every like, comment, share, search, and purchase creates information that companies can analyze. This has major effects on businesses and society. Businesses use big data to understand customer behavior. Companies can study what products people buy, what advertisements they click on, and what trends are popular. This helps businesses improve products and create personalized marketing strategies. Governments and organizations also use social media data to understand public opinion. During elections, natural disasters, or major events, big data helps leaders understand what people are discussing online. However, big data can also have negative effects. Social media platforms may spread false information quickly. Personal information can be collected without users fully understanding how it is used. Targeted advertising may also manipulate people’s decisions. Cyber...

  Blog Post 43: Big Data in Education Education has changed significantly because of big data. Schools, universities, and online learning platforms collect information about student attendance, grades, behavior, and learning progress. This data helps educators improve teaching methods and support students more effectively. One major advantage is personalized learning. Teachers can analyze student performance data to understand strengths and weaknesses. Students who struggle in certain subjects can receive additional help, while advanced learners can be given more challenging tasks. Online learning platforms also use big data to recommend courses and educational materials based on student interests and performance. This makes learning more flexible and efficient. Schools can also use big data for decision-making. Administrators can identify trends in student performance and improve school programs. Governments may also use educational data to improve national education systems. Howe...

  Blog Post 42: Big Data and Smart Cities Big data is helping cities become smarter and more efficient. Governments collect information from traffic systems, public transportation, electricity networks, water systems, and security cameras. This data helps city planners understand how cities operate and how services can be improved. One important use of big data is traffic management. Traffic sensors and GPS systems collect real-time information about road conditions. Governments can use this data to reduce traffic jams, improve public transportation routes, and decrease travel time for citizens. Big data is also useful for managing resources such as electricity and water. Smart meters can track energy usage and help reduce waste. Waste management systems can monitor garbage collection schedules to make services more efficient. Public safety is another area where big data is used. Security systems can analyze crime patterns and help law enforcement respond more quickly to emergencie...

  Blog 36: Data Security and Analytical Software Big data systems require strong security technologies and advanced analytical software to function properly. Since organisations collect sensitive information such as customer details, financial records, and healthcare data, protecting this information is extremely important. Cybersecurity technologies such as encryption, firewalls, and multi-factor authentication help prevent unauthorised access to data. Companies often use services from organisations like Palo Alto Networks and Cisco to improve protection. Analytical software is also necessary to interpret large datasets. Tools such as Tableau , Power BI , and SAS Analytics help businesses create charts, identify trends, and make decisions. Database management systems are also required to organise large datasets effectively. Without proper security and analytical tools, big data would create more risks than benefits. Therefore, these technologies are essential requirements for s...

  Blog 35: Fast Internet and Network Infrastructure A strong network infrastructure is another major technological requirement of big data. Large amounts of data are constantly transferred between devices, servers, and users around the world. Without fast internet connections and reliable networks, this process would become very slow and inefficient. Many companies rely on high-speed broadband, fibre-optic networks, and 5G technology to support rapid data transfer. For example, companies such as Cisco and Huawei develop networking equipment that supports data transmission. Data centres also require strong internal networks to move information between servers quickly. If network systems are weak, delays may occur during data analysis. The Internet of Things (IoT) also increases the demand for better networks. Smart devices such as fitness trackers, smart homes, and connected vehicles continuously generate large amounts of data that must be transmitted instantly. Reliable cybersecu...

  Blog 34: Powerful Data Processing Technologies Big data requires powerful processing technologies that can analyse large datasets quickly and accurately. Traditional computers may take days or even weeks to process massive amounts of information, which is why specialised systems are needed. One major technology used for processing big data is Apache Hadoop . Hadoop allows organisations to process huge datasets across many computers at the same time through parallel processing. This saves time and improves efficiency. Another important tool is Apache Spark . Spark is faster than many traditional systems because it processes data in memory rather than relying entirely on storage devices. It is widely used in industries such as finance, healthcare, and e-commerce. Artificial intelligence and machine learning technologies also support big data processing. These systems can automatically identify patterns, predict outcomes, and improve decision-making processes. Powerful processors, g...

  Blog 32: Big Data in Business – The Future of Decision Making Businesses are already using big data, but its future applications will take decision-making to a whole new level. Companies will rely even more on data to understand customers, improve products, and increase profits. One key application is customer behavior prediction . By analyzing browsing history, purchases, and preferences, businesses can predict what customers want before they even know it themselves. This allows for highly personalized marketing. Big data will also enhance automation and artificial intelligence . Companies will use data-driven systems to automate tasks, improve efficiency, and reduce human error. Another future trend is real-time decision making . Businesses will be able to instantly analyze data and make quick decisions, giving them a competitive advantage in fast-moving markets. Overall, big data will help businesses become smarter, faster, and more customer-focused, shaping the future of glob...

  Blog 31: Big Data in Smart Cities – Building the Cities of Tomorrow As cities grow, managing resources becomes more complex. Big data will play a key role in developing smart cities that are efficient, safe, and sustainable. One major future use is in traffic management . By analyzing real-time data from cameras, GPS, and sensors, cities can reduce traffic congestion, optimize traffic lights, and suggest faster routes to drivers. This will save time and reduce pollution. Big data will also improve energy usage . Smart grids will analyze electricity consumption patterns and distribute energy more efficiently, reducing waste and lowering costs. Another application is public safety . Data from surveillance systems and emergency services can help predict crime patterns and respond faster to incidents. In the future, smart cities powered by big data will provide better living conditions, reduce environmental impact, and improve overall quality of life for residents.

 Blog Post 29: Big Data in Smart Cities - Building the Future Big data plays a major role in developing smart cities. Governments collect data from traffic systems, sensors, and public services to improve urban living. One example is traffic management. Data from cameras and GPS systems helps reduce congestion by adjusting traffic signals and suggesting faster routes. This saves time and reduces pollution. Public transportation systems also use big data to optimize routes and schedules. Another application is energy management. Smart grids use data to monitor electricity usage and reduce waste. Cities can also manage water supply, waste collection, and emergency services more efficiently using data analytics. Additionally, big data improves public safety by analyzing crime patterns and helping law enforcement respond faster. In conclusion, big data is helping cities become more sustainable, efficient, and comfortable for residents, shaping the future of urban life.

 Blog Post 28: Big Data in Education - Enhancing Learning Experiences In education, big data helps improve teaching methods and student performance. Schools and online platforms collect data on student attendance, grades, and learning behavior. This data is then analyzed to identify strengths and weaknesses. For instance, adaptive learning systems use big data to customize lessons based on each student's pace and understanding. If a student struggles with a topic, the system provides additional resources or exercises. Teachers can also use data dashboards to monitor progress and provide targeted support. Big data also helps in predicting student outcomes. Schools can identify students at risk of failing or dropping out and take early action to support them. Additionally, universities use big data to improve curriculum design and learning strategies. As a result, big data is making education more personalized, efficient, and accessible for everyone.

  Blog 26: Big Data in Agricultural Science Agricultural science has improved through big data by helping farmers increase crop production and reduce waste. Sensors placed in farms collect information about soil quality, weather conditions, and crop health. Farmers use drones and satellite imagery to monitor crops and detect diseases early. Companies such as John Deere develop smart farming technologies that use data analytics. Big data also helps scientists develop better farming techniques to feed growing populations. Visualization: Smart Farming Data Tools Tool Purpose Drones Crop monitoring Sensors Soil analysis Satellites Weather tracking AI Systems Predict crop yield Big data continues to transform scientific research by helping scientists solve complex global challenges.

  Blog 25: Big Data in Space Science Space science produces enormous amounts of data through telescopes, satellites, and space missions. Scientists use big data tools to analyze planets, stars, galaxies, and other space objects. For example, NASA and European Space Agency collect data from space telescopes and Mars missions. The Hubble Space Telescope captures huge volumes of images and scientific data that researchers analyze to study the universe. Big data helps scientists discover new planets and better understand space phenomena. Visualization: Sources of Space Data Satellites Space telescopes Mars rovers Space probes Astronomy databases

  Blog 24: Big Data in Environmental Science Environmental scientists use big data to study climate change, pollution, and natural disasters. Satellites, weather sensors, and ocean monitoring systems generate massive amounts of environmental information daily. Organizations like NASA collect satellite data to monitor deforestation, rising sea levels, and global temperatures. Scientists analyze this information to predict natural disasters such as floods, hurricanes, and wildfires. Big data helps governments create environmental protection policies and disaster response plans. Visualization: Environmental Data Collection Sources Satellites Weather stations Ocean sensors Air pollution monitors Wildlife tracking systems

  Blog 20: Value – Importance of Data Analysis Value refers to the useful insights businesses gain from analyzing large datasets. Collecting data alone is not enough; organizations must convert data into meaningful information. For example: Netflix recommends movies based on user preferences Amazon predicts customer purchases Hospitals use patient data to improve treatments Big data helps organizations increase profits, reduce risks, and improve customer experiences. Visualization: Business Benefits Table Benefit Example Better Marketing Personalized ads Cost Reduction Automated systems Improved Decisions Data forecasting Customer Satisfaction Faster services This shows how businesses turn data into valuable outcomes.

  Blog 19: Veracity – Accuracy of Data Veracity refers to the reliability and quality of data. Poor-quality data can lead to incorrect business decisions. Problems affecting veracity include: Missing information Duplicate records False information Human errors Outdated data For example, fake social media accounts may provide inaccurate data to companies trying to understand customer behavior. Healthcare organizations must maintain accurate patient data to avoid medical mistakes. Visualization: Bar Graph: Common Data Quality Problems Problem Percentage Missing Data 30% Duplicate Data 25% Incorrect Data 20% Outdated Data 25% This chart highlights major issues affecting data quality.

  Blog 18: Variety – Different Types of Data Variety refers to the different forms of data collected by organizations. In the past, most data was structured and stored in tables. Today, businesses manage structured, semi-structured, and unstructured data. Examples include: Text messages Videos Images Emails Social media posts Audio recordings Sensor data Platforms like Instagram and TikTok generate mostly unstructured data such as videos and images. Visualization: Pie Chart of Data Types Structured Data → 20% Semi-Structured Data → 30% Unstructured Data → 50% This visual demonstrates the diversity of modern data.

  Blog 17: Velocity – Speed of Data Generation Velocity refers to how quickly data is generated, transferred, and processed. Modern businesses often require real-time analysis to make fast decisions. For example, Visa processes thousands of transactions per second, and X receives millions of posts daily. Stock markets also rely heavily on fast data processing because financial prices change instantly. Visualization: Real-Time Data Sources Social Media Posts → Every second Credit Card Transactions → Instant GPS Tracking → Continuous Online Purchases → Real-time This flow diagram shows how quickly modern systems receive information.

 Blog 15: Traditional data analysis focuses mainly on descriptive statistics, which means it explains what has already happened rather than predicting future outcomes. This limits its usefulness in strategic planning and forecasting. Businesses today require advanced insights such as predicting customer behavior, identifying trends, and detecting risks before they occur. Traditional tools lack the algorithms and computational power needed for predictive and prescriptive analysis. Moreover, they often cannot handle unstructured data like images, videos, or text. This restricts the depth and scope of analysis. Modern approaches, such as machine learning and artificial intelligence, overcome these limitations by providing deeper insights and more accurate predictions. Therefore, relying only on traditional data analysis can limit innovation and growth.

 Blog 14: One major limitation of traditional data analysis is its inability to provide real-time insights. Traditional methods often involve collecting data, storing it, and analyzing it later, which creates a time gap between data generation and decision-making. In fast-paced environments like finance, healthcare, and e-commerce, this delay can be costly. Businesses need immediate insights to respond to customer behavior, market trends, or potential risks. Traditional analysis cannot keep up with this demand, as it relies heavily on batch processing rather than continuous data flow. As a result, decisions based on outdated information may not be accurate or effective. Modern analytics systems solve this issue by offering real-time data processing, allowing organizations to act quickly and make better-informed decisions.

