Lesson 1-3: Introduction to Data Security & Analytics PDF

LESSON 1: INTRODUCTION TO DATA SECURITY INTRODUCTION TO DPA (DATA PRIVACY ACT) REPUBLIC ACT NO. 8792, SEC.32, ALSO KNOWN AS THE E-COMMERCE ACT - DTI ADMINISTRATIVE ORDER NO. 8: Defining guidelines for the protection of personal data in information private sector YEAR 200...

LESSON 1: INTRODUCTION TO DATA SECURITY INTRODUCTION TO DPA (DATA PRIVACY ACT) REPUBLIC ACT NO. 8792, SEC.32, ALSO KNOWN AS THE E-COMMERCE ACT - DTI ADMINISTRATIVE ORDER NO. 8: Defining guidelines for the protection of personal data in information private sector YEAR 2001: The first time they drafted the law under the legal and regulatory committee of the former information technology and e commerce council (I-TEC) JUNE 6 2012 - the Philippine congress passed the Senate Bill no.2965 and House Bill no, 4115. The reason why it took too long to pass the bill was due to lack of awareness of identity theft and other data theft issues Former president Benigno S Aquino III signed the RA no. 10173 or Data Privacy Act of 2012 on August 15, 2012 What is the right of Privacy Act - The right to let alone. RA 10173 - DATA PRIVACY ACT - the law that seeks to protect personal information of users and individuals. KEY TERMS: PERSONAL INFORMATION (PI): Examples: name contact number birthdate address gender citizenship gender IP address citizenship status place of work place of birth payroll and benefits SENSITIVE PERSONAL INFORMATION (SPI): Examples: proceeding case tax return website visited SSS number information issues by materials downloaded Licenses revocation or the government grievance information suspension bank/car number leave of absence reason DIFFERENCE BETWEEN PI AND SPI: Personal Information is data that can identify a person directly or indirectly. Sensitive Personal Information are data that are so sensitive that if ever leaked, it would lead to harm, loss and unauthorized access.  PRIVILEGED INFORMATION: Data that are received within the context of a protected relationship. Examples: Husband - Wife Priest - Penitent Attorney - Client Doctor - Patient DATA SUBJECT: It refers to an individual whose personal information is processed. DATA PROCESSOR: refers to any natural or judicial person qualify to act as such under this act to whom a personal information controller may outsource the processing of personal data pertaining to a data subject. DATA PROCESSING SYSTEM: refers to any operation or any set of operations performed upon personal information. YOUR OBLIGATIONS: OBLIGATION #1: Adhere to data privacy principle Transparency Legitimate Purpose Proportionality OBLIGATION #2: Uphold data subject right information correct data profitability object erase file a complaint access damage OBLIGATION #3: Implement secure measures Organizational Technical Physical DATA PROTECTION AND POLICIES: Organizational Security Measures: Sample Procedures and Policies: Appoint DPO Email Create policies and procedures Access to control Records of Processing Act BYOD Management of HR Duty of Confidentiality Contract with PIPS or PICS Storage and Retention Policy Technical Security Measures: Physical Security Measures: Encryption Facility Access Passwords Floor plan design Firewalls Transfer of Electronic data Audit logs Personnel Back-ups Natural Calamities 5 PILLARS OF DPA: 1. Appoint a data protection officers 2. Conduct a privacy 3. Create a Privacy Management Program 4. Implement Data Privacy and Security Measures 5. Be ready in case of data breach LECTURE 2: INTRODUCTION TO ANALYTICS DEFINITION Analytics refers to the systematic analysis, interpretation, and visualization of data to derive meaningful insights and support decision-making. It involves the use of various statistical, mathematical, and computational techniques to process and transform raw data into valuable information that can be used to understand patterns, trends, and relationships within the data. OBJECTIVES 1. Describe: Summarize and describe data in a meaningful and informative way. 2. Predict: Use historical data to forecast future outcomes or trends. 3. Prescribe: Provide recommendations or actions based on analysis to achieve desired outcomes or optimize processes. HISTORY OF ANALYTICS Early History: The origins of analytics can be traced back to ancient civilizations like the Babylonians and Egyptians who used basic counting and record-keeping systems for trade, taxation, and other administrative purposes. Statistical Analysis and Probability Theory: The 17th century saw significant advancements in probability theory and statistics. Mathematicians like Blaise Pascal and Pierre-Simon Laplace laid the foundations for statistical analysis and probability, which are fundamental to modern analytics. Industrial Revolution: The Industrial Revolution (late 18th to early 19th centuries) marked a shift towards data-driven decision-making in industries. Data collection and analysis became essential for optimizing manufacturing processes and improving efficiency. Early Computing and Data Processing: In the mid-20th century, the advent of computers and computing technologies revolutionized data processing. Pioneers like Herman Hollerith (inventor of the punch card system) and Grace Hopper (COBOL programming language) played significant roles in early data processing and programming. Decision Support Systems (DSS): In the 1960s and 1970s, the concept of Decision Support Systems (DSS) emerged, focusing on providing analytical tools and technologies to support managerial decision-making. DSS integrated data analysis, modeling, and simulations to aid decision-makers. Business Intelligence (BI): In the 1980s and 1990s, the term "Business Intelligence" gained popularity. BI involved the use of software and technologies to collect, analyze, and present data for better business decision-making. OLAP (Online Analytical Processing) and data Big Data and Advanced Analytics: The early 21st century witnessed a surge in data generation, leading to the emergence of "Big Data." Advanced analytics, including machine learning, artificial intelligence, and predictive modeling, gained prominence in extracting insights from vast and diverse datasets. Current Trends and Future Prospects: Today, analytics is a critical component of various domains, such as finance, marketing, healthcare, sports, and more. Real-time analytics, data visualization, AI-powered analytics, and a focus on ethical data usage are among the latest trends in the analytics landscape. IMPORTANCE OF ANALYTICS 1. Informed Decision-Making: Analytics provides data-driven insights, enabling organizations and individuals to make informed, evidence-based decisions. This leads to better strategies and outcomes. 2. Operational Efficiency: By analyzing processes and operations, organizations can identify inefficiencies and optimize workflows. This can result in cost savings, improved productivity, and streamlined operations. 3. Competitive Advantage: Analyzing market trends, consumer behavior, and competitor activities allows organizations to stay ahead of the competition. By understanding the market dynamics, companies can tailor their products, services, and strategies accordingly. 4. Customer Understanding and Personalization: Analytics helps in understanding customer preferences, behavior, and needs. This knowledge enables businesses to personalize products, services, and marketing strategies, enhancing customer satisfaction and loyalty. 5. Risk Management and Fraud Detection: Analytics helps in assessing risks in various domains, such as finance and insurance. It allows organizations to predict and mitigate risks and detect fraudulent activities effectively. 6. Innovation and Product Development: Through data analysis, organizations can gain insights into market demands and consumer preferences. This information aids in the development of innovative products and services that cater to specific market needs. 7. Healthcare Improvements: In the healthcare sector, analytics can improve patient care, optimize resource allocation, predict disease outbreaks, and enhance medical research by analyzing vast amounts of health-related data. 8. Resource Allocation and Optimization: Analytics helps in optimizing the allocation of resources like human capital, finances, and assets. This ensures that resources are allocated efficiently, maximizing returns and minimizing waste. 9. Supply Chain Management: Analyzing supply chain data can help organizations optimize inventory levels, reduce costs, enhance logistics, and improve overall supply chain efficiency.  10. Government and Public Services: Governments use analytics to optimize resource allocation in public services, predict and prevent crime, improve public health, and enhance citizen services. 11. Environmental Sustainability: Analytics can be employed to analyze data related to energy consumption, waste generation, and environmental impacts. This enables the development of sustainable strategies and practices. 12. Continuous Improvement: By analyzing performance metrics and feedback, organizations can continuously improve their processes, products, and services to meet changing market demands and customer expectations. ADVANTAGES OF ANALYTICS 1. Informed Decision-Making: Analytics helps in making informed and data-driven decisions, minimizing reliance on intuition or guesswork. 2. Improved Efficiency and Productivity: By identifying bottlenecks and optimizing processes, analytics enhances operational efficiency and productivity within organizations. 3. Cost Savings: Optimization and efficiency improvements often lead to cost savings in resource allocation, inventory management, marketing, and more. 4. Better Customer Understanding: Analytics helps in understanding customer preferences and behavior, enabling businesses to tailor their offerings and marketing strategies for better customer satisfaction. 5. Competitive Advantage: Organizations that effectively leverage analytics gain a competitive edge by identifying market trends, predicting consumer behavior, and adapting strategies accordingly. 6. Risk Management and Fraud Detection: Analytics aids in identifying and mitigating risks, whether in financial investments, cybersecurity, or fraud detection, providing a more secure environment. 7. Innovation and Product Development: Analyzing market data and consumer feedback helps in innovating and developing products or services that meet specific market needs and preferences. 8. Real-Time Insights: Advanced analytics allows for real-time monitoring and insights, enabling rapid responses to changes in market conditions or other variables. 9. Healthcare Advancements: In the healthcare sector, analytics improves patient care, drug development, disease management, and resource allocation. 10. Environmental Sustainability: Analytics supports initiatives for environmental sustainability by providing insights into resource consumption, emissions, and strategies for reducing the environmental footprint. DISADVANTAGES OF ANALYTICS 1. Data Privacy and Security Concerns: The collection and analysis of vast amounts of personal and sensitive data raise concerns about privacy breaches and unauthorized access to data. 2. Bias and Misinterpretation: If not handled carefully, analytics can introduce biases based on the data selected or the algorithms used, leading to misleading or incorrect interpretations.  3. Overreliance on Data: Overemphasis on data can sometimes stifle creativity and critical thinking, as decisions may be solely driven by what the data suggests rather than considering broader factors. 4. Implementation Challenges: Implementing analytics systems and tools can be complex, requiring significant investments in technology, infrastructure, training, and ongoing maintenance. 5. Data Quality Issues: Poor-quality or incomplete data can lead to inaccurate results and faulty decision-making, highlighting the importance of data quality management. 6. Lack of Skill Sets: The demand for skilled data analysts and data scientists often outstrips supply, creating a talent gap in the field of analytics. 7. Ethical Dilemmas: Analytics raises ethical questions related to the appropriate use of data, potential biases, and the impact of decisions on individuals and society. 8. High Initial Costs: Setting up analytics infrastructure and implementing advanced analytics tools can have a high upfront cost, especially for small businesses. MAJOR TYPES OF ANALYTICS 1. Descriptive Analytics: Descriptive analytics involves analyzing historical data to understand past events and trends. It helps in summarizing what has happened in the past and provides context for understanding the present. 2. Predictive Analytics: Predictive analytics uses statistical algorithms and machine learning techniques to analyze historical data and make predictions about future outcomes or trends. It helps in forecasting potential future scenarios based on patterns observed in the data 3. Prescriptive Analytics: Prescriptive analytics goes beyond predicting future outcomes and recommends actions to achieve desired results. It suggests optimal courses of action to meet specific objectives by considering various possible scenarios. USED OF ANALYTICS IN MARKETING 1. Customer Segmentation: Analytics helps identify distinct customer segments based on demographics, behavior, preferences, and purchasing patterns. This segmentation enables targeted marketing strategies to tailor products, services, and promotions for specific groups. 2. Customer Behavior Analysis: Analyzing customer interactions, website visits, clicks, and purchases provides insights into customer behavior. Marketers can optimize the user experience, identify conversion barriers, and enhance engagement based on these insights. 3. Market Basket Analysis: Analytics helps identify which products are frequently purchased together, aiding in cross-selling and upselling strategies. Understanding these patterns can optimize product placements and promotional offers. 4. Campaign Effectiveness: Analyzing marketing campaigns allows marketers to measure their effectiveness, including click-through rates, conversion rates, and ROI. This analysis helps refine future campaigns for better performance. 5. Customer Lifetime Value (CLV) Prediction: Analytics predicts the potential value a customer will bring to a business over their lifetime. It informs marketing strategies to acquire and retain high-value customers while optimizing marketing budgets.  6. Sentiment Analysis: Utilizing natural language processing (NLP), sentiment analysis gauges public opinion and attitudes towards a brand, product, or campaign by analyzing social media, reviews, and online conversations. This insight helps in reputation management and brand sentiment. 7. Personalized Marketing: By analyzing customer data, businesses can create personalized marketing campaigns, recommendations, and offers that align with individual preferences, increasing engagement and conversion rates. 8. A/B Testing: Analytics allows for controlled experiments (A/B tests) to compare different versions of marketing materials, such as emails, advertisements, or website layouts. Marketers can determine which version performs better and make data-driven decisions for optimization. 9. Channel Attribution Modeling: Analytics helps in understanding the contribution of various marketing channels (e.g., social media, email, ads) to conversions. This insight guides budget allocation and strategy adjustments to maximize ROI. 10. Predictive Modeling for Lead Scoring: Analytics predicts which leads are most likely to convert into customers. Marketers can prioritize and focus their efforts on leads with the highest probability of conversion. 11. Competitor Analysis: Analytics allows businesses to monitor and analyze competitor activities, marketing strategies, and performance. This understanding helps in refining their own marketing strategies and gaining a competitive edge. 12. Real-time Analytics: Marketers use real-time analytics to monitor ongoing campaigns, website traffic, and social media engagement. Immediate insights enable quick adjustments to optimize marketing efforts as needed. LESSON 3: DATA COLLECTION AND SOURCES DATA CRITICAL ROLE IN MARKETING ANALYTICS: Data plays a critical role in marketing decision-making in today's data-driven business environment. It empowers marketers to make informed, strategic, and targeted decisions that can have a significant impact on a company's success. KEY WAYS: Customer Insights Segmentation and Optimization Budget Allocation Targeting Customer Journey Performance Real-time Mapping Analytics Decision-Making Predictive Analysis Competitive Content and Measuring and Analysis Message Reporting SUCCESS STORY OF AMAZON Personalized Recommendations Marketplace Dynamics Supply Chain Optimization Customer Segmentation Review and Feedback Analysis SUCCESS STORY OF NETFLIX Content Recommendations Content Production A/B Testing Churn Prediction Content Marketing: Global Expansion TYPES OF DATA: 1. STRUCTURED DATA Format and Organization: Structured data is highly organized and follows a predefined format. It is typically stored in databases, spreadsheets, or tables with a well-defined schema. Each data element is labeled, and the relationships between data elements are clearly defined. Examples: Examples of structured data include customer information in a CRM database (name, address, phone number), sales transaction records (date, product, price), and financial data (income statements, balance sheets). Analysis Methods: Structured data is well-suited for traditional quantitative analysis methods. It can be easily queried, filtered, and aggregated using SQL and other structured query languages. Statistical analysis and data mining techniques are commonly applied to structured data. Consistency: Structured data is consistent and reliable, making it ideal for business reporting and decision-making. It is often used for structured business processes and applications. Machine Learning: Structured data is also used in machine learning, particularly for tasks like classification, regression, and clustering. However, it may be combined with unstructured data for more comprehensive analysis. 2. UNSTRUCTURED DATA Format and Organization: Unstructured data lacks a predefined structure and does not fit neatly into tables or databases. It includes text, images, audio, video, social media posts, emails, and other content that does not adhere to a specific schema. Examples: Unstructured data examples include social media comments, customer reviews, images, videos, audio recordings, and text documents. These types of data often contain rich and diverse information. Analysis Methods: Analyzing unstructured data is more challenging because it does not have a clear structure. Natural language processing (NLP), sentiment analysis, image recognition, and audio processing techniques are often used to extract insights from unstructured data. Variability: Unstructured data can be highly variable and may require preprocessing, cleaning, and feature extraction to make it suitable for analysis. It often contains noise, ambiguities, and diverse linguistic expressions.  Machine Learning: Unstructured data is a valuable source of information for machine learning applications like text classification, image recognition, and recommendation systems. Machine learning models can be trained to make sense of unstructured data and extract meaningful patterns. Emerging Data Sources: With the growth of the internet, social media, and sensor technologies, unstructured data is becoming increasingly important in analytics. Analyzing unstructured data can provide valuable insights into customer sentiment, brand perception, and emerging trends. DATA COLLECTION METHOD Surveys and questionnaires Interviews Observations Web scraping Social media monitoring PRIMARY DATA COLLECTION METHOD Surveys and Open-Ended Experiments Questionnaires Surveys Psychological Tests Case Studies Interviews and Examinations Biological and Content Analysis Focus Groups Medical Data Observation Diaries and Logs Collection Sensor Data Ethnographic Research SECONDARY DATA COLLECTION METHOD Internal sources (company records, sales data) External sources (government databases, industry reports) Syndicated data (Nielsen, ComScore) Online data repositories (Kaggle, Data.gov) DATA QUALITY Accurate Customer Satisfaction Efficient Reporting Decision-Making Brand Reputation Research and Analysis Cost Reduction Compliance and Time Savings Effective Business Regulatory Data Integration Operations Requirements Resource Allocation Risk Management Data Security Data Mining and Machine Learning DATA QUALITY CHALLENGES Incomplete Data Data Security Data Bias Data Validity Duplicate Data Data Aging Inaccurate Data Data Volume Data Integration Data Governance Data Irrelevance Data Entry Errors Inconsistent Data Data Format DATA CLEANSING Data Profiling Data Integration Data Documentation Data Transformation Outlier Detection and Data Validation Duplicate Data Removal Treatment Data Governance Data Enrichment Data Quality Monitoring Data Correction Handling Missing Data Standardization Data Quality Tools ETL (MEANING): Extract (E): In this stage, data is extracted from different source systems, which can include databases, applications, spreadsheets, flat files, web services, and more. The extraction process gathers the relevant data from these sources, often using techniques like querying, file transfer, or API calls. Extracted data is typically stored temporarily in a staging area. Transform (T): The transformation phase involves cleaning, reshaping, and enriching the data to make it suitable for the target database or data warehouse. Transformations may include data validation, data normalization, data cleansing, aggregations, calculations, and joining data from different sources. This step ensures that the data is consistent and compatible for analysis. Load (L): Once the data has been extracted and transformed, it is loaded into the target system, such as a data warehouse or a database. The data is structure and organized according to a predefined schema or data model, making it accessible for reporting and analytics. DATA COLLECTION TOOLS Survey and Questionnaire Tools: Google Forms: A web-based tool for creating and distributing surveys and questionnaires. SurveyMonkey: An online survey platform with a user-friendly interface. Qualtrics: A comprehensive survey and research tool for creating and analyzing surveys. Data Collection Mobile Apps: SurveyCTO: A mobile data collection platform that works offline and supports various types of data. ODK Collect (Open Data Kit): An open-source Android app for data collection in the field. Fulcrum: A mobile data collection and analysis platform for smartphones and tablets.  Data Entry and Database Software: Microsoft Excel: A widely used spreadsheet software for data entry and simple analysis. Microsoft Access: A database management system for creating and managing databases. Google Sheets: A web-based spreadsheet tool with collaborative features. Social Media Listening Tools: Brandwatch: A social listening and analytics platform for tracking brand mentions and social media conversations. Hootsuite: A social media management tool that includes monitoring and reporting features. Sprout Social: A social media management and analytics platform for businesses.