Changing the way you learn

Big Data.

Evolution of Big Data
1. Origins (1960-2000)
  1. Technological Evolution: The first data processing systems, such as punched cards, allowed the management of large volumes of information in government and business projects.
  2. Digital Data Growth: The Emergence of Databases relational databases (RDBMS) and the development of the World Wide Web increased data generation and storage.
2. Development (2000-2010)
  1. Exponential Data Increase: The rise of the internet and social networks, such as Facebook and Twitter generated a massive amount of digital data.
  2. Big Data Technologies: Technologies such as Hadoop were developed to manage and process large volumes of data efficiently.
3. Big Data Era (2010 to present)
  1. Machine Learning Integration: Companies began to use algorithms advanced and machine learning techniques to extract valuable information from large data sets.
  2. Applications in Key Sectors: Big Data became essential for the decision making in sectors such as health, finance and marketing
4. Big Data in e future
  1. Integration with IoT and Edge Computing: Real-time data collection and analysis from Connected devices will improve efficiency and instant decision making.
  2. Ethics and Data Privacy: Organizations must implement policies and technologies to protect sensitive information and ensure the ethical use of data.
Implications of Big Data
1. Business
  1. Marketing y Ventas: Análisis de datos de clientes para personalizar campañas y mejorar la segmentación de mercado.
    1. Data analysis tools such as Google Analytics and CRM (Customer Relationship Management) to analyze the customer behavior and personalize campaigns.
  2. Supply chain management: Inventory and logistics optimization by analyzing data in real time
    1. Programs such as SAP and Oracle allow inventory optimization and Logistics through real-time data analysis.
  3. Finance: Fraud detection and financial risk analysis.
    1. Predictive analysis tools such as SAS and machine algorithms learning to detect fraud and evaluate financial risks.
2. Health
  1. Diagnosis and Treatment: Analysis of large volumes of data physicians to identify patterns and improve diagnoses.
    1. Medical data analysis software such as IBM Watson Health Helps identify patterns and improve diagnoses.nd improve diagnoses.
  2. Medical Research: Using data to discover new therapies and medications.
    1. Platforms such as Bioinformatics and analysis tools genomics to discover new therapies and drugs.
  3. Hospital Management: Optimization of resources and improvement in patient care through operational data analysis.
    1. Hospital Information Systems (HIS) and Operational Data Analysis to optimize resources and improve patient care.
3. Government
  1. Public Policies: Data analysis to design more effective and evidence-based policies.
    1. Data analysis tools like Tableau and Power BI to design evidence-based policies.
  2. Resource Management: Monitoring and optimization of the use of public resources.
    1. Resource management systems (ERP) for monitor and optimize the use of public resources.
  3. Public Safety: Using data to prevent crime and improve emergency response.
    1. Predictive analytics software and surveillance systems for prevent crime and improve emergency response
4. Security
  1. Cybersecurity: Detection of threats and vulnerabilities through the analysis of data patterns.
    1. Security platforms like Splunk and FireEye to detect threats and vulnerabilities by analyzing data patterns.
  2. Data Protection: Implementation of measures to ensure the privacy and security of information.
    1. Encryption technologies and data management programs for ensure the privacy and security of information.
  3. Fraud: Identification and prevention of fraudulent activities in real time.
    1. Machine learning algorithms and real-time analysis to identify and prevent fraudulent activities.
5. Privacy
  1. Regulatory Compliance: Ensure that safety practices Data handling complies with privacy regulations.
    1. Compliance management tools like OneTrust to ensure that Data handling practices comply with privacy regulations.
  2. Transparency: Ensure that users understand how their data is used.
    1. Consent management programs and communication platforms for ensure that users understand how their data is used.
  3. Protection of Personal Data: Implementation of technologies and policies to protect sensitive information.
    1. Encryption technologies and security policies to protect sensitive information.
Main Definitions of Big Data
1. Speed
  1. The speed with which data is generated and processed. This includes the real-time analysis of data from IoT sensors and financial transactions.
2. Volume
  1. It refers to the enormous amount of data generated and stored. Examples include social media data, electronic transactions, and transaction records. sensors.
3. Variety
  1. Diversity of data types, such as structured data (databases), not structured (images, videos) and semi-structured (XML, JSON).
4. Veracity
  1. Data quality and accuracy. It is crucial to ensure that the data is reliable and accurate to obtain valid results in the analysis.
5. Worth
  1. Useful information obtained from data analysis. The goal is to transform large volumes of data into valuable insights for decision making.
Motivations of Big Data
1. Competitiveness
  1. Improved Decision Making: Companies use Big Data to analyze large volumes of information and make more informed and faster decisions, which gives them a competitive advantage in the market
2. Innovation
  1. Development of New Products and Services: Data analysis allows us to identify new market opportunities and needs, facilitating the creation of products and services innovative.
3. Efficiency
  1. Process Optimization: Big Data helps identify inefficiencies and optimize operational processes, which can reduce costs and improve productivity.
4. Personalization
  1. Personalized Experiences: Companies can use data to offer personalized experiences for your customers, improving satisfaction and loyalty.
5. Strategic Decision Making
  1. Risk and Opportunity Assessment: Big Data analysis allows you to evaluate risks and opportunities more accurately, supporting long-term strategic decision making term.

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	Created by Luis David Guanga Chavez over 1 year ago