 Blog 12: Inferential Statistics Inferential statistics is a branch of statistics that goes beyond simply describing data and focuses on making predictions or drawing conclusions about a larger population based on a sample. Instead of analyzing every single data point, inferential statistics uses a smaller group (sample) to estimate characteristics of a bigger group (population). This is especially useful when it is not practical or possible to collect data from everyone. Common techniques in inferential statistics include hypothesis testing, confidence intervals, and probability analysis. For example, researchers may survey a small group of people to predict the opinions of an entire country. This type of analysis helps in decision-making, forecasting, and scientific research. However, because it involves estimation, there is always a level of uncertainty, which is why accuracy and proper sampling methods are important. Inferential statistics is widely used in fields such as healt...

 Blog 11: Descriptive Statistics Descriptive statistics is a branch of statistics that focuses on summarizing and describing the main features of a dataset. It helps to present data in a meaningful way so that it is easier to understand. Instead of analyzing the entire dataset in detail, descriptive statistics uses measures such as mean (average), median (middle value), and mode (most frequent value) to provide a clear summary. It also includes the use of graphs, charts, and tables, such as bar charts, pie charts, and histograms, to visually represent data. For example, a teacher might use descriptive statistics to summarize the test scores of a class and identify overall performance. This type of statistics does not make predictions or draw conclusions beyond the given data; it simply describes what the data shows. Descriptive statistics is important because it helps individuals and organizations quickly understand large amounts of information and identify patterns or trends. In c...

 Blog 10: Traditional Statistics Traditional statistics refers to the methods used to collect, organize, analyze, and interpret data in a structured way. Before the rise of big data, traditional statistics was the main approach used by researchers, governments, and businesses to understand information and make decisions. It typically involves working with smaller, structured datasets that are collected through surveys, experiments, or observations. Common tools in traditional statistics include charts, graphs, tables, and numerical summaries such as averages and percentages. These methods help simplify complex data and make it easier to understand patterns and trends. For example, a school may use traditional statistics to calculate the average score of students in an exam or to compare performance between different classes. Although traditional statistics is still widely used, it has some limitations, especially when dealing with extremely large or unstructured data. Despite this,...

  Blog 7: Big Data Today and the Future (2020s and Beyond) Today, big data continues to grow faster than ever before. Technologies such as artificial intelligence, the Internet of Things (IoT), and 5G networks are producing enormous amounts of information. Devices like smart watches, smart home systems, self-driving cars, and medical monitoring equipment constantly generate real-time data. Companies use this information to improve services and predict customer needs. For example, Netflix uses big data to recommend movies, while Amazon uses it to predict purchases and improve delivery systems. However, modern big data also creates concerns about privacy, cybersecurity, and ethical issues. Laws such as the General Data Protection Regulation were introduced to protect personal information. In the future, quantum computing may allow organizations to process even larger datasets at incredible speeds. Experts predict that big data will continue transforming healthcare, education, tran...

  Blog 6: Social Media and Cloud Computing Era (2010s) The 2010s saw explosive growth in big data due to social media platforms and cloud computing services. Websites such as Facebook , Instagram , Twitter (now X ), and YouTube generated billions of posts, photos, and videos daily. Smartphones also contributed significantly because users constantly created location data, messages, app activity records, and online purchases. Cloud services such as Amazon Web Services , Microsoft Azure , and Google Cloud allowed businesses to store huge amounts of data without building expensive physical data centers. Artificial intelligence and machine learning also became more common during this decade. Companies used big data to train algorithms that improved recommendation systems, fraud detection, and healthcare predictions. Governments also used big data for smart city planning, transportation systems, and national security monitoring. By the end of the 2010s, big data had become part of eve